A model tuned to your data, not a generic one
Fine-tuning, evaluation, and training pipelines for teams that need a model that performs on their specific domain, data, and constraints — plus the consulting to know when fine-tuning is (and isn't) the right call.
The problem
What this solves
General-purpose models plateau on domain-specific tasks, and most teams don't have the ML infrastructure or expertise to fine-tune, evaluate, and safely deploy a custom model.
Deliverables
What's included
- Dataset preparation and curation for fine-tuning or evaluation
- Fine-tuning small/mid-size open-weight models on your domain data
- Rigorous evaluation harnesses (not just vibes) to prove the tune actually improved results
- Training infrastructure setup (single-GPU to multi-GPU, cost-aware)
- Consulting on model selection, fine-tune vs. RAG vs. prompting trade-offs
Process
How it works
Audit
Assess whether fine-tuning is actually the right lever versus RAG or better prompting.
Curate data
Prepare a clean, representative dataset — the single biggest factor in tune quality.
Train & evaluate
Run the fine-tune with a held-out evaluation set and clear success metrics.
Deploy
Ship the tuned model behind the same production-grade serving layer as any AI feature.
Tools & stack
What we use
Related work
Case studies
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FAQ
Common questions
Available for new projects
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