LLM fine-tuning & training
We fine-tune and train language models for US companies: data preparation, training, evaluation, and deployment — for the cases where prompting and RAG genuinely aren't enough. We'll also tell you when you don't need it.
No sales script. You talk to the engineers who'd build it.
Our team works a shifted day so you get real-time standups and same-day turnarounds in your time zone, not next-morning replies.
Every line of code, model weight, and prompt is yours from day one. NDAs and clean IP assignment are standard, not an upsell.
You work directly with the engineers building your system. No account managers sitting between you and the people writing code.
We move from scoping to a working system in production in weeks. Most engagements ship something usable inside the first month.
What we build
Concrete systems we ship, tuned to your data and your stack.
Data preparation
Build and clean the training set — usually the part that decides whether fine-tuning works at all.
Fine-tuning & LoRA
Efficient tuning of open and hosted models for your domain, style, or task.
Evaluation
Benchmark the tuned model against the base so you know it actually improved, on your data.
Deployment
Serve the model reliably and cost-effectively, hosted or self-hosted, wherever it needs to run.
How we work
Scope & evals
We pin down what success means and build the evaluation set before writing the feature, so quality is measured, not guessed.
Build in the open
Weekly demos against real data. You see progress every week and can change direction before it gets expensive.
Ship & instrument
We deploy with logging, cost tracking, and guardrails in place, then tune against production traffic.
Hand off or stay
Take the keys with full docs, or keep us on for iteration. Either way you're never locked in.
Questions, answered
Do we actually need fine-tuning?
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Often not, and we'll say so. Prompting plus RAG solves most problems faster and cheaper. Fine-tuning earns its place for consistent formatting, a specific voice, or narrow-task accuracy that prompting can't reach.
How much data do we need?
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Less than people expect for style and format tasks — sometimes a few hundred good examples. We focus on data quality over quantity, since that's what moves the result.
Can you fine-tune open-source models we self-host?
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Yes. We fine-tune Llama, Mistral, and similar models and deploy them in your infrastructure when privacy, cost, or control rules out hosted APIs.
How do you know the tuned model is better?
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We benchmark it against the base model on a held-out set from your data and report the difference. If it doesn't beat good prompting, we tell you rather than bill you for it.
Let's scope your build.
Tell us what you're trying to ship. We'll tell you honestly whether AI is the right tool and what it would take.
Start the conversation