New Contextual AI Platform enables custom model manipulation and advanced analytics for global enterprises

Lilt, the leading AI solution for enterprise translation, today at its third ever AI Day announced the company’s latest innovations, including third-party large language model integration in the Contextual AI Engine, self-serve model building and training, and advanced program analytics. As leading enterprises rush to implement a global AI and LLM strategy, Lilt’s Contextual AI Platform offers a quick and scalable path to achieving extraordinary ROI from AI.

“As the speed of AI innovation accelerates, global enterprises are eager to look beyond the hype and build AI programs that they can implement in production with best-in-class technology,” said Lilt CEO Spence Green. “We’re thrilled that we can now offer our customers the ability to orchestrate and manipulate company-specific LLMs that they can quickly operationalize at scale.”

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During the 90 minute live virtual event, Lilt announced several new features across multiple layers of the Contextual AI Platform, including:

LILT-V3 Models

Following the release of LILT-V2 in Spring 2023, Lilt today released LILT-V3, its newest-generation LLMs that offer improved output accuracy. LILT-V3 leverages cutting-edge methods for fine-tuning and prompting, with in-context learning and a training data system that optimizes for high-relevance domain-specific data sets and context. The model’s larger neural networks synthesize and generalize this information and interpret those high-relevance examples in real-time for optimal model outputs. With these models in production, Lilt customers can now advance their AI strategy for global customer reach and achieve incredible quality at scale.

Third-party LLM Support & Model Builder

Lilt’s newest foundation model integration enables customers to harness Amazon Translate, DeepL, and Google Translate models right within the Lilt Platform. With this new offering, businesses can now seamlessly choose their large language models of choice, unlocking the best models for their unique enterprise use case. As leading enterprises develop bespoke LLM strategies, the Lilt Contextual AI Platform enables comprehensive execution of that strategy across workflows and business systems.

This third-party LLM support pairs enterprise LLM preferences with Lilt’s proven production processes, linguists, and Translate tool – providing the highest quality Instant Translation for their use case.

Model Builder

Lilt additionally announced its Model Builder, a new management feature within the platform’s orchestration layer. Lilt Model Builder enables fine-tuning of third-party enterprise LLMs to be used in the Lilt Contextual AI Engine. The Lilt Platform has historically enabled model fine-tuning for Lilt’s proprietary LLMs, with a continuous training cycle that feeds training data to models in real time as human translators and verifiers perform work in the platform. With Model Builder, Lilt customers can now apply that same quality-verified training data to fine-tuning of third-party LLMs. Model Builder empowers Lilt customers to create purpose-built LLMs that generate customized, high quality, and brand aligned outputs for a range of business applications. Users can fine-tune their models in just a click with Lilt’s model building and training interface, optimizing model quality on the cadence of days or weeks instead of months or years.

Advanced Analytics

Lilt additionally announced new and improved premium program insights via a suite of advanced analytics tools that empower customers with in-depth insights into their Contextual AI program and operations, all within the Lilt Platform. This user-friendly tool offers customizable visualizations of vital metrics and program-level insights to support strategic decisions and optimization of  the AI strategy, quality, and business impact.

These new features are available to Lilt customers and are in production starting today,  powering enterprise use cases across a range of industries and business units.

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