Oddly AutomatedAI Model Training & Fine-tuning
Domain-specific models and enterprise RAG on your private data - measured with real evals, not vibes.
Who this is for
Companies with proprietary knowledge - documents, statutes, support histories, product data - that generic models answer poorly or unsafely, and where wrong answers carry real cost.
What we build
- Enterprise RAG on private knowledge bases with precise source citations and low hallucination rates.
- Fine-tuning and instruction-tuning of open models for your domain and tone.
- Local / self-hosted model isolation for zero-leakage, EU-resident data handling.
- Multi-model orchestration with fallback routing to balance quality, latency and cost.
How we measure quality
Every deployment ships with an evaluation suite: accuracy on a labelled test set, citation correctness, latency, and cost-per-inference. We optimize against those numbers rather than one-off demos, so quality is provable and regressions are caught automatically.