Calsoft launches Synthetic Data Curation service powered by LLM agents, enabling enterprises to generate compliant, domain-specific datasets at scale for AI.

Calsoft Inc., a global digital engineering and Data & AI solutions provider, announced the launch of its 𝐒𝐲𝐧𝐭𝐡𝐞𝐭𝐢𝐜 𝐃𝐚𝐭𝐚 𝐂𝐮𝐫𝐚𝐭𝐢𝐨𝐧 service powered by Large Language Model (LLM) agents.

We designed this solution to help teams move faster without compromising on compliance or quality”
— Ankur Somani, Associate VP – Technology at Calsoft

As enterprises accelerate AI adoption, many face a critical bottleneck: access to clean, contextual, and compliant data. Whether due to privacy concerns, limited labeled datasets, or the need to simulate rare events, data gaps are slowing model development in key industries. Calsoft’s new service addresses this challenge by enabling large-scale generation of high-quality synthetic datasets tailored to specific domains.

This service is particularly relevant for teams working on fine-tuning foundation models, building domain-specific classification systems, or testing AI behavior in edge-case scenarios where real-world data is either insufficient or too sensitive to use. The ability to generate controlled, representative datasets without manual labeling or privacy concerns marks a shift in how enterprises can scale their AI pipelines.

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𝐓𝐡𝐞 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧: 𝐀𝐈-𝐃𝐫𝐢𝐯𝐞𝐧 𝐃𝐚𝐭𝐚 𝐂𝐮𝐫𝐚𝐭𝐢𝐨𝐧 𝐚𝐭 𝐒𝐜𝐚𝐥𝐞

Enterprises struggling with scarce or restricted datasets can now leverage Calsoft’s AI-driven curation service to:

– Scale: Leverage agentic pipelines of finetuned LLMs to generate millions of structured, semi-structured, or unstructured records within days

– Ensure Quality: Use multi-agent validation to maintain thematic accuracy, coherence, and domain alignment

– Maintain Compliance: Filter all outputs through PII-scrubbing and bias detection agents, ensuring governance at every step

Built on a closed-loop LLM agent architecture, the solution mirrors a human-in-the-loop process, where Generator agents create content, PII agents ensure privacy, Critic agents assess consistency, and Refiner agents improve flagged outputs.

𝐄𝐚𝐫𝐥𝐲 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 𝐟𝐫𝐨𝐦 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐞𝐝 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐏𝐢𝐥𝐨𝐭𝐬

In pilot programs with clients in finance and life sciences:

– Data preparation timelines were reduced by up to 70%

– Thematic accuracy in generated datasets exceeded 95%

– End-to-end delivery of production-ready data took under 72 hours

These outcomes demonstrate how synthetic data can move from being a workaround to becoming a viable, production-ready alternative to real-world datasets. Teams that were previously slowed by access delays or redaction pipelines are now able to generate usable data in a fraction of the time.

“We designed this solution to help teams move faster without compromising on compliance or quality,” said Ankur Somani, Associate VP – Technology at Calsoft. “With a closed-loop agent model, we’re delivering scalable synthetic data pipelines that are technically sound and deployment-ready.”

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𝐖𝐡𝐞𝐫𝐞 𝐈𝐭’𝐬 𝐁𝐞𝐢𝐧𝐠 𝐔𝐬𝐞𝐝

This offering is being adopted across sectors such as:

– Regulated industries: Finance, Insurance, Healthcare, Life Sciences

– Data-intensive operations: Log analytics, eCommerce personalization

– Emerging use cases: AI education, experimental model testing

Organizations in these sectors face recurring challenges when it comes to acquiring and validating training data. With Calsoft’s synthetic data curation service, these companies are able to accelerate time-to-value, reduce dependency on real user data, and extend model testing into scenarios that would be difficult to recreate otherwise.

“Our Synthetic Data Curation offering embodies Calsoft’s vision to democratize AI,” said Nilesh Chopda, Solution Architect. “By overcoming the data availability barrier, we’re empowering enterprises to innovate responsibly and at scale.”

𝐖𝐡𝐲 𝐂𝐚𝐥𝐬𝐨𝐟𝐭

With over two decades of experience in digital product engineering solutions, Calsoft focuses on technological innovation and engineering expertise to bring enterprise-grade safeguards to synthetic data curation, which includes:

– Peer-reviewed agent QA pipelines

– Built-in PII and bias filtering

– Custom domain adaptation

– Scalable delivery frameworks

These capabilities make the service suitable for enterprises looking to accelerate GenAI use cases while keeping control over data fidelity, risk, and compliance.