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AI · Sub-service
Model Fine-tuning & Evaluation
Dataset curation, fine-tuning, and evaluation frameworks so model quality is engineered — not hoped for.
Summary
Dataset curation, fine-tuning, and evaluation frameworks so model quality is engineered — not hoped for.
Problem
What teams are stuck with
Teams swap models weekly without baselines, so nobody knows if quality improved.
Fine-tuning on noisy data embeds bad habits and policy violations.
Offerings
What we deliver
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01
Eval harnesses and scorecards
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02
Dataset labeling and curation
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03
Fine-tune and preference optimization
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04
Prompt versioning and A/B tests
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05
Safety and red-team checks
Approach
How we work it
Define task rubrics and gold sets before training.
Prefer RAG + tools first; fine-tune when style, domain language, or latency demands it.
Automate regression evals on every prompt or model change.
Outcomes
What good looks like
- Documented quality baselines
- Faster model iteration with less risk
- Clear go/no-go for production promotion
Related
Keep exploring
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