AI, Tuned to Production.
Evaluate, tune, and serve the best LLMs for your business. If you can measure it, reinforcement learning can optimize it.
They needed a lot of help
NVIDIA loves Adaptive Engine!
Just testing and testing
Aïkan has developed Juribot—a chatbot to help customers answer questions about insurance documents. Off-the-shelf models like GPT-4o can hallucinate references to non-existent laws. These mistakes are costly, expose Aïkan to reputational risk, and increase time-to-resolution for support cases.
Adaptive ML tuned a Llama 3.1 8B model, reducing hallucinations by 25% over GPT-4o, and 42% over the base model. The LLM was trained using synthetic annotations, significantly reducing production time and costs.
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Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
AT&T has deployed Adaptive Engine as their reinforcement tuning platform, identifying 50+ use cases where fine-tuning will be required, ranging from text-to-SQL to customer support, call summarization, document RAG, and more.
During AT&T’s evaluation period of Adaptive Engine, a Llama 3.1 8B was fine-tuned to improve factuality and helpfulness on RAG for telco documents. The tuned model achieved a 51% win rate vs GPT-4o.
Using Adaptive Engine, SK Telecom tuned open models as small as Gemma 3 4B to exceed frontier performance at multilingual content moderation.
“Moderating content in Korean is a challenge where off-the-shelf APIs often fall short. We were impressed to find that using RL on a small, open 4B model unlocked a new level of precision - outperforming the largest proprietary models in both Korean and English.”
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
They needed a lot of help
NVIDIA loves Adaptive Engine!
Just testing and testing
Aïkan has developed Juribot—a chatbot to help customers answer questions about insurance documents. Off-the-shelf models like GPT-4o can hallucinate references to non-existent laws. These mistakes are costly, expose Aïkan to reputational risk, and increase time-to-resolution for support cases.
Adaptive ML tuned a Llama 3.1 8B model, reducing hallucinations by 25% over GPT-4o, and 42% over the base model. The LLM was trained using synthetic annotations, significantly reducing production time and costs.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
AT&T has deployed Adaptive Engine as their reinforcement tuning platform, identifying 50+ use cases where fine-tuning will be required, ranging from text-to-SQL to customer support, call summarization, document RAG, and more.
During AT&T’s evaluation period of Adaptive Engine, a Llama 3.1 8B was fine-tuned to improve factuality and helpfulness on RAG for telco documents. The tuned model achieved a 51% win rate vs GPT-4o.
Using Adaptive Engine, SK Telecom tuned open models as small as Gemma 3 4B to exceed frontier performance at multilingual content moderation.
“Moderating content in Korean is a challenge where off-the-shelf APIs often fall short. We were impressed to find that using RL on a small, open 4B model unlocked a new level of precision - outperforming the largest proprietary models in both Korean and English.”
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
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Adaptive Harmony is our in-house preference tuning stack: inference, training, and RL all under a unified codebase.
We are hiring exceptional talent across technical, product, and commercial roles in our New York and Paris offices.
Contact our team and book a demo of Adaptive Engine for your use case. Get to production faster, today.
