LLM Fine-Tuning & Structured Output
Your off-the-shelf AI isn't good enough. We fine-tune models to your exact domain.
The Problem
Generic LLMs produce generic results. They hallucinate, miss domain-specific nuance, and can't reliably output the structured formats your systems need. API-only approaches lock you into vendor pricing that scales linearly with usage.
Our Approach
We fine-tune open-source models — Qwen, Llama, Mistral, and others — to understand your domain and produce structured, reliable output. This gives you better accuracy than a generic API at a fraction of the ongoing cost.
The process:
- Data curation — We work with your existing data to build high-quality training datasets that teach the model your domain's language, rules, and edge cases.
- Fine-tuning — Model training with careful evaluation against benchmarks that matter to your business, not just academic metrics.
- Structured output — Models trained to produce JSON, XML, or whatever format your downstream systems consume — reliably and consistently.
- Deployment — Production deployment on AWS via SageMaker endpoints, with monitoring and cost optimization built in.
Experience
We've fine-tuned LLMs for structured output generation in production systems, built RAG pipelines with vector search for enterprise SaaS platforms, and implemented generative AI across data enrichment workflows processing high-volume data streams.