We fine-tune, align and distill open-weight language models on your data — then hand over the weights, the evals and the pipeline to keep improving them. Enterprise LLM fine-tuning, delivered as a fixed-scope engagement.
custom models shipped to production since our first engagement
median reduction in inference cost versus the frontier API it replaced
median task-accuracy gain over the untuned base model on held-out data
median from kickoff to the model serving real traffic
Most teams don’t need another platform. They need a model that works, an eval that proves it, and a pipeline that keeps it working. That is the job.
Full-parameter and LoRA/QLoRA fine-tuning on curated demonstrations. Teaches format, tone, domain vocabulary and task procedure.
02Preference optimization and reinforcement learning against verifiers, reward models and expert raters — for judgment, not just format.
03Move frontier-model behavior into a model small enough to run on one GPU, at the edge, or inside your firewall.
04Task-specific eval suites, calibrated LLM judges, regression gates and human review — the instrument panel for every decision.
05Mining your logs and documents, expert annotation, preference collection and synthetic data with contamination controls.
06Quantization, serving on vLLM/TensorRT-LLM/SGLang, canaries, drift monitoring and scheduled retraining.
Frontier APIs are extraordinary generalists. But on a narrow task you run a million times a month, a specialist trained on your own labels wins on accuracy, cost, latency and control — and the margin compounds with every retraining cycle.
| Insurance claims extraction | Accuracy | $ / 1k claims | p50 |
|---|---|---|---|
| Frontier API, zero-shot | 81.4% | $4.80 | 2.1 s |
| Frontier API, 12-shot + RAG | 86.9% | $11.20 | 3.4 s |
| Open 70B, prompted | 78.2% | $2.10 | 1.6 s |
| Open 8B, untuned base | 72.8% | $0.34 | 0.2 s |
| 8B fine-tuned (SFT → DPO) | 94.7% | $0.31 | 0.18 s |
We write the success metric into the contract before we touch a GPU. If the feasibility sprint says fine-tuning is the wrong tool, we tell you — and you keep the eval suite.
Define the task, the metric and the bar the model must clear to ship.
Mine logs, write guidelines, run expert annotation, generate and filter synthetic data.
Base-model bake-off, SFT, then preference or RL rounds against the eval.
Held-out evals, red-teaming, regression gates, side-by-side human review.
Quantize, deploy, canary, monitor drift, retrain on fresh data each cycle.
Move the sliders to your current volume. Estimates use our median results across production engagements; your briefing gets a real number.
Assumes a fine-tuned 8B model on dedicated GPUs at 70% utilisation, including serving overhead and a quarterly retraining cycle.
A 14B model fine-tuned on 61,000 analyst dispositions and aligned with DPO on 9,000 disagreement pairs. Runs fully on-prem behind the bank’s model-risk controls.
Distilled from a frontier teacher into a 7B student, then tuned against coder feedback.
RL against a verifier for tool-call correctness and a reward model for escalation judgment.
We had spent nine months prompt-engineering. They gave us a smaller model that was better on every metric we cared about, and the eval harness to prove it.
When the task is narrow, repeated at volume, and has a definable notion of “correct”: extraction, classification, routing, structured generation, domain-specific drafting, tool-calling agents. In those cases a fine-tuned 3–14B model routinely matches or beats a prompted frontier model at 5–20× lower cost and 3–6× lower latency. When the task is open-ended or the knowledge changes daily, start with retrieval. Our fine-tuning vs RAG guide walks through the decision.
You do. Every engagement ends with the trained weights, adapters, training configs, data lineage and the evaluation harness transferred to you. No runtime lock-in, no per-token royalty.
It doesn’t have to. We train inside your VPC on AWS, Google Cloud, Azure or Oracle Cloud, on Databricks or Snowflake-adjacent GPU capacity, or on-premises. When you’d rather we host training, data sits in single-tenant, encrypted storage that is destroyed at handover. See Security.
Fixed-price engagements start at $24,000 for a two-week feasibility sprint. A production model typically lands between $95,000 and $180,000 including evaluation and deployment. GPU compute is passed through at cost. The full breakdown is on Pricing and in our cost guide.
Any open-weight family with a permissive licence — Llama, Qwen, Mistral, Gemma, Phi, DeepSeek and others — from 1B to 400B+ parameters, plus hosted fine-tuning APIs from the major model providers when that is the right call. We pick the base model with an eval bake-off, not by habit.
Median six and a half weeks from kickoff to production traffic. A feasibility sprint answers “will this work?” in two weeks, with a working checkpoint and a baseline-vs-tuned scorecard.
A 45-minute technical briefing with a senior research engineer. You leave with a feasibility read, a data plan and a fixed quote — whether or not you hire us.
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