We design, train and run custom language models for enterprises: measurably more accurate on your work than off-the-shelf AI, a fraction of the cost to run, and owned entirely by you.
custom models shipped to production for enterprise clients
median reduction in inference cost versus the API it replaced
median accuracy gain over the untuned base model, on held-out data
of clients commission a second model within a year
Most companies don’t need another platform. They need a model that works, evidence that it works, and a way to keep it working. We provide all three as one accountable team.
Supervised fine-tuning, preference optimization and reinforcement learning on your data, taking a base model from generalist to specialist in your task. One accountable engagement from data to production.
Move the behaviour of a frontier model into one small enough to run on a single GPU, inside your firewall.
Task-specific test suites, calibrated automated judges and release gates your risk committee can review.
RLHF, DPO and verifier-based RL that teach a model your experts’ judgement, not just their formats.
Serving, monitoring and scheduled retraining, so quality holds after launch.
On a narrow task you run a million times a month, a model trained on your own labels wins on accuracy, cost, latency and control. And the lead grows with every retraining cycle, because it learns from data nobody else has.
Read our open experiment: a 0.5B model from 19% to 97% exact match on unseen formats →
Field-level exact match, 2,400 claims, two-adjuster adjudication. Costs at list price.
The success metric goes into the contract before we touch a GPU. If the feasibility read says fine-tuning is the wrong tool, we tell you, and you keep the evaluation suite.
Names withheld under NDA. Every figure is measured on a frozen held-out set the client approved before training.
Fine-tuned on 61,000 analyst dispositions and aligned on 9,000 disagreement pairs. Passed model-risk validation first time.
Distilled from a frontier teacher into a 7B model and tuned on coder feedback. 92.3% top-3 accuracy.
RL against a tool-call verifier and an escalation reward model. CSAT up 11 points.
Set your current volume. The estimate uses the cost model from our break-even research; your briefing gets a precise number.
Assumes a fine-tuned 8B model on dedicated GPUs at 35% average utilisation, including operations and quarterly retraining.
We had spent nine months on prompt engineering. They delivered a smaller model that was better on every metric we cared about, and the evaluation harness to prove it to our risk committee.
We publish the experiments and cost models behind our recommendations, with code, so your team can check our reasoning.
Eight training runs on a 0.5B model, in- and out-of-distribution.
Code and results included CalculatorFull, LoRA and QLoRA, from real model configs.
0.5B to 72B SimulationPower analysis, paired vs unpaired.
Monte Carlo DerivationWith a tabular simulation.
Math + code SimulationAnd the 1978 estimator that fixes it.
StatisticsCan’t find your question? Ask a senior engineer.
When the task is narrow, repeated at volume and has a definable notion of correct: extraction, classification, routing, structured drafting, tool-calling agents. In those cases a fine-tuned 3–14B model routinely matches or beats a prompted frontier model at a fraction of the cost and latency. When 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 configuration, data lineage and evaluation harness transferred to you. No runtime dependency on us and no per-token royalty.
It doesn’t have to. We train inside your VPC on AWS, Google Cloud, Azure or Oracle Cloud, on your Databricks workspace, or on-premises. See Security.
It depends on the task, how much labelled data already exists, the training methods the evaluation calls for and where the model must run. We scope every engagement against a written success metric and send a tailored proposal after a briefing; talk to us to get one. GPU compute is passed through at cost, and the underlying economics are public in our compute cost model.
Any open-weight family with a suitable licence (Llama, Qwen, Mistral, Gemma, Phi, DeepSeek and others) from 0.5B to 400B+ parameters, plus hosted fine-tuning APIs when that is the right call. We choose by eval bake-off, not habit.
Median six and a half weeks from kickoff to production traffic. A feasibility sprint answers “will this work, and by how much?” in two weeks.
A 45-minute briefing with a senior research engineer. You leave with a feasibility read, a data plan and a scoped proposal, whether or not you hire us.
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