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Mirasys Appoints Steve Johnson as Computer Vision Manager to Strengthen the Dell Partnership

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Mirasys Appoints Steve Johnson as Computer Vision Manager to Strengthen the Dell Partnership

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Mirasys Appoints Steve Johnson as Computer Vision Manager to Strengthen Dell Partnership and Deliver White-Glove Hardware-to-VMS Solutions

Mirasys, a global leader in open, high-performance video management software (VMS), announced the appointment of Steve Johnson as its Dell Computer Vision Manager. In this role, Steve will serve as a strategic liaison between Mirasys and Dell Technologies, strengthening the combined hardware and software offering delivered to enterprise and public-sector customers through a white-glove, value-driven approach.

Steve’s role is critical as customers increasingly demand clarity & confidence across their entire video stack — His ability to bridge Dell’s infrastructure expertise with Mirasys is game-changing.”

— Carl Raubenheimer – CEO Mirasys USA

Steve brings extensive experience across computer vision, AI-driven analytics, and enterprise infrastructure. His focus will be to ensure customers receive tightly aligned, validated solutions that combine Dell’s infrastructure capabilities with Mirasys’ open, high-performance VMS platform.

“I’ve spent my career at the intersection of video, infrastructure, and analytics, and Mirasys sits exactly where the market is shifting,” said Steve Johnson, Computer Vision Manager at Mirasys. “Customers want intelligent video systems that perform at scale without being locked into rigid ecosystems or unpredictable cost models. By aligning best-in-class hardware with Mirasys’ VMS, we can deliver that with confidence.”

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Strengthening the Dell–Mirasys Value Proposition
In his role, Steve will act as a technical and strategic bridge between Dell and Mirasys, ensuring customers benefit from:
– Optimized and validated hardware configurations
– Seamless integration between infrastructure and VMS
– White-glove guidance from design through deployment
– Long-term performance, reliability, and predictable TCO

“Our goal is to make hardware and software feel like a single, cohesive solution — one that reduces risk, simplifies deployment, and maximizes long-term value,” Steve added. ”

Role and Near-Term Focus
Over the next 90 days, Steve will focus on:
– Aligning Dell infrastructure capabilities with the Mirasys product roadmap
– Strengthening collaboration with channel partners and integrators
– Developing repeatable, validated solution frameworks for priority verticals
– Enhancing customer experience through prescriptive, white-glove engagement

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Over the next 12–24 months, his role will support Mirasys’ expansion into larger, more complex enterprise environments by ensuring hardware and software scale together without compromising reliability or cost control.

Executive Perspective
Carl Raubenheimer, CEO of Mirasys, commented on the appointment:
“Steve’s role is critical as customers increasingly demand clarity and confidence across their entire video stack,” said Raubenheimer. “His ability to bridge Dell’s infrastructure expertise with Mirasys’ open VMS platform enables us to deliver a true white-glove experience — one that prioritizes performance, reliability, and long-term value.”

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Leading Provider AI.cc Simplifies Enterprise AI Adoption by Consolidating 400 Models into a Single High-Performance API

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Indexly Announces Mission to Build the AI Visibility OS for the Modern Internet

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The global artificial intelligence landscape in 2025 and 2026 has reached a fever pitch, characterized by an explosion of generative models and a paradigm shift in how businesses integrate intelligence into their workflows. As the market moves from experimental pilot programs to full-scale enterprise deployment, the complexity of managing multiple AI providers has become a significant bottleneck. Enter AI.cc (AICC), a comprehensive ecosystem that has evolved from a premium domain into a multi-dimensional infrastructure powerhouse. By consolidating over 400 high-performance AI models into a single, unified API, AI.cc is effectively lowering the barrier to entry for the generative AI era.

The Challenges of the Multi-Model Era

In the current technological climate, Large Language Models (LLMs) are iterating at a monthly pace. For developers and enterprise architects, this rapid evolution presents a dual-edged sword. While capabilities are increasing, the overhead of managing diverse API keys, varying documentation standards, and disparate billing systems is becoming unsustainable. Furthermore, the risk of “vendor lock-in” looms large; a company heavily invested in a single provider’s ecosystem may find itself at a competitive disadvantage if a superior model is released by a rival firm.

AI.cc addresses these pain points directly through its “One API” philosophy. By providing a centralized technical hub, AI.cc allows enterprises to remain agile, cost-effective, and technologically resilient in a volatile market.

Key Takeaways: Why AI.cc is the Standard for Enterprise AI
Unmatched Model Diversity: Access to over 400 models covering text, image, video, 3D, voice, and OCR.
Simplified Integration: A single endpoint (https://api.ai.cc) compatible with standard OpenAI formats.
Cost Optimization: Significant operational savings ranging from 20% to 80% compared to direct-from-vendor pricing.
Enterprise-Grade Performance: Unlimited TPM/RPM (Tokens/Requests Per Minute) with ultra-low latency for high-frequency agentic workflows.
Infrastructure Resilience: Elimination of single-provider dependency through a robust, serverless architecture.
Technical Architecture: The Power of “One API”

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The core of the AI.cc value proposition is its high-performance model aggregation platform. Unlike traditional middleware, AI.cc acts as a sophisticated technical abstraction layer. Developers no longer need to learn the nuances of Google Gemini, Anthropic Claude, or Meta’s Llama series individually. Instead, by simply updating the base URL in their existing code to https://api.ai.cc, they gain instant access to a curated library of the world’s most powerful models.

Unified Billing and Compliance

For large organizations, the administrative burden of AI adoption is often overlooked. Procurement departments struggle with managing dozens of individual subscriptions and auditing the usage of various API keys across different departments. AI.cc solves this by centralizing financial management. With a unified billing system and sophisticated permission auditing, enterprises can maintain strict compliance while allowing their developers the freedom to experiment with the latest models from OpenAI, DeepSeek, ByteDance, and more.

Scalability and Performance

AI.cc is built on a high-performance serverless architecture designed for horizontal scaling. In the 2025-2026 landscape, the demand for AI is no longer limited to simple chatbots. We are seeing the rise of high-frequency autonomous agents that require massive throughput. AI.cc supports these requirements with virtually unlimited concurrency, ensuring that enterprise-level “Agentic” workflows never hit a performance ceiling.

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Ready to streamline your AI operations?

The AI industry is currently undergoing a fundamental transformation. In late 2025, the focus shifted from “passive chat interfaces” to “active autonomous agents.” These agents do not just answer questions; they negotiate, exchange information, and close business loops independently. This is known as the Agent-to-Agent (A2A) communication network.

AI.cc is at the forefront of this transition. By integrating next-generation models like GPT-5.2 and Claude 4.5 Opus, which feature enhanced “operational reliability,” AI.cc serves as the essential infrastructure for these agents. The platform acts as a traffic hub where agents can interact across different model backends seamlessly. In this context, AI.cc is no longer just a tool for human assistance—it is the foundational layer for the future’s automated economy.

Data Excellence: The 7.3T AICC Corpus

In the generative AI stack, data is the “new oil,” but quality is the refinement process that determines value. AI.cc is not merely a consumer of models; it is a significant contributor to the global AI research community. Through its AICC (AI-ready Common Crawl) initiative, the platform has constructed a massive multi-lingual corpus consisting of 7.3 trillion (7.3T) tokens.

Scientific Contribution and Model Performance

Utilizing the advanced MinerU-HTML extraction technology, the AICC corpus has set new benchmarks for data quality. Internal testing and external benchmarks demonstrate that models trained on the AICC corpus achieve an average accuracy of 50.82% across 13 key benchmarks—significantly outperforming datasets like RefinedWeb and FineWeb. This emphasizes the value of “Extraction Quality” in building web-scale datasets. By making tools like MainWebBench and MinerU-HTML public, AI.cc has solidified its position as a scientific leader in AI infrastructure, providing a powerful technical endorsement for its commercial web-scraping and data services.

The Future of Compute: AICCTOKEN and DePIN

As the demand for GPU compute continues to outpace supply, the reliance on centralized cloud giants like AWS and Google Cloud presents a strategic risk for AI companies. AI.cc is addressing this through its AICCTOKEN project, which leverages Decentralized Physical Infrastructure Networks (DePIN).

Democratizing High-Performance Computing

The AICCTOKEN ecosystem allows for the democratization of GPU power. By creating a decentralized marketplace for compute, AI.cc offers several critical advantages to its users:

Reduced Costs: Developers can rent compute power on-demand, avoiding the “GPU tax” associated with long-term contracts from centralized providers.
Censorship Resistance: A decentralized network is inherently more resilient and less susceptible to the restrictions of a single entity.
High Availability: By pooling global resources, AI.cc ensures that high-performance training and inference remain accessible even during peak global demand.
Strategic Value: Transforming AI into a Utility

The true genius of the AI.cc model lies in its ability to transform AI from a “proprietary technology” into a “commodity utility.” In the current market, the specific model used is becoming less of a competitive moat than the efficiency, cost, and stability with which that model is deployed. AI.cc provides the “pipes and electricity” for the AI age.

For industry practitioners, the choice is clear. Attempting to build and maintain individual integrations for 400 different models is an exercise in diminishing returns. By leveraging AI.cc, firms can focus on what truly matters: building innovative applications and delivering value to their end-users, while leaving the complex infrastructure management to the experts.

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Announcing peerspot.ai: Your AI Marketing Agent that Creates High-Quality Content Using Customer Proof

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Announcing peerspot.ai: Your AI Marketing Agent that Creates High-Quality Content Using Customer Proof

Peerspot Reviews & Ratings 2026

PeerSpot’s new AI agent instantly transforms voice-of-customer feedback into blogs, one-pagers, social assets, battlecards, and more.

PeerSpot announced the launch of peerspot.ai, a generative AI agent that makes it effortless for B2B marketers to create high-performing content using their most powerful asset: the voice of their customer.

For years, marketing teams have treated reviews primarily as “badges” or static proof points. To extract more value, they had to manually sift through hundreds of quotes to build a single case study or slide deck. peerspot.ai changes this dynamic entirely, giving marketers an “always-on” engine that turns that raw data into finished marketing assets in seconds.

“Marketers are under immense pressure to produce more high-quality content with fewer resources,” said Russell Rothstein, Founder and CEO at PeerSpot. “We built peerspot.ai to generate marketing content with ease using your customers’ actual experiences. We are turning reviews from a passive trophy into active fuel for your daily marketing engine.

High-Quality Content, Zero “AI Fluff” Unlike general-purpose AI tools that often generate generic or hallucinated copy, peerspot.ai is grounded exclusively in PeerSpot’s verified review data. This ensures that every blog post, report, and social caption is not only generated instantly but is also factually accurate and deeply authentic.

Marketing Technology News: MarTech Interview with Miguel Lopes, CPO @ TrafficGuard

The platform acts as an AI strategist that helps marketers:

  • Write detailed blog posts based on specific user use cases and industries.
  • Create social media assets optimized for LinkedIn, turning user sentiment into viral cards.
  • Build PDFs and reports that summarize buyer feedback for sales, SDR, and product teams.

Marketing Technology News: Disrupt or Be Disrupted: The AI Wake-Up Call for B2B Marketers

“UiPath values the depth and quality of the long-form reviews we receive on PeerSpot because they capture authentic customer insight. peerspot.ai unlocks even more value from that foundation by instantly transforming verified customer voice into targeted quotes, social assets, and campaign content. Because the content is grounded in structured review data, it’s also LLM-ready, helping us amplify authentic customer insight across sales, marketing, and AI-driven buyer discovery.” – Dana Ionescu, Senior Customer Marketing Manager, UiPath

Write in to psen@itechseries.com to learn more about our exclusive editorial packages and programs.

Why MarTech audits are becoming a critical step before scaling digital advertising spend?

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Why MarTech audits are becoming a critical step before scaling digital advertising spend?

Many businesses increase advertising budgets, expecting immediate growth in conversions and revenue. In theory, allocating more funds to digital advertising should translate into greater visibility, higher engagement, and stronger sales performance. However, in practice, simply increasing ad spend does not guarantee improved outcomes.

In many cases, businesses discover that scaling their advertising budgets amplifies existing inefficiencies rather than delivering the growth they anticipated. Without a clear understanding of how campaigns are currently performing, additional investment can reinforce the same problems that were limiting results in the first place.

Digital advertising today operates within a highly complex ecosystem. Marketing teams often manage multiple channels simultaneously, including search advertising, social media campaigns, display networks, and programmatic platforms. Each channel involves its own targeting methods, bidding strategies, analytics dashboards, and creative formats.

At the same time, customer journeys have become more fragmented, with users interacting with brands across devices, platforms, and touchpoints before making a purchasing decision. As a result, determining what is truly driving performance—and what may be hindering it—has become increasingly difficult for marketing teams.

Scaling campaigns in such an environment without proper evaluation can lead to significant inefficiencies. For example, inaccurate conversion tracking may cause businesses to misinterpret which campaigns are actually generating value. Poorly structured advertising accounts can result in overlapping audiences, redundant keywords, or misaligned bidding strategies.

In some cases, ineffective ad creatives or unclear messaging may limit engagement even when budgets increase. When these issues remain unresolved, increasing spend may accelerate the rate at which marketing budgets are wasted rather than improving overall return on investment.

Another challenge is the growing complexity of marketing technology stacks. Modern marketing operations often rely on a wide range of tools and platforms, including analytics systems, customer data platforms, campaign management software, and advertising automation tools. While these technologies offer powerful capabilities, they can also create operational complexity if they are not properly configured or integrated. Data discrepancies between platforms, incomplete tracking setups, and disconnected reporting systems can make it difficult for marketers to gain a clear view of campaign performance.

As a result, organizations are increasingly recognizing the importance of conducting structured evaluations of their marketing infrastructure before expanding advertising investments. Rather than immediately increasing budgets, many businesses are choosing to analyze their existing marketing systems, campaign structures, and data accuracy to identify potential issues that could limit performance. This proactive approach allows marketing teams to uncover hidden inefficiencies and develop a clearer understanding of how their advertising ecosystem is functioning.

This is where MarTech audits are becoming an essential part of modern marketing strategy. A MarTech audit provides a comprehensive review of the technologies, processes, and data systems that support digital advertising activities. Instead of focusing only on campaign performance metrics, these audits examine the broader marketing infrastructure to determine whether tools are configured correctly, whether tracking systems are accurate, and whether campaigns are structured in a way that supports optimal results.

By examining marketing technologies, campaign setups, and analytics frameworks, a MarTech audit helps organizations identify technical gaps and strategic weaknesses that might otherwise go unnoticed. It allows businesses to verify that conversion tracking is functioning correctly, that campaign structures align with performance goals, and that advertising platforms are being used effectively. This level of analysis ensures that marketing teams are not making decisions based on incomplete or misleading data.

Ultimately, conducting a MarTech audit before scaling advertising spend enables businesses to move forward with greater confidence. Instead of relying on assumptions about what may drive growth, organizations can base their investment decisions on accurate insights and clearly identified opportunities for improvement. As digital advertising continues to evolve and marketing ecosystems grow more sophisticated, MarTech audits are increasingly becoming a critical step in ensuring that advertising investments are both efficient and sustainable

The Risks of Scaling Advertising Without Analysis

It sounds easy to develop your digital advertising: just spend more and expect greater results. But if you don’t know what is really making a campaign work, raising the budget can soon lead to wasted money. This is why many businesses now use MarTech insights and audits to make sure their marketing platforms and campaigns are ready to grow.

  • The Pressure to Scale Advertising Quickly

In the fast-paced world of digital marketing, companies often think that spending more on ads would automatically bring in more visitors, more sales, and more money. This assumption is occasionally correct, but increasing campaigns without first checking their performance might cause great difficulties. Companies are increasingly using MarTech solutions to run campaigns, track results, and improve performance as digital ecosystems get more complicated. If you don’t evaluate these systems properly, spending more on advertising may make problems worse rather than fixing them.

Companies often increase their advertising when they notice early success or want to stay ahead of the competition in crowded markets. But if you don’t think carefully about this method, it can easily waste resources. Use MarTech platforms to determine whether campaigns are ready to grow, providing the data and insights you need. Marketers may make investment decisions based on insufficient information if these tools are not checked or improved.

There are several platforms, analytics systems, and automation tools that make up the digital advertising world today. Search advertisements, display campaigns, social media promotions, and programmatic advertising all provide the performance data you need to interpret correctly. A strong MarTech infrastructure makes sure that this data is collected, analyzed, and turned into useful information. It is dangerous to try to grow advertising without this base.

Campaign structures that don’t work well can hurt performance.

One of the biggest dangers of scaling advertising without proper research is running campaigns that aren’t well set up. Even with higher costs, campaigns that aren’t well-organized, lack clear segmentation, or use poor bidding tactics often struggle to perform well.

Marketers can identify structural problems in advertising accounts using a well-configured MarTech environment. Businesses can identify problems that may be limiting performance by reviewing campaign hierarchies, keyword groups, and audience targeting models. Without this research, spending more on ads can just send more people through a system that isn’t working well.

For instance, campaigns that use keywords or audience segments that aren’t related to each other could show ads that aren’t relevant to potential customers. A MarTech audit can identify these problems and suggest changes to make targeting more accurate and campaigns more effective before they grow.

  • Incorrect Conversion Tracking Leads to Misleading Data

Accurate information is the most important part of effective digital marketing. Businesses can use conversion monitoring to identify which efforts drive leads, purchases, or other useful actions. But if tracking systems aren’t set up well, marketing teams could make decisions based on false information.

A well-implemented MarTech stack ensures that conversion events are tracked correctly across all advertising platforms and analytics tools. Without this validation, companies can spend more on efforts that are working but don’t actually get them very far.

For example, tracking inaccuracies may cause conversions to be counted twice or not linked to the correct marketing channels. A full assessment of MarTech helps identify these problems and ensures that reporting tools provide accurate performance information.

  • Poor Targeting and Audience Segmentation

To be effective, advertising needs to reach the proper people. Marketers may now target customers based on their demographics, interests, behaviors, and browsing habits thanks to modern digital platforms. But if your targeting tactics aren’t clear, your ads might not get to the right people.

A structured MarTech analysis examines how audience segmentation is configured across different marketing platforms. Businesses can improve their targeting by analyzing customer data and behavioral insights to identify high-value audiences.

If you don’t do this analysis, raising your advertising budget means that your ads reach people who are unlikely to buy. A well-optimized MarTech environment ensures that advertising dollars go to the people most likely to buy and engage.

  • Budget Allocation Across Underperforming Channels

Another big danger of scaling advertising without study is how funding is distributed. Many companies put money into more than one marketing channel at the same time. These channels include search engines, social media sites, display networks, and marketplaces.

Marketing teams may struggle to determine which channels generate the most revenue without help from MarTech analytics solutions. Because of this, budgets may stay with platforms that aren’t performing well, while channels that are profitable don’t receive enough funding.

A thorough analysis of MarTech lets businesses assess how well their channels are performing by analyzing data from all of them. Businesses can better plan their budgets before expanding their campaigns by reviewing KPIs such as cost per acquisition, return on ad spend, and customer lifetime value.

  • Scaling Inefficiencies Instead of Results

Increasing ad spending can worsen existing problems if underlying issues are not fixed. Campaign structures are still not perfect, tracking technologies still produce inaccurate data, and targeting methods still reach the wrong people.

In some cases, increasing funds doesn’t help performance. Instead, it raises costs without giving you much, if any, return on your investment. Because of this, many marketing directors now see MarTech audits as an important step before spending more on advertising.

Organizations can identify and fix performance issues before scaling campaigns by examining the entire marketing technology ecosystem. This method ensures that extra spending yields real results rather than wasting money.

What a MarTech Audit Is and Why You Should Do It

An audit of MarTech examines the technologies, platforms, and systems that support digital marketing. As companies use more and more new technologies for advertising management, analytics, automation, and consumer engagement, their marketing technology stacks are becoming increasingly complex.

If you don’t check these systems regularly, they can break down or stop working as well. A MarTech audit is a disciplined way to look at the whole marketing technology ecosystem and make sure that each part is helping the campaign do its job well.

  • Evaluating Marketing Platforms and Tools

One of the most important parts of a MarTech audit is examining the platforms used to run marketing campaigns. These could be systems for managing ads, tools for analyzing data, platforms for storing customer data, email marketing software, and solutions for automating campaigns.

Companies can determine whether their MarTech infrastructure supports their current marketing goals by examining how these technologies are configured and connected. Sometimes, audits reveal tools that aren’t needed, outdated platforms, or underused features, all of which slow operations.

Identifying these problems helps companies simplify their technology stacks and ensure that each platform adds real value to marketing operations.

  • Assessing Data Accuracy and Tracking Systems

Data integrity is another important part of a MarTech audit. Marketing strategies depend on precise performance data to inform decisions. If analytics systems or monitoring technologies are set up incorrectly, the information they provide may not be accurate.

A MarTech review ensures that conversion tracking, attribution models, and analytics connectors function properly across all marketing channels. Making sure that data is correct helps marketing teams decide how to spend their money, who to target, and how to improve their campaigns.

  • Identifying Optimization Opportunities

A MarTech audit not only finds problems, but it also shows ways to make things better. Organizations can find ways to improve performance by looking at the structures of their campaigns, the workflows for automation, and the ways they group customers.

For instance, companies might find ways to automate operations that they do again and over, make their personalization tactics better, or add more data sources to their MarTech systems. These changes can make marketing much more efficient and effective.

  • Preparing Marketing Systems for Scalable Growth

The main purpose of a MarTech audit is to get marketing operations ready for long-term growth. Instead of making assumptions about how their technological infrastructure affects performance, businesses can now see how it really does.

With this information, companies can improve their MarTech environment, fix problems, and ensure their marketing systems are ready to handle larger ad budgets. Companies that take this proactive approach are much more likely to get steady returns as they grow their digital marketing activities.

Key Areas Examined in MarTech Advertising Audits

Digital advertising campaigns work in complicated systems of platforms, analytics tools, and automation technology. As businesses rely more and more on marketing technology to run and improve their campaigns, MarTech has become a key part of digital advertising success. A MarTech advertising audit looks at the tools, data systems, and campaign techniques that help ads work in a systematic way.

A MarTech audit looks at the deeper technology and strategic roots of digital marketing operations instead of just the surface-level campaign KPIs. Companies may find problems, make sure their data is correct, and make sure their advertising spending is in line with measurable business results by looking at these systems.

A MarTech advertising audit usually looks at a few important areas. These aspects include tracking conversions, setting up campaigns, choosing the right audience, measuring creative performance, and managing the budget. These parts work together to give a full picture of how marketing technology and advertising tactics work together to generate results.

Conversion Tracking and Data Accuracy

Tracking conversions and making sure the data is correct are two very important parts of good digital advertising. They make sure that marketing teams can precisely track what customers do as a result of campaigns, such as buying something, signing up, or downloading anything. Businesses can make smart choices when they have reliable tracking solutions that give them consistent and accurate performance data across all of their marketing platforms.

  • Verifying That Tracking Systems Measure Conversions Properly

One of the most important parts of digital advertising is tracking conversions. It lets companies track client behaviors that come from advertising efforts, such as purchases, sign-ups, downloads, or form submissions. Checking that these tracking systems are set up correctly is a big part of any MarTech advertising audit.

Marketing companies use conversion data a lot to figure out how well a campaign is doing and how to best spend their money. Organizations may get wrong performance reports if tracking systems are not set up appropriately. This might lead to bad decisions. A MarTech audit checks tracking codes, tag management systems, analytics connectors, and attribution models to make sure that conversions are being recorded correctly.

Another crucial part of this process is making sure that the events being tracked are in line with the goals of the organization. For instance, if a firm values completed sales more than email sign-ups, the MarTech infrastructure should put more emphasis on tracking and evaluating those higher-value conversions.

  • Ensuring Data Consistency Across Marketing Platforms

Search engines, social media networks, and advertising exchanges are all part of modern digital marketing. You need to combine the performance data from all of these platforms into one analytics environment. A MarTech audit makes sure that the data you get from various sources is accurate and consistent.

There may be differences between the data supplied by the platform and the data from internal analytics tools in some circumstances. These inconsistencies could be caused by mistakes in tracking, wrong attribution models, or systems that don’t work together completely. Businesses may find these problems by looking at the whole MarTech ecosystem. This makes sure that their data infrastructure gives them a clear and accurate picture of how well their ads are doing.

It is important to have accurate data to scale campaigns properly. Marketers might put more money into initiatives that don’t work if they don’t get accurate insights.

Campaign Structure and Targeting

The structure and targeting of a campaign decide how well ads reach the correct people. A well-planned campaign structure helps advertisers stay within their budgets, try out different audience segments, and improve performance across all platforms. By going over these things, you can make sure that your advertising efforts are in line with your strategic goals and that you aren’t wasting money on targets that are too similar or not well defined.

  • Evaluating Campaign Architecture and Organization

Checking the structure of a campaign is another important part of a MarTech advertising audit. Campaign architecture is the way that advertising accounts are set up, including the structure of campaigns, ad groups, and targeting settings.

Marketers may better manage their costs, try out different target segments, and improve performance at a very small level with well-structured campaigns. A MarTech audit checks to see if the way campaigns are set up makes it easy to target the right people and keep track of spending.

When campaigns aren’t well-organized, they can reach the same people, run the same commercials, and waste money. For instance, if several campaigns try to reach the same group of people with similar messages, they can bid against each other in advertising auctions, which would raise expenses for no reason. A MarTech audit of campaign architecture can assist in finding and fix these problems.

  • Identifying Redundant Campaigns and Ad Groups

When new techniques are added to huge advertising accounts, campaigns tend to build up over time. If you don’t review these efforts from time to time, they can become useless or not fit with your current marketing goals.

A MarTech audit checks to see if current campaigns and ad groups are still useful and if they help the company reach its overall performance goals. Extra campaigns can waste ad spending and make it harder to figure out how well they are working. Organizations may make their advertising accounts more efficient and clear by finding and getting rid of these problems.

Marketing Technology News: MarTech Interview With Fredrik Skantze, CEO and Co-founder of Funnel

Keyword and Audience Strategy

Keywords and audience strategy are quite important for figuring out how relevant ads are to people who might want to buy something. Businesses may make sure their ads reach the people who are most likely to click on them by choosing the proper keywords and clearly defining their target demographic. A well-thought-out plan makes a campaign work better and cuts down on money wasted on ads.

  • Reviewing Search Queries and Targeting Logic

Keyword targeting and dividing your audience into groups are very important for digital advertising to work. These tactics decide who sees ads and how relevant those ads are to their needs and interests. Marketers meticulously look over keyword lists, search queries, and audience targeting settings during a MarTech advertising audit.

This research helps figure out if campaigns are going after the right search phrases and groupings of people. Sometimes, businesses find that their advertisements are showing up for searches that aren’t relevant, which means they’re wasting money on ads. A MarTech audit looks at these tendencies and suggests changes that will make targeting more accurate.

Targeting the right audience is just as crucial, especially for display and social media ads. Marketers may now use modern MarTech systems to construct complex audience segmentation based on things like demographics, interests, behavior, and past encounters with a brand. By looking at these audience methods, you can make sure that your campaigns target those who are most likely to interact with the ad.

  • Identifying Opportunities to Reduce Wasted Spend

Another reason to look over your keyword and audience tactics is to cut down on wasted ad spending. When targeting is not done well, you may acquire a lot of impressions or clicks that don’t lead to real business results.

A MarTech audit can help businesses find keywords that aren’t working well, audience segments that aren’t appropriate, or targeting settings that are too broad. Changing these things makes campaigns more effective by focusing ad spending on high-value viewers and search queries that are relevant.

Ad Creative and Messaging Performance

The effectiveness of ads in getting people to pay attention and take action depends on how well the ad’s creativity and language work. Headlines, images, and calls to action are all very important for getting people to interact with your site and make a purchase. Marketers may figure out which messages work best for their target demographic and get better campaign outcomes by looking at these things.

  • Analyzing Engagement Metrics

Targeting and bidding tactics are not the only things that impact how well an ad campaign works. Headlines, pictures, calls to action, and the overall message are all important creative parts that help get people’s attention and keep them interested.

An advertising audit for MarTech looks at creative success by looking at engagement metrics including click-through rates, conversion rates, and levels of involvement. These numbers show how people react to different types of ads and creative messages.

Marketers may figure out which creative assets work best with their target consumers by looking at this data in the context of the larger MarTech framework. This information helps businesses enhance their messaging strategy and make their future campaigns more effective.

  • Determining Which Messaging Delivers the Best Results

The message of an ad must fit with both the needs of the audience and the brand’s stance. A MarTech audit checks to see if the present messaging methods are getting across the value of a product or service.

Some messages, for example, may focus on the attributes of a product, while others may focus on the benefits to the customer or special offers. Marketers can find out which message techniques work best by looking at campaign data in the MarTech ecosystem and then using those methods in other campaigns.

In competitive markets, it’s important to keep testing and improving creative assets to keep advertising working well.

Budget Allocation and Bidding Strategies

Budgeting and bidding techniques are very important for getting the most out of advertising and getting the most money back. By carefully spreading funds among campaigns, you can make sure that channels that do well have the resources they need to grow. Checking bidding tactics also helps make sure that the money spent on ads is in line with the goals of the campaign and produces outcomes that can be measured.

  • Assessing Budget Distribution Across Campaigns

To get the most out of your advertising money, you need to plan how to spend it. A MarTech audit looks at how budgets are spread out among different campaigns, channels, and groups of people.

A lot of the time, businesses find out that a big part of their advertising budget goes to initiatives that don’t work very well. Marketers can find chances to move money to campaigns that do better by looking at performance data with MarTech analytics tools. This approach makes sure that money spent on advertising goes toward the best techniques and brings in verifiable company value.

  • Evaluating Bidding Strategies and Performance Goals

Modern advertising platforms have a number of bidding tactics that are meant to improve the performance of campaigns. These methods might be about getting the most clicks, conversions, or return on ad expenditure. A MarTech advertising audit checks to see if the bidding tactics used fit with the company’s overall marketing goals.

For instance, a campaign that wants to make sales right away could need a different bidding strategy than one that wants to get people to know about the brand. When you look at these techniques through the MarTech lens, you can be sure that your campaigns are set up to reach their goals.

The audit process may also reveal chances to use more advanced bidding tactics that are backed by machine learning and automation capabilities. Adding these features to the MarTech ecosystem can make campaigns work much better.

Strengthening Advertising Performance Through MarTech Audits

The goal of looking at these important areas during a MarTech advertising audit is to build a better base for digital marketing success. Businesses may learn a lot about how their advertising systems work by looking at conversion tracking, campaign structures, targeting techniques, creative performance, and budget management.

A good MarTech audit not only finds problems that are happening right now, but it also shows how things could be better in the future. Organizations may spend more on advertising with more confidence and develop their businesses in digital markets that are becoming more competitive if they have accurate data, well-planned campaigns, and effective targeting methods.

How MarTech Audits Help Advertising Return on Investment?

Digital ads are becoming one of the most significant ways for modern businesses to flourish. To obtain more customers and make more money, businesses spend a lot of money on online ads on search engines, social media sites, and programmatic networks. But spending more on ads doesn’t always mean better results. Businesses often see their returns go down because they haven’t fixed the core problems with their campaigns. This is where MarTech audits come in. They are very important for getting the most out of your advertising and getting the most out of your money.

A MarTech audit is a planned look of the technology, campaign structures, tracking systems, and data pipelines that make digital advertising possible. The audit doesn’t just look at the obvious KPIs of a campaign; it also looks at the technological and strategic infrastructure that supports marketing operations. Businesses may make their campaigns better before spending more money on advertising by finding hidden problems and performance obstacles.

One of the main reasons MarTech audits help advertising ROI is that they find problems that are hard to find when running campaigns daily. Marketing teams could spend a lot of time tweaking keywords, changing bids, or trying out new creative assets. However, they might not see bigger structural or technological problems that are hurting the success of their campaigns. A full MarTech review gives you a better idea of how marketing systems work together and how they affect results.

A MarTech audit can also help make sure that marketing technology is in line with corporate goals, which is another major benefit. A digital marketing ecosystem is made up of many different technologies, including as analytics platforms, customer data systems, advertising dashboards, and automation software. When these technologies are not connected or are set up incorrectly, the data they create may not be complete or consistent. A good MarTech audit makes sure that these platforms operate well together and give you reliable information to help you make decisions.

Identifying Inefficiencies That Impact Campaign Performance

A lot of advertising campaigns don’t do well, but it’s not because of bad marketing concepts; it’s because of problems with the way the marketing technology stack is set up. For instance, campaigns could use old targeting settings, broken data systems, or wrong tracking codes. These problems can make ads much less successful without being obvious right away.

Companies may find these problems and fix them before they spend more money on ads by doing a thorough MarTech review. When the infrastructure that supports campaigns works better, the same amount of money spent on advertising can get better results. This is how MarTech audits help advertising expand in a way that lasts.

  • Improving Targeting Accuracy

One of the most important things that affects how well an ad works is how well it targets its audience. Marketers may reach very particular groups of people on digital platforms based on things like their age, gender, where they live, what they buy, and what they like to do online. But these targeting features only work when they are backed up by correct data and well-organized groups of people.

A MarTech audit helps you figure out how marketing platforms acquire, handle, and use data about your audience. Businesses can find out if their targeting tactics are in line with how customers actually behave by looking at customer data platforms, analytics integrations, and segmentation models. When the MarTech ecosystem gives marketers solid information about their audiences, they may show ads to people who are most likely to click on them and buy something.

Better targeting not only gets more people to click on ads, but it also cuts down on wasted ad spending. Instead of sending ads to big, unqualified groups, they are sent to those who are likely to be interested in them. This leads to increased conversion rates and better use of marketing dollars.

  • Optimizing Campaign Structures

The way a campaign is set up has a big impact on how well advertising budgets are used. Campaigns that aren’t well-organized can have the same audience in more than one ad group or use keywords in a way that doesn’t work. These problems might make it hard to judge how well a campaign is doing and make it better.

A MarTech audit looks at the structure of advertising accounts to see if campaigns are set up to work best. As part of this process, you go over campaign hierarchy, audience segmentation, and targeting parameters. Businesses can acquire better insights into performance indicators and make sure that budgets are used properly across multiple advertising activities by reorganizing campaigns under the MarTech framework.

Optimized campaign structures also help marketing teams try out new ideas, look at performance trends, and grow successful campaigns without confusing advertising accounts.

  • Reducing Wasted Advertising Spend

One of the biggest problems in digital marketing is wasting money on ads. This happens when advertising are sent to the wrong people, triggered by unrelated terms, or shown in places that don’t get people to interact with them.

A thorough MarTech audit looks at campaign data from several platforms to find the sources of wasted spending. Marketers can find patterns that show when money is being wasted by looking at search queries, audience groups, and information on where ads are placed. Once these problems are fixed in the MarTech infrastructure, companies may use their advertising expenditures to pay for techniques that bring in more money.

Cutting down on wasted spending has a direct effect on the return on investment (ROI) of advertising. Organizations can generate greater outcomes by optimizing their existing campaigns through better MarTech management instead of raising costs to make up for inefficiencies.

  • Raising the number of conversions

A well-done MarTech audit should also help with conversion rate optimization. Conversions are the main goal of most advertising initiatives, whether they are trying to get people to buy something, get leads, or sign up for something. But low conversion rates are sometimes a symptom of bigger problems in marketing strategies.

For instance, bad tracking systems could report conversions incorrectly, and analytics solutions that aren’t well-connected might not be able to tell where people drop off in the customer journey. An audit of MarTech looks at these technologies to make sure that conversion tracking and attribution models show how real users behave.

Businesses may make their marketing more effective by making sure that the data is correct and targeting the right people with their campaigns. This will help users do what they want to do. These changes over time lead to increased conversion rates and better returns on money spent on advertising.

  • Creating a Strong Foundation for Scalable Growth

In the end, a MarTech audit leads to changes that make advertising campaigns more effective and scalable. Businesses may safely raise their advertising budgets when campaigns are based on precise data, well-designed frameworks, and successful targeting methods. They know that spending more money will lead to real results.

This proactive approach stops businesses from running inefficient ads that don’t work and lets them focus on long-term growth with the help of a strong MarTech ecosystem.

The Strategic Benefit of Pre-Scaling Audits

Many businesses are realizing how important it is to do MarTech audits before spending more on ads as digital marketing gets more complicated. Instead of blindly scaling campaigns, companies that use this method get a strategic edge by knowing exactly how their marketing systems work.

A MarTech audit before scaling gives you a clear plan on how to make things better. Companies can find both technical and strategic changes that need to be made before they spend more on advertising by looking at the whole marketing technology environment.

  • Getting a better look at how well marketing is doing

One of the best things about a MarTech audit is that it gives you a better idea of how well your marketing is working. Digital marketing creates a lot of data from many different platforms, which makes it hard for marketing teams to effectively understand results without the right tools and interfaces.

A well-organized MarTech infrastructure brings all of this data together into unified analytics dashboards that make it easy to see how well a campaign is doing. Businesses can find holes in their data reporting and make sure that their analytics systems give them accurate information by going through the audit process.

Better visibility lets marketing professionals see which initiatives make money, which channels give the highest return on investment, and which techniques need to be improved.

  • Enabling Data-Driven Decision Making

Making decisions based on data is a key part of modern digital marketing. This method only works, though, if the data it is based on is correct and complete. A MarTech audit makes sure that marketing teams have access to good data that helps them plan their strategies and improve their campaigns.

With accurate data insights, companies can make smart choices about how to spend their money, who to target, and how to grow their campaigns. Marketers may use MarTech analytics to help them make decisions and get the most out of their ads instead of relying on guesses or inadequate results.

  • Building Confidence Before Scaling Campaigns

It costs a lot of money to scale up advertising efforts; marketing directors need to be sure that their campaigns are ready for growth in terms of both strategy and technology. A MarTech audit gives you this confidence by checking that your marketing systems are working correctly and that your campaigns are set up to get the best results.

This preparedness lowers the chance of performance problems that come out of nowhere when budgets go up. Companies can be more sure that their advertising efforts will grow if they make sure that their MarTech infrastructure can handle more campaigns.

  • Scaling Campaigns While Minimizing Risks

One of the best things about doing a MarTech assessment before scaling campaigns is that it can help lower risks. Ad platforms work in competitive bidding situations, and campaigns that aren’t effective can quickly use up big funds without getting any results.

A thorough examination of MarTech finds any issues early on, so businesses can deal with them before spending more on advertising. This proactive strategy makes sure that initiatives are developed on a solid technological base and backed up by precise performance statistics.

When campaigns are finally scaled, the MarTech audit process helps marketing teams get better outcomes while keeping a better track of how well their ads are doing.

  • Driving Sustainable Advertising Growth

Businesses that put MarTech audits first before increasing their advertising budgets are setting themselves up for more long-term growth. Instead of reacting to short-term changes in performance, they make marketing plans based on solid data, well-organized campaigns, and technology platforms that work well together.

This strategy makes sure that advertising investments always pay off and help the business as a whole succeed. As digital marketing changes, MarTech audits will play an even bigger role in helping businesses get the most out of their advertising.

Conclusion

As digital advertising changes, companies are putting more money into marketing technology, data platforms, and advertising channels to stay ahead of the competition. But raising the budgets for ads without first knowing how well the campaigns and marketing systems are really working might squander money and make things less efficient. This is why companies that want to get the most out of their digital advertising spending need to do MarTech audits.

A MarTech audit gives you the information you need to understand how marketing technologies, analytics tools, and advertising platforms work together to help campaigns do well. Instead of only looking at surface-level data like clicks, impressions, or traffic volume, organizations learn more about how their whole marketing system works. This broad view helps marketing teams find technological problems, incorrect data, and inefficient structures that can be getting in the way of advertising success.

One of the best things about a MarTech audit is that it can find improvement opportunities that you didn’t know about. As companies get more tools, add more advertising channels, and try out alternative campaign methods, many digital marketing systems change over time. These developments can make marketing better, but they can also make the MarTech landscape more complicated. Platforms may not work well together, tracking systems may give different results, and campaign structures may become less effective. By doing a thorough MarTech audit, companies may find and fix these problems before they spend more money on advertising.

Another important result of a MarTech audit is making the marketing infrastructure stronger. For digital ads to work, they need dependable data, well-planned campaigns, accurate targeting methods, and useful automation tools. When these parts work together in a well-optimized MarTech ecosystem, marketing teams can run campaigns with more accuracy and confidence. This better infrastructure makes it easier to ensure that advertising money is spent on tactics that lead to measurable business results.

Another great thing about MarTech audits is that they help you make decisions based on data. Companies can now get a lot of performance data in the world of digital marketing. But this information is only useful if it is correct, combined, and easy to understand. A full MarTech assessment makes sure that analytics platforms, conversion tracking systems, and attribution models are all in line with the goals of the organization. When marketing directors have solid information, they can make smart choices about how to spend their money, who to target, and how to grow their campaigns.

Before expanding advertising initiatives, doing a MarTech audit also helps lower risk. Businesses often make existing problems worse when they spend more on advertising without fixing them. Campaigns that weren’t doing well before may use up more money without getting improved outcomes. First, companies make sure their MarTech infrastructure is ready to support expansion by reviewing and improving existing marketing processes.

In the end, businesses that put a lot of effort into MarTech audits have a strategic edge in the digital marketplace. By finding problems early and making specific adjustments, they build a stronger base for marketing operations that can grow. This proactive approach lets firms get the most out of their advertising money while keeping a better track of how well their campaigns are doing.

Companies that check their marketing systems before putting additional money into digital ads are much more likely to see long-term growth. When marketing teams have a well-optimized MarTech ecosystem, they can confidently grow their campaigns, get more people to interact with them, and get better long-term returns on their digital marketing investments.

Reply announces a partnership with Mistral AI to develop sovereign and enterprise-grade artificial intelligence solutions

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Reply announces a partnership with Mistral AI to develop sovereign and enterprise-grade artificial intelligence solutions

Reply announced a new partnership agreement with AI leader Mistral AI aimed at accelerating the adoption of local, customizable, secure and enterprise-grade generative AI solutions at scale.

At the core of the collaboration is a shared vision of frontier AI, designed to enable organizations to adopt AI solutions while ensuring data control, protection of sensitive information, compliance with regulatory requirements and deployment on European infrastructures.

By combining Mistral AI’s high-performance AI models with Reply’s expertise in designing and customizing Large Language Models using proprietary and domain-specific data, organizations in highly regulated sectors – such as public administration, defense, financial services, healthcare, telecommunications, and energy & utilities – can deploy tailored AI solutions that integrate seamlessly with existing systems. These solutions support the transformation of operational processes, enhance decision-making and deliver measurable business value, while ensuring the highest standards of security, data sovereignty and compliance.

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A second core pillar of the Reply–Mistral AI collaboration is advanced AI model customization. Reply will become a Mistral Forge global launch partner, enabling its teams to design and train Large Language Models on proprietary and specialized datasets tailored to complex, data-intensive domains and ready for operational use.

Under this agreement, both companies are collaborating with the Austrian Academy of Sciences, focusing on the creation of a customized Large Language Model for the Greek language, spanning ancient, medieval, and modern texts. The model is designed to support researchers working with ancient Greek sources by providing advanced text search and text completion capabilities. It is trained on a highly specialized corpus that includes published ancient Greek literature, digitized inscriptions and papyri from multiple collections and selected modern Greek texts, curated from publicly available and scholarly sources. Within this initiative, Reply and Mistral AI collaborate on the training and evaluation of the model to ensure accuracy, reliability and practical relevance, demonstrating how sovereign AI infrastructures and advanced model customization capabilities can be effectively combined even in highly specialized and data-intensive domains.

“The integration of the Mistral AI ecosystem with Reply’s experience in developing AI solutions tailored to specific business processes will enable organizations to deploy custom, secure and governable models, designed to ensure data sovereignty and data protection, that integrate seamlessly into existing operational workflows and scale reliably within enterprise architectures.” said Filippo Rizzante, CTO of Reply.

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Mistral AI’s Chief of Revenue Marjorie Janievicz said: “We are proud to partner with Reply. Together, we will help organizations deploy AI that meets their needs for performance, control, and customization.”

Through this collaboration, Reply and Mistral AI provide a trusted and secure environment on European infrastructures, accelerating the adoption of advanced AI solutions while enabling organizations with stringent regulatory, privacy and data protection requirements to fully leverage generative AI.

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AutoRaptor joins NIADA as an official Member Benefit Partner

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AutoRaptor joins NIADA as an official Member Benefit Partner

Partnership gives NIADA members exclusive access to AutoRaptor’s AI-powered CRM platform with 35% discount

AutoRaptor, the AI-first automotive CRM platform built exclusively for independent dealerships , is joining NIADA’s member benefit program, offering members up to 35% off their monthly subscription, making enterprise-grade CRM and AI sales tools more accessible to independent dealers.

Independent dealers face unique challenges that off-the-shelf CRM tools weren’t designed to solve: limited staff, high-velocity used car inventory, and buyers who expect a fast, personalized experience. AutoRaptor was built from the ground up to address exactly these needs, combining lead management, automated follow-up, and AI-powered capabilities, like AutoRaptor’s AI Sales Assistant, into a single platform that helps smaller dealerships compete and win.

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“We’ve always believed that independent dealers deserve the same quality technology as the big franchise groups without the enterprise price tag. Partnering with NIADA is a natural extension of that mission,” said Jami Ribeiro, AutoRaptor Chief of Staff.
“This deal gives thousands of independent dealers a meaningful, cost-effective path to modernizing how they manage leads and close deals.”

NIADA represents more than 13,000 independent automobile dealers nationwide. The organization’s member benefit program connects dealers with vetted vendors offering exclusive pricing across technology, operations and business services.

“Along with the state associations, we are committed to creating more value for members and enhancing our member benefit program,” said NIADA CEO Jeff Martin. “This new partnership continues that commitment to our members.”

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Through this partnership, NIADA members gain exclusive access to AutoRaptor’s full platform, including:

  • AI Sales Assistant (AISA) — automated lead follow-up across voice, SMS, email, and web chat agents.
  • Integrated Desking & Payment Penciling — build and present deal structures directly within the CRM with e-signature
  • Universal communications experience — phone, email, web, and third-party listing integrations in one place
  • Real-time pipeline visibility and automation— know where every deal stands and automate each next step
  • Seamless DMS integrations — including Dealertrack and other leading dealer management systems

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Dataminr and Crisis24 Announce Strategic Partnership to Pioneer the Future of AI-Powered Global Risk Management

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Dataminr and Crisis24 Announce Strategic Partnership to Pioneer the Future of AI-Powered Global Risk Management

The partnership will create the industry’s most advanced Critical Event Management (CEM) platform with unmatched AI and agentic capabilities

Dataminr, the leader in AI-powered real-time event, threat and risk intelligence, and Crisis24, the global, AI-enhanced leader in integrated risk management, intelligence-led security and medical operations, personal protection, medical concierge and crisis consulting, announced a multi-year strategic partnership. This agreement will bring Dataminr’s industry-leading real‑time intelligence and agentic AI capabilities to Crisis24’s leading proprietary risk management platform that is trusted by Fortune 500 companies and other global organizations to manage risk and critical events.

Dataminr’s AI platform identifies, in real-time, the most critical and relevant events, threats and risks from within more than one million public data sources, across text, image, video, audio, and sensor data. Combining this platform with Crisis24’s industry leading critical event management platform will deliver significantly faster signal to action with deeper contextual intelligence for organizations managing complex and ever evolving risks. This comes at a time when the industry is entering a new age where AI-powered intelligence and agentic workflows will fundamentally reshape how organizations protect their people, facilities, and operations.

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“In today’s risk environment, speed and relevance are power,” said Gregoire Pinton, Managing Director and Global Head of Integrated Risk Management, Crisis24. “By uniting Dataminr’s comprehensive early‑signal detection and deep context with Crisis24’s innovative technology, ‘human and machine’ multi-layered intelligence, risk management workflows, mass notification, and global response capabilities, we are redefining how organizations anticipate and act on risk. This integrated solution empowers leaders to know sooner, decide faster, and respond with confidence.”

“By embedding our industry-leading AI-powered real-time event, threat, and risk intelligence into Crisis24’s industry-leading critical event management platforms, we’re empowering leaders to act with unprecedented speed and confidence to reduce risk and better protect people, assets, and operations,” said Matt Harrell, Chief Partner Officer at Dataminr.

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Using Agentic and Predictive AI to Set a New Standard in Critical Event Management

Together, Dataminr and Crisis24 will provide clients with an unprecedented level of precision and situational awareness when managing critical events, from within a ‘single pane of glass’ solution that combines visualization tools, automation, analytics, assistance, and response.

The partnership dramatically reduces the time it takes for organizations to detect and respond to events worldwide. This integrated environment will be the first to feature Dataminr’s advanced AI capabilities, including ReGenAI Live Briefs, autonomous Intel Agents that provide critical context about breaking events, and Predictive Intelligence. These innovations will be followed with client-tailored intelligence that fuses Dataminr’s signals with internal customer data to adapt the capabilities of Live Briefs, Intel Agents, and Predictive Intelligence to each organization’s unique operations, risk profile, and assets.

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Commvault Extends Enterprise Resilience to Structured and AI Data with Real-Time Governance Controls

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Commvault Extends Enterprise Resilience to Structured and AI Data with Real-Time Governance Controls

Expansion of Data Security Posture Management and real-time Data Access Governance, powered by Satori, helps security teams reduce AI and cloud data risks

Commvault, a leader in unified resilience at enterprise scale, announced an expansion of its data and AI security capabilities within Commvault Cloud, enabled via its recent acquisition of Satori. The advancements extend data discovery, classification, and risk assessment into structured data environments and introduce real-time access governance for structured databases, including vector databases used in AI applications. These innovations expand Commvault’s existing data security posture management (DSPM) functionality for unstructured data, while data access governance adds real-time control of structured data access.

These advancements also unify visibility by identifying sensitive data, surfacing exposure and policy violations, and consolidating risk insights to help organizations prioritize remediation based on impact. This yields improved resilience, prioritized risk remediation, support for compliance, and reduced data exposure to strengthen resilience across both production and backup data.

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In the face of evolving threats, sensitive or regulated data continues to be a key target for cyberattacks. In fact, 90% of organizations have exposed sensitive data that can be surfaced by AI,while a separate study uncovered that 46% of all breaches involve customer personally identifiable information (PII) and 40% involve employee PII.2 Without comprehensive discovery, classification, and access controls, that sensitive data can be stolen by bad actors, increase the risk of security breaches and non-compliance with privacy regulations, and complicate recovery efforts.

The Commvault Cloud platform delivers broad data discovery, classification, and policy enforcement across structured, semi-structured, and unstructured data in hybrid and multi-cloud environments. The new advancements include:

  • AI-enabled classification capabilities: Automatically identify and classify sensitive data across the enterprise, highlighting environments with high concentrations of sensitive data, excessive access, or data that has been retained longer than intended.
  • Data access governance: Monitor and control how structured data is accessed and used, helping to reduce the risk of data leakage, including via AI models or generative AI outputs.
  • Enhanced resilience via Commvault Cloud: Unifying data visibility and access controls directly into cyber resilience and cyber recovery workflows enables organizations to reduce data risk before an incident and recover more effectively afterward.

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“As organizations expand into cloud and AI-driven environments, sensitive data is increasingly distributed across structured and unstructured systems,” said Yoav Cohen, Vice President of Product Management at Commvault and Co-founder of Satori. “Without clear visibility and real-time access controls, that data can become overexposed and difficult to manage. By extending discovery and governance into structured data environments, we are helping organizations reduce unnecessary exposure and strengthen resilience across both live and backup data.”

“As enterprises race to scale AI, data governance is emerging as a critical gap. Legacy data security tools weren’t built for environments where AI models can inadvertently expose sensitive information buried in vector databases and cloud data warehouses,” said Jennifer Glenn, Research Director, Data and Information Security, IDC. “Unifying the convergence of data security posture management and cyber resilience equips CISOs and CIOs with the tools they need to manage AI-driven risk across their expanding AI footprint.”

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MediaScience Unveils Breakthrough AI “Ad Cloning” Technology That Enables Element-by-Element Creative Testing

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MediaScience Unveils Breakthrough AI "Ad Cloning" Technology That Enables Element-by-Element Creative Testing

New MediaPET-powered methodology allows researchers to perfectly recreate advertisements using AI, enabling precise measurement of every creative element.

MediaScience announced a major breakthrough in advertising research with its latest feature, “Creative Twin™.” The company, known for its work as a global leader in media and advertising innovation, has developed a new AI-powered methodology that enables researchers to perfectly recreate advertisements and test the impact of every individual creative element within them.

The announcement will be presented today at the Advertising Research Foundation’s (ARF) Audience x Science annual conference by founder and CEO, Duane Varan.

Developed using proprietary software within MediaPET.ai, a MediaScience spinoff, the innovation allows MediaScience to generate AI-based replicas of existing advertisements that are indistinguishable from the original creative.

In controlled testing with 812 respondents in the United States, conducted in collaboration with the Ehrenberg-Bass Institute, one of the world’s leading marketing science academic research centers, audiences were unable to differentiate between the original advertisement and the AI-generated version. Once an ad has been recreated in this format, every component within it can then be systematically modified to measure its precise impact on audience response.

The “Creative Twin” opens the door to testing variables that were previously difficult or prohibitively expensive to isolate.

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By digitally manipulating every element of a video ad, for example, the platform allows marketers to test identical executions with different celebrities —or with none at all—revealing the precise brand fit and incremental value each talent delivers.

The technology also enables creative optimization at scale for addressable advertising. Instead of spreading budgets across multiple executions, brands can produce one high-quality ad and digitally adapt it for different audiences—for example, replacing a straight—haired model in a shampoo commercial with the same model now with curly hair (targeted to women who style their hair curly) —delivering personalized creative without compromising production value.

In the case of shampoo, for example, women with curly hair were exposed either to the original ad, which featured a model with straight hair, or an AI-modified version of the same model with curly hair. The curly hair AI version significantly outperformed the original straight-haired version, delivering higher brand recognition, brand attitude and brand choice (indicative of purchase likelihood).

“This represents a fundamental shift in how advertising creative can be evaluated and optimized,” said Duane Varan, CEO of MediaScience and MediaPET. “For the first time, researchers can isolate and measure the contribution of individual creative elements within an advertisement, providing marketers with unprecedented clarity about what truly drives effectiveness. And they can now properly optimize and personalize ads without compromising on production quality.”

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Other examples include:

  • A premium puppy food commercial featuring a Labrador can be modified to feature a poodle or French bulldog and delivered to owners of those specific breeds.
  • A coffee ad can explore what the relative contribution of a particular celebrity is. Would an alternative celebrity boost ad impact? Would a generic model deliver similar results?

In each case, the modified advertisement retains the full production quality of the original creative.

The methodology not only supports optimization of advertising creative but also enables precise measurement of the economic value of individual creative decisions, including casting, visuals, messaging, and other production choices.

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Adaptiva Introduces Aida, an Enterprise-Safe AI Advisor for Autonomous Endpoint Management

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Adaptiva Introduces Aida, an Enterprise-Safe AI Advisor for Autonomous Endpoint Management

New AI-powered platform helps IT and Security teams move from exposure insight to real-time action at enterprise scale

Adaptiva, a global leader in autonomous endpoint management, announced the launch of Aida, an enterprise-safe AI advisor designed to help IT and Security Operations teams reduce exposure risk and take action across large, complex environments simply by asking.

Built for organizations managing hundreds of thousands of endpoints, Aida transforms real-time endpoint data into instant answers and insights. By combining natural-language interaction with Adaptiva’s proven endpoint platform, Aida enables teams to quickly understand patch posture, risk, and compliance.

“Security and IT teams are overwhelmed by exposure data but starved for time,” said Dr. Deepak Kumar, Founder and CEO of Adaptiva. “Aida changes that dynamic. It turns real-time endpoint data into immediate answers, helping teams reduce risk faster and operate at the speed and scale modern enterprises demand.”

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From Answers to Autonomous Action

Aida launches with an intuitive Ask Aida experience, allowing users to query their environment in plain English and instantly generate dashboards, charts, and reports. Aida will evolve beyond insights to support AI-powered mitigation, remediation, and orchestration, enabling autonomous IT and SecOps workflows.

Unlike general-purpose AI tools, Aida is purpose-built for enterprise environments and built on Adaptiva’s proven autonomous endpoint management platform.

Enterprise-Safe by Design

Aida is powered by an Adaptiva-controlled LLM microservice that ensures customer data is never shared externally, delivering AI capabilities that meet enterprise security and compliance requirements. This architecture allows organizations to confidently adopt AI without compromising sensitive endpoint data.

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Extending Exposure Management Leadership

Aida strengthens Adaptiva’s ability to support modern exposure management strategies by bridging the gap between insight and action. While many AI solutions in the market are limited to answering questions and prioritizing risk, Aida provides real-time visibility into endpoint posture and accelerates how teams move from insight to informed action.

“Exposure management requires both clarity and speed,” added Kumar. “Aida gives organizations immediate access to trusted endpoint intelligence so they can prioritize effectively and respond with confidence, while building toward more autonomous operations in the future.”

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BMC Advances Trusted AI Orchestration With New Control-M Capabilities

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BMC Advances Trusted AI Orchestration With New Control-M Capabilities

Agentic AI innovations and expanded integrations enable enterprises to operationalize AI workloads reliably at scale

BMC, a global leader in software solutions enabling business faster than humanly possible, announced new AI innovations for the Control-M solution that advance the orchestration foundation enterprises need to compete, differentiate, and thrive in an AI-powered world.

For most large enterprises, the complexity of modern data, application, and AI pipelines is growing faster than the operational foundations needed to run them reliably. Orchestrating business processes across hybrid environments and diverse technologies is among the most difficult challenges of the AI era—and a key reason that enterprises come to BMC first when trust and reliability are essential.

New AI capabilities and innovations include:

New Agentic AI Capabilities for AI-Powered Orchestration Across Workflows

The Control-M solution expands its agentic AI investments with new AI-driven capabilities across the workflow lifecycle to help teams build, run, and manage workflows more efficiently. These innovations simplify workflow planning and job creation, analyze workflow performance and failures to accelerate troubleshooting, and generate automated operational insights that highlight optimization opportunities. With these updates, users can reduce manual effort, accelerate design, and proactively surface risks within governed, enterprise-grade boundaries.

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Orchestration of AI Agents

The Control-M solution also expands its AI orchestration integrations, making it easier for organizations to orchestrate AI agents and AI-powered tasks alongside data pipelines, applications, and operational workflows. New integrations with solutions such as CrewAI, LangGraph, and Snowflake Cortex enable teams to run AI-driven processes with the same reliability, visibility, and governance expected from every Control-M workflow. By connecting AI agents into enterprise workflows, Control-M helps organizations move AI from isolated experiments to trusted production assets that deliver business value safely and at scale.

AI for Multiple Environments – Jett (the Control-M AI advisor) and AI Workflow Creator Available to More Customers

BMC is expanding the power of generative AI across deployment models by bringing advanced AI capabilities to self-hosted Control-M environments. With Jett, the Control-M AI advisor, and the AI Workflow Creator, organizations can benefit from intelligent workflow creation and in-context guidance regardless of where they run the Control-M solution. This expansion enables more customers to leverage modern assistive technologies to design, understand, and manage workflows more efficiently.

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New Integrations and Connectivity for Easier Orchestration 

The Control-M solution also expands connectivity and integration capabilities to help organizations automate faster across hybrid environments. Enhancements to Managed File Transfer improve performance, reliability, and governance, as well as new high availability and disaster recovery support, which strengthen data movement. Expanded agentless execution for Windows and new out-of-the-box integrations further reduce scripting, accelerate onboarding, and enable more scalable automation across applications and data platforms.

“Across every industry, AI is accelerating, and workflows are becoming more dynamic than ever. The enterprises that lead will be the ones that build a trusted orchestration foundation now,” said Abhijit Kakhandiki, senior vice president and general manager for Digital Business Automation at BMC. “By integrating generative and agentic AI directly into the orchestration lifecycle, we are ensuring that AI is not just an isolated tool, but a trusted, governed, and scalable foundation for execution of mission-critical workloads.”

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FreeWheel Integrates Tunnl Audiences to Accelerate Political and Public Affairs Targeting Across CTV for 2026 Campaigns

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FreeWheel Integrates Tunnl Audiences to Accelerate Political and Public Affairs Targeting Across CTV for 2026 Campaigns

In adding latest data partner to its platform, FreeWheel cements position as a one-stop shop for political buyers targeting audiences with precision at scale and a streamlined process from strategy to activation.

SmartBear Survey: 70% of Software Experts Concerned that Application Quality is Suffering Given Faster AI Code Development

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CNaught Launches Carbonlog, the First Tool to Track the Carbon Footprint of AI-Assisted Software Development

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60% have already experienced quality issues in the past year as development outpaced testing

Neat Appoints Javed Khan as CEO to Lead AI Transformation

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Neat Appoints Javed Khan as CEO to Lead AI Transformation

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Neat, the pioneering video technology company, announced the appointment of Javed Khan as Chief Executive Officer (CEO). Khan, a seasoned technology executive with a proven track record in AI-driven transformation, takes the helm as the company gears up for global expansion. The appointment of Khan signals Neat’s commitment to deeper investments in artificial intelligence as the engine for its next wave of innovation. With a career defined by bold leadership, technical mastery, and a product-first mindset, Khan is uniquely positioned to unite sophisticated edge computing with Neat’s simple, elegant user experiences.

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“Neat is a product-centric company that is relentlessly focused on simplicity and intelligence. I’m honored to join the team and energized to be working alongside some of the brightest minds as we define the next generation of collaboration.” – Javed Khan

Khan joins Neat following his tenure at Aptiv, where he served as Executive Vice President of Intelligent Systems, building intelligent edge solutions across automotive, transportation, robotics, aerospace, and defense. Prior to Aptiv, Khan was the Senior Vice President and General Manager of Cisco Collaboration, where he led the turnaround and modernization of the Cisco Webex portfolio across video conferencing, video devices, and contact center solutions during and after the pandemic.

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“Javed brings a rare combination of deep technical expertise and proven enterprise leadership,” said OJ Winge, on behalf of the Neat Board. “His experience scaling complex, AI-enabled systems and leading global collaboration platforms positions Neat to build upon our technology leadership and accelerate our long-term growth.”

“Recent advancements in edge computing and large language models are allowing us to embed AI into edge devices running in the conference room. This architectural shift will allow us to unlock entirely new collaboration experiences. I am excited to join Neat as we have the unique opportunity to lead this transition,” said Javed Khan, CEO of Neat. “Neat is a product-centric company that is relentlessly focused on simplicity and intelligence. I’m honored to join the team and energized to be working alongside some of the brightest minds as we define the next generation of collaboration.”

Khan’s arrival comes at a pivotal time as Neat transitions from disruptive challenger to dominant enterprise force, deepening its focus on intelligent edge computing and accelerating toward public market readiness. His long-standing relationships within the industry—including with members of the Neat founding team—promises a seamless leadership transition.

Khan succeeds Janine Pelosi, who led Neat through a period of significant expansion, strengthened the company’s operational foundation, and broadened its product portfolio.

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Snowflake Launches Project SnowWork, Bringing Outcome-Driven AI to Every Business User

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Snowflake Launches Project SnowWork, Bringing Outcome-Driven AI to Every Business User

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Project SnowWork brings agentic intelligence directly to business users’ desktops to accelerate productivity and get work done faster

  • Designed to orchestrate planning, analysis, and execution, Project SnowWork is an autonomous enterprise AI platform that helps business users accelerate everyday work

  • Launching in research preview to a limited set of customers, Project SnowWork handles complex, multi-step tasks and delivers real, data-driven outcomes to business users

  • Project SnowWork brings Snowflake’s vision for the agentic enterprise to life, where enterprise data, intelligence, and action are connected in a governed way

Snowflake , the AI Data Cloud company, announced the research preview of Project SnowWork, a new autonomous enterprise AI platform designed to help business users massively accelerate workflows. Acting as a proactive AI partner, Project SnowWork empowers individuals and teams across the business to simply ask for what they need and have Project SnowWork securely complete multi-step tasks based on conversational prompts.

Whether it’s building a board-ready forecast slide deck, creating a spreadsheet that identifies churn risks, or uncovering supply chain bottlenecks, Project SnowWork autonomously executes simple or complex workflows end-to-end to drive action.

“We are entering the era of the agentic enterprise, ushering in a fundamentally new way to work. This shift is about much more than technology, it’s about unlocking new levels of productivity and efficiency by embedding intelligence directly into the operating fabric of the enterprise,” said Sridhar Ramaswamy, Chief Executive Officer, Snowflake. “Project SnowWork looks to put secure, data-grounded AI agents on every surface, so business leaders and operators can move from question to action instantly. By elevating AI from experimentation to enterprise-grade autonomous execution, Project SnowWork serves as the secure foundation for how modern enterprises will get work done in the AI era.”

The rise of the agentic enterprise is not just about answering questions with AI, rather, it’s a shift to using AI to drive decisions and action. This future will require enterprises to connect intelligence, applications, and enterprise data and context into a trusted foundation that can continuously coordinate action at scale.

Project SnowWork makes this possible by bringing Snowflake’s enterprise data platform and AI capabilities directly to business users through a simple, outcome-driven desktop experience. More than a productivity agent, Project SnowWork can:

  • Plan and autonomously execute simple or complex multi-step workflows across governed Snowflake data to deliver finished outputs — from reprioritizing sales territories to executive-ready presentations.
  • Generate analysis with recommended actions, turning insights into prioritized next steps tailored to each business role.
  • Securely orchestrate data, AI, and enterprise systems to complete tasks end-to-end, reducing backlogs and accelerating decisions.

Unlike general-purpose AI agents, Project SnowWork is built on a single enterprise-wide source of truth with governed metrics, shared business definitions, cross-cloud interoperability, and built-in security and auditability.

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Key capabilities Project SnowWork delivers:

  • Pre-Built, Persona-Specific Skills: Provides pre-configured, role-aware AI “profiles” for finance, sales, marketing, operations, and more that understand common business workflows, terminology, and KPIs, accelerating time-to-value.
  • Multi-Step Task Completion: Enables the AI platform to autonomously plan and complete complex workflows — querying data, applying analysis, synthesizing insights, generating structured deliverables, and preparing next steps within a single interaction — compressing reporting cycles and eliminating manual coordination across systems.
  • Built-In Security and Access Controls: Automatically enforces Snowflake’s role-based access controls (RBAC), masking policies, audit logging, and data governance rules — ensuring AI actions operate within the same trusted perimeter as enterprise data.

“Enterprises have invested heavily in data platforms and AI, yet the last mile of translating governed data into everyday business outcomes remains largely manual,” said Sanjeev Mohan, Principal at SanjMo. “Project SnowWork represents a meaningful shift from AI as an analytical tool to AI as an execution layer embedded directly into enterprise workflows. By grounding autonomous task execution in trusted, governed Snowflake data, shared business definitions, and cross-cloud and cross-domain interoperability, the company is extending its platform from a system of insight to a system of action, which is where measurable business value is ultimately realized.”

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AI Built for Action, Not Just Insights

Organizations have invested heavily in modern data platforms and AI, yet most business users still rely on analysts, static dashboards, and siloed systems to answer basic questions. Today’s AI tools often require technical expertise and lack the enterprise data foundation needed to deliver trusted, actionable outcomes.

The result is a widening gap between AI’s potential and its real business impact. Project SnowWork closes that gap by securely embedding AI into the flow of work, enabling every employee to operate with the speed, precision, and insight once reserved for data specialists.

It allows business users to move from intent to actions and outcomes without filing tickets with data teams or searching for static dashboards. For example, Sales Operations teams can now automate repetitive reporting, work across multiple data sources without coding, and generate presentation-ready deliverables in minutes instead of days.

Advancing Snowflake’s Enterprise AI Architecture

Snowflake’s portfolio of AI products enables every employee to use AI on trusted, governed data so organizations can embrace the agentic enterprise where intelligence drives secure, data-driven action across the business.

For business users, Snowflake Intelligence is the enterprise intelligence agent built to turn all the knowledge within an organization into trusted business insights. It enables every employee to ask complex questions in natural language and move beyond the “what” to uncover the critical “why.” Grounded in governed enterprise data and shared business context, it delivers transparent, verifiable answers inside Snowflake’s secure perimeter. Project SnowWork extends this idea by helping teams act on those insights and execute multi-step workflows directly on top of trusted Snowflake data.

For builders that want to turn complex data engineering, analytics, machine learning, and agent-building tasks into simple, informed interactions, Cortex Code is a data-native AI coding agent that automates and accelerates end-to-end enterprise development. It generates production-ready code, orchestrates workflows, and enforces best practices, enabling teams to move from prototype to deployment with speed and confidence.

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ActiveCampaign is First to Launch AI that Acts, Not Just Answers at Spring Innovation Keynote

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MarcomJobsList.com Launches Game-Changing Job Board for Marketing & Communications Professionals

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New proactive, agent-to-user AI and personalization bring enterprise-grade marketing intelligence to small and mid-size businesses

ActiveCampaign, a leading autonomous marketing platform, will spotlight two first-to-market capabilities at its Spring 2026 Innovation Keynote on April 8: agent-to-user AI, where AI autonomously initiates insights and recommendations on behalf of marketers based on performance signals, and AI personalization that allows businesses to tailor Active Intelligence to their brand voice and priorities.

“The future of marketing isn’t just AI that responds when asked; it’s AI that works alongside you,” said Jason VandeBoom, Founder and CEO of ActiveCampaign. “These innovations move us beyond prompt-and-respond systems to AI that monitors performance, identifies opportunities, and recommends action automatically, helping customers move forward faster.”

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Two First-to-Market Capabilities

  • AI that works for and ahead of you: ActiveCampaign’s Active Intelligence acts as an always-on marketing partner, continuously monitoring campaigns and automations, flagging issues, and surfacing next-best actions without constant manual intervention. This includes:
    • AI Performance Intelligence: Continuously analyzes campaign and automation performance against billions of signals across the platform, identifying what is outperforming, what is underperforming, and why. For example, a retailer might be alerted that open rates are 20% above comparable brands, with AI pinpointing the specific creative, timing, and audience factors driving the lift.
    • AI Content Optimization: Monitors campaign engagement and automatically diagnoses when performance drops. Identifies the factors most likely impacting results (e.g., subject lines, preheaders or personalization strategies) and recommends adjustments informed by patterns across the account.
    • Autonomous Campaign Optimization: Analyzes campaign and automation performance in real time, identifying opportunities to improve audience targeting, send timing, and engagement frequency. Generates optimized versions of existing automations or proposes new flows to improve underperforming campaigns, ready for marketer review and activation.
  • AI Behavior Customization: ActiveCampaign is the first marketing automation platform to introduce custom AI instructions, allowing SMBs to configure how Active Intelligence operates across the platform. Businesses define brand voice, priorities, and strategic preferences once, and the AI applies them everywhere, shaping insights, recommendations, campaign creation, and automations so outputs consistently reflect how the business actually markets. Agency partners can apply and manage these AI behaviors across all the accounts they oversee, tailoring intelligence to each client while scaling their own expertise and best practices across their portfolio.

“Before ActiveCampaign, we were spending time we didn’t have trying to figure out why our marketing wasn’t working,” said Chris Clark, Founder and CEO at racquet sport event company, Toss and Spin. “Now the platform tells us clearly where to focus. Instead of digging through reports or guessing what to optimize, we act on clear recommendations, and we’re now at 90% facility capacity.”

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What to Expect from the Spring 2026 Innovation Keynote

The event will provide an inside look at how ActiveCampaign is advancing its autonomous marketing vision, featuring:

  • The next chapter of autonomous marketing: Chief Product & Technology Officer Chai Atreya will share how agent-to-user recommendations and AI personalization advance ActiveCampaign’s mission of AI that works for and alongside marketers.
  • Live demos of first-to-market capabilities: A deep dive into autonomous AI recommendations and personalization, and what proactive, brand-tailored AI means for SMBs.
  • Customer and partner success stories: A customer panel featuring the Founder and CEO of Toss and Spin and the Founder and CEO of Zen Anchor and Analytics Mates, along with a partner panel featuring the Founder and General Manager of eduConverse and the President of Financialize, sharing how Active Intelligence has driven results for their businesses.

The Bigger Picture

As AI reshapes how businesses engage customers, marketers need platforms that go beyond mere task execution. They need systems that proactively surface opportunities, take action, and continuously improve performance. With recent enhancements, like the acquisition of Feedback Intelligence, ActiveCampaign is expanding its ability to power autonomous marketing workflows. These capabilities create a continuous improvement engine where AI agents learn and improve over time, delivering AI that users trust like an extension of their team.

“Many AI tools in marketing are reactive, which is useful when you know what to ask for, but limited otherwise,” said Roger Beharry Lall, Research Director, SMB Marketing Applications and Agents at IDC. “What ActiveCampaign is doing with agent-to-user AI, surfacing recommendations before marketers ask, is what the industry, and especially small businesses with limited expertise, really needs. AI that doesn’t just respond, but initiates. Combined with brand-level customization, SMBs now have capabilities that traditionally required an enterprise-level investment.”

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TVEyes Announces Significant Investment in Product Innovation and Content

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TVEyes Announces Significant Investment in Product Innovation and Content

The leading media intelligence company for global audio and video coverage announces the launch of its new, premium media player for API partners, while also expanding its global monitoring footprints across APAC content and 19 additional European Union markets.

TVEyes, the global leader in broadcast, podcast and online video media monitoring and brand intelligence, is announcing a series of innovations designed to advance its media intelligence platform for partners worldwide. This includes the launch of a new premium and integrated media player that delivers an enhanced media editing and viewing portal for customers, as well as a significant expansion in global reach through deep broadcast source additions across Europe and Asia Pacific.

The new premium media player is an intuitive solution designed for API partner platforms and their end users to quickly search, analyze and act on media content across broadcast and podcast sources. This provides TVEyes partners with a more efficient API solution and an advanced and unified viewing experience for their customers.

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Built with a flexible architecture, the player simplifies integration for partner platforms while maintaining a consistent playback experience across media types. For end users, features such as a visual filmstrip with thumbnail previews and transcript-based tools make it easier to locate, analyze, and share key segments.

The launch reflects TVEyes’ continued investment in content and innovation, and in the long-term success of its API partners. Built on a scalable global infrastructure and decades of indexed media content, TVEyes transforms broadcast, podcast and online video signals into structured data and actionable media intelligence. This enables partners to deliver deeper insights, faster discovery and more powerful media analysis to their end users.

“The launch of our new premium media player is an important part of our mission to make the world’s most impactful media signals instantly accessible and actionable,” said Daren Benzi, Chief Commercial Officer at TVEyes. “Through ongoing investments in infrastructure, data, content and technology, TVEyes provides our partners and their customers with a more powerful way to capture the most influential media, surface the most relevant data, and stay ahead of media events that impact brands and drive public conversations.”

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Alongside the player launch, TVEyes is expanding its global media intelligence footprint to meet the growing demand for international media visibility. The company is increasing coverage across the European Union with 19 new markets, bringing a broader range of national and regional broadcast sources into a single unified feed.

New markets include Belgium, Bulgaria, Cyprus, Finland, Greece, Luxembourg, Malta, Poland, and Portugal, with additional expansion underway across Albania, Croatia, Hungary, Iceland, Latvia, Lithuania, Norway, Romania, Slovakia, and Slovenia. The growth builds on recent launches in Malaysia and Indonesia and further expands the breadth of TVEyes’ global media coverage.

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ZINFI Technologies Launches ZINFI.AI: The Industry’s First Knowledge Base for Partner Orchestration & Ecosystem Management (POEM™)

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ZINFI Technologies Launches ZINFI.AI: The Industry's First Knowledge Base for Partner Orchestration & Ecosystem Management (POEM™)

New AI-Powered Platform Provides a Comprehensive Framework for IT and Manufacturing Leaders to Design, Build, and Grow High-Performance Ecosystems; Beta Access Announced for April 2026.

ZINFI Technologies, Inc., announced the launch of ZINFI.AI, a revolutionary global knowledge base dedicated to Partner Orchestration & Ecosystem Management (POEM™). Representing the next evolution beyond traditional partner management, ZINFI.AI provides the methodology, tools, and intelligence to help organizations transition from transactional relationships to strategic ecosystem orchestration.

ZINFI.AI is specifically designed for leaders in the IT and Manufacturing sectors who are responsible for building and scaling complex partner ecosystems. The platform combines advanced artificial intelligence with decades of partner expertise to deliver a “human-in-the-loop” approach, ensuring that AI-generated recommendations are validated by industry nuance and strategic thinking.

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The First-of-its-Kind POEM™ Framework
At the heart of ZINFI.AI is the POEM™ framework, which addresses the complete partner lifecycle through three interconnected layers:

  • The 8 Lifecycle Stages: A roadmap for the entire partner journey, including Strategize, Recruit, Onboard, Enable, Market, Sell, Incentivize, and Accelerate.
  • The 6Ps Assessment Model: A comprehensive evaluation tool covering Partners, Programs, Processes, People, Platforms, and Performance KPIs to ensure ecosystem maturity.
  • AI-Powered Intelligence: Contextual recommendations and pattern recognition built on a foundation of over 5,000 expert articles and 100+ specialized tools and templates.

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Beta Access and Ecosystem Expansion
ZINFI also announced that by early April 2026, the ZINFI.AI platform will be opened to a select group of beta users. These participants will gain early access to a deeper knowledge base and an expanded suite of POEM-specific tools designed to help organizations navigate the complexities of modern partner relationships and market dynamics.

“POEM™ is not simply a new name for old practices; it represents a fundamental reimagining of how organizations drive sustainable competitive advantage,” said Sugata Sanyal, founder and CEO of ZINFI. “With ZINFI.AI, we are providing the industry’s first specialized knowledge base that gives IT and Manufacturing leaders the ‘map’ to navigate the future of ecosystem-led growth.”

Sector-Based Intelligence
Recognizing that manufacturing ecosystems operate differently from software-based technology alliances, ZINFI.AI delivers tailored guidance for specific industry contexts. This ensures that organizations receive relevant, high-impact strategies rather than generic “one-size-fits-all” best practices.

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AI Development Company IIH Global Expands AI Development Services in the Global Market

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AI Development Company IIH Global Expands AI Development Services in the Global Market

IIH Global – WordCamp Ahmedabad 2019 a.k.a 3.0

Leading AI Development Company IIH Global launches advanced AI Development Services UK to help businesses scale with AI-driven solutions.

IIH Global, a globally recognized AI Development Company, announced the expansion of its advanced development Services in UK, reinforcing its position as a trusted technology partner for businesses seeking intelligent, scalable, and future-ready solutions.

As a dedicated AI Development Company, our mission is to deliver AI Development Services that create real business value and long-term growth for our clients.”

— Sanjay Panchal, Director at IIH Global

As organizations across industries accelerate their digital transformation journeys, the demand for reliable and results-driven AI Development Services continues to grow. IIH Global is addressing this demand by delivering tailored AI solutions that help businesses improve efficiency, unlock insights, and create competitive advantages in an increasingly data-driven world.

With a strong presence in the United Kingdom, IIH Global’s AI Development Services are specifically designed to meet the needs of UK-based startups, SMEs, and enterprises. The company combines global delivery capabilities with local market expertise, ensuring solutions that align with business goals, compliance requirements, and evolving industry standards.

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“Our focus as an AI Development Company is not just to build intelligent systems, but to deliver real business impact, we help organizations move from AI exploration to full-scale implementation with measurable outcomes”, said Milan Patel, CEO from IIH Global.

IIH Global offers end-to-end AI Development Services, covering strategy, consulting, development, deployment, and ongoing optimization. These services are built to support businesses at every stage of their AI journey, from initial concept validation to enterprise-wide implementation.

As a leading AI Development Company, IIH Global specializes in creating custom AI solutions tailored to specific business challenges. Whether it is predictive analytics, intelligent automation, or data-driven decision-making systems, each solution is engineered to deliver high performance and scalability.

The company’s AI Development Services in UK also include advanced machine learning model development, enabling businesses to process large volumes of data and extract actionable insights. These capabilities empower organizations to make faster, smarter decisions while reducing operational risks.

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In addition, IIH Global provides natural language processing (NLP) solutions as part of its AI Development Services, allowing businesses to enhance customer engagement through intelligent chatbots, conversational AI platforms, and automated support systems.

Automation remains a core focus, with AI-powered workflows designed to eliminate repetitive tasks, improve accuracy, and increase productivity. Through its comprehensive AI Development Services UK, IIH Global enables businesses to streamline operations and optimize resource utilization.

One of the key strengths of IIH Global as an AI Development Company is its ability to integrate AI into existing systems. This ensures that businesses can adopt advanced technologies without disrupting their current operations, making the transition to AI seamless and efficient.

IIH Global’s AI Development Services are already delivering measurable results across industries such as healthcare, fintech, retail, logistics, and construction. Businesses are leveraging these solutions to enhance customer experiences, improve operational efficiency, and drive sustainable growth.

With its expanding footprint in the UK, IIH Global is strengthening its commitment to delivering high-quality AI Development Services UK. The company’s local presence enables closer collaboration with clients, faster project delivery, and a deeper understanding of market-specific needs.

Quality, security, and scalability remain central to IIH Global’s approach as an AI Development Company. The company follows best practices and international standards to ensure that all AI solutions are robust, secure, and built for long-term success.

Looking ahead, IIH Global plans to further enhance its AI Development Services by investing in emerging technologies and continuous innovation. The company aims to help businesses stay ahead in a rapidly evolving digital landscape by providing intelligent solutions that drive real results.

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Canto Deepens Commitment to Product-Led Brands with DAM for Products

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Canto Deepens Commitment to Product-Led Brands with DAM for Products

A single connected system that gets product content to market faster and keeps it accurate everywhere it lives.

Canto, the leader in AI-powered digital asset management, announced continued capability expansion of DAM for Products, its platform for brands that manage, launch, and sell physical goods. As product content complexity accelerates across ecommerce, social marketplaces, and retail channels, Canto is deepening its investment in the tools and integrations that help teams create, manage, and activate product content at scale—including new Shopify and Amazon integrations.

Marketing, ecommerce, and creative teams waste too much time chasing down assets, correcting channel inconsistencies, and manually pushing updates across storefronts. DAM for Products eliminates that. Built on Canto’s leading digital asset management platform, it connects product images, SKUs, and attributes in one place — and activates them across every channel automatically.

“Getting product content right across every channel is one of the hardest operational challenges brands face, and the cost of inconsistency compounds the more channels you add,” said Alan Beiagi, Chief Product and Technology Officer, Canto. “DAM for Products gives brands the infrastructure to move faster without sacrificing accuracy or consistency, and our expanding integrations make those benefits even stronger.”

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New integrations with Shopify and Amazon enable brands to synchronize product assets and metadata from Canto directly into their storefronts, eliminating manual uploading and ensuring that every product page reflects current and brand-approved content. Canto Media Publisher, part of DAM for Products, ensures approved assets are delivered via CDN at the speed and scale commerce requires. Canto is also expanding its partner ecosystem to support additional commerce endpoints, with new syndication partnerships underway to help customers distribute product assets and information across more channels.

“Before Canto DAM for Products, our product data was scattered across Word docs, spreadsheets, emails, and team conversations, which created a real risk of using outdated or incorrect information,” said Christine Baker, Senior Graphic Designer & Production Lead at Marini SkinSolutions. “Now, we can keep our product imagery and data in one place and ensure updates are reflected in Shopify.”

For marketing and ecommerce teams managing frequent product launches, catalog updates, and multi-channel distribution, DAM for Products provides a single connected system that links creative assets to commerce outcomes. The result is product content that moves faster and arrives consistently across every channel.

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