Custom LLM Fine-Tuning & Security Pipelines

Why This Matters

Prompt engineering gets you 80% of the way. The last 20% — consistent domain vocabulary, proprietary reasoning patterns, strict output formatting, brand tone — requires fine-tuning. A fine-tuned model is also faster and cheaper to run at scale than heavy system prompts.

What Teams Use This For

• Customer support bot trained on your ticket history
• Legal document drafting model with your firm's style and clause library
• Medical coding assistant trained on ICD/CPT nomenclature
• Financial analyst model trained on your research methodology
• Code generation model for your internal framework and conventions

What You Receive

• Training data curation and cleaning pipeline
• Fine-tuning via OpenAI API, Together AI, or Hugging Face
• Before/after eval report (accuracy, consistency, latency)
• Guardrail layer (refusals, scope enforcement, PII redaction)
• Deployment-ready model endpoint with authentication

Project Packages

Fixed-price scopes with clear deliverables. No hourly billing surprises. Source code ownership transferred on all packages.

Stack Used

Proof of Delivery

Built production AI systems on Claude API, OpenAI, and open-weight models. QChat's multi-tenant architecture handles per-client model configuration. Fine-tuning projects include training data pipeline design, eval harness, and guardrail implementation.