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Custom Model Fine-Tuning & Adaptation
We apply AI to the full SDLC to ship faster with fewer defects - AI-assisted coding and reviews, auto-generated unit/integration tests, flaky-test reduction, and intelligent QA pipelines.
Our Approach
We tailor LLMs to your domain—so they speak your terminology, follow your policies, and perform on your data. We handle the full pipeline: deciding when RAG is enough vs. fine-tuning, curating/cleaning datasets, choosing the right technique (LoRA/QLoRA, full-finetune, instruction tuning, preference tuning like DPO/ORPO, or distillation), running evals and red-teaming, and deploying efficient, compliant models on your cloud. Model-agnostic across open-source and API-hosted families; strict data privacy and governance included.
What you get
- RAG vs. fine-tune decision
- Data specification, collection, and cleaning scripts
- Training plan and configurations
- Domain instruction set and synthetic data recipes (if needed)
- Evaluation suite and report (accuracy, hallucination, safety, latency, cost) vs. baselines
- Safety and guardrail tests (toxicity, policy adherence), red-team findings and fixes