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Off-the-shelf LLMs aren't built for your domain. Our LLM fine-tuning services help enterprises, researchers, and startups train custom AI models on proprietary data — delivering higher accuracy, lower hallucination, and production-ready performance tailored to your use case

You shipped the demo. It looked great. The retrieval worked. The model responded fluently. The investors nodded. The Slack channel celebrated. Then you deployed to production. Within 30 days, your support queue filled with complaints. The model was confidently wrong. It hallucinated facts that were nowhere in your documents. It ignored your output format half the time. It worked fine on the test queries and broke on real user inputs. Your inference bill was 3x the estimate. A

If you are building an LLM-powered product in 2026, writing code or integrating an API is the easy part. The hard part is everything that comes after: How do you know your model actually works on your domain? How do you prove it improved after fine-tuning? How do you stop it from hallucinating in production? How do you align its behavior to what your users expect? These are not product questions. They are LLM training research questions — and most engineering teams do not hav