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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.
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.”
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.
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.
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.
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.
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.”
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.
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.
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.
They needed a lot of help
NVIDIA loves Adaptive Engine!
Just testing and testing
Adaptive Engine: The RLOps platform for Industrialized Intelligence
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Adaptive Harmony is our in-house preference tuning stack: inference, training, and RL all under a unified codebase. Adaptive Harmony is our in-house RL infrastructure: inference, training, and reinforcement learning under a unified codebase, built by the architects behind some of the most widely used open-source models.
We're building the industrialized intelligence layer for enterprise, and we're hiring exceptional engineers, researchers, and commercial operators to join the team, in New York and Paris.
Ready to transform your Enterprise AI into an industrialized intelligence asset? Book a demo and see how Adaptive Engine bridges the last mile from prototype to autonomous production.
