The Power of Proprietary Models: What Makes LILT's AI Different

Not all language models are created equal. LILT’s AI technology differs from other providers in four key aspects: superior quality, precision & compliance from enterprise-specific custom models; ability to learn in real time from human feedback and assets; transparent analytics for AI performance tracking; and native models that do not rely on outsourcing to third-party LLM providers.

Enterprise-specific custom models deliver consistent quality

Common practice in the industry is to act as orchestrators of third-party, off-the-shelf models, routing content to various MT engines like Google, DeepL, or AWS. LILT operates differently by building in-house, high-performance AI models customized for every enterprise and every data source.

When other solutions claim to offer “custom” AI, look deeper: their customizations are most likely limited to terminology tuning and do not involve training the underlying models. LILT’s proprietary models are precision-tuned with each customer’s unique brand assets and domain-specific data. The domain-specific fine-tuning is performed for functional areas like marketing, legal, and product, and across various industries like healthcare, finance, manufacturing, and retail. This ensures every translation is accurate, on-brand, and compliant.

Adaptive AI improves accuracy & reduces cost

LILT's proprietary AI model self-learns from human feedback in real time, instantly applying corrections within the current project and globally across all enterprise content. This real-time adaptation eliminates redundant error corrections, which drastically reduces costs while increasing accuracy and speed-to-market.

In "bolted-on" systems where AI is secondary, you’ll often find yourself paying for the same error to be corrected multiple times within the same document and across global projects. Even those who offer a paid add-on service to help train “custom” engines aren’t really offering adaptive AI: their training is static (occurring in one-time bursts) rather than continuous. Static training means that a change in brand voice may require a wait of 6 to 12 months for the next retraining session to update the model, putting product launches and market expansion goals at risk.

Transparent analytics track AI performance

For users of most AI solutions, it’s common to encounter a “black box”: something that appears to work but offers little visibility into AI-driven efficiency. Besides the operational slow-downs caused by opacity, this also makes it difficult to justify cost and impact to leadership. In answer to the long-standing tech transparency problem, LILT’s AI-native architecture provides a unified data layer that tracks model evolution in real time. As a result, users gain full visibility into AI accuracy, efficiency, and quality improvements through enterprise-level dashboards and reporting.

Equipped with the ability to provide evidence-based ROI, leaders can prove that multilingual data is an appreciating asset, justifying the budget shift from fixing errors to expanding reach.

Native models offer greater control and security

If data privacy is a main concern, native models offer more control — whereas third-party integrations (like OpenAI via a TMS) do not. For high-stakes environments (like military or intelligence), LILT models can run on hardware that is physically disconnected from the internet. This effectively ensures data privacy and eliminates the risk of external hacking or data exfiltration.

Achieve a Scalable AI Win

Most Enterprise AI implementations fail because they rely on non-native AI, static training, and surface-level customizations. These “bolted-on” solutions create inaccuracies, delays, and operational opacity that cancel out the security, efficiency and ROI they promise, ultimately stunting business growth.

The difference between a failed AI initiative and a successful one comes down to the underlying models being used. LILT provides a different path: a true AI-native platform powered by adaptive, secure, and domain-specific models. By moving away from third-party dependencies, LILT customers notice a measurable quality and efficiency impact within weeks of deployment.

See LILT’s AI In Action.

“The AI model did all the heavy lifting and continues to do so, which is important for us running across so many countries and languages.” - Angus Cormie, Director & GM, EMEA at Lenovo

"We wanted a more automated, more streamlined solution where the Engine learns and improves right as we use it. This is what the LILT solution offered us.” - Loïc Dufresne de Virel, Head of Localization at Intel