Every engagement ships these as concrete artifacts you own — not slides, not hand-waving.
Curated training pairs (typically 1k–10k examples). Built from your historical data, augmented synthetically where needed, audited for leakage and label noise.
LoRA adapters or full fine-tunes on Llama 3.3 / Mistral / Qwen 3 / Phi-4 — whichever fits your latency, cost, and licence constraints.
Held-out test set with task-specific metrics (BLEU / ROUGE / exact-match / custom rubrics) and a frontier-model judge for open-ended outputs.
vLLM / TGI serving config, autoscaling rules, drift monitoring, and the playbook for rolling back to the prior checkpoint.
We review your problem and tell you honestly if RAG / prompting beats fine-tuning. If yes, no fine-tune happens.
Mine your historical data, augment with synthetic pairs if needed, label, audit. Quality of dataset > quantity, every time.
Iterate on base model, LoRA rank, learning rate, epochs. Daily eval scores, leaderboard against the un-tuned baseline.
vLLM serving on your VPC, traffic shadowing, gradual rollout, drift dashboards.
Best-in-class where it matters; boring and battle-tested everywhere else.
Cost depends on base-model choice, dataset size, training-run count, and serving setup. Compute is passthrough at cost. Multiple-model engagements priced as a pod retainer.
When you've squeezed RAG and prompting and still need: (a) consistent output format at scale, (b) >10× cost reduction, (c) sub-100ms latency, or (d) on-prem deployment with no API calls. Otherwise, don't.
No. Training happens in your cloud or our isolated environment. Weights, datasets, and adapters are yours and contractually never re-used.
LoRA for 9 out of 10 cases — cheaper, faster, easier to manage multiple variants. Full fine-tune only when LoRA caps out on quality (rare).
Often surprisingly small. We've shipped 7B fine-tunes that outperform GPT-4-class on narrow tasks. Smaller = cheaper inference forever.