LLM Engineering & Fine-Tuning
Pick the right model, adapt it to your domain, and prove it works. We take language models from a promising demo to a system that holds up under production traffic, cost budgets and compliance review.
- Fine-tuning
- Model evaluation
- Structured outputs
- Cost optimisation
Most LLM projects fail after the demo
A prototype that works on ten hand-picked examples tells you almost nothing about behaviour on ten thousand real ones. We build the evaluation harness first, then engineer the model layer against it — so every prompt change, model upgrade or fine-tune is a measured improvement rather than a hopeful one.
Capabilities in this practice
Each of these ships as a working system integrated with what you already run — not a slide deck or a proof of concept that stalls at the pilot.
Model Selection & Benchmarking
We benchmark candidate models against your actual tasks and data — quality, latency, and cost per resolved request — instead of leaderboard scores that were never about your workload.
Fine-Tuning & Adaptation
LoRA and QLoRA adaptation on domain data, instruction tuning for house style and format, and preference tuning where output quality is subjective rather than binary.
Distillation & Quantisation
Move a proven workload from a frontier model to a smaller, cheaper one without losing accuracy — the single biggest lever on AI unit economics at volume.
Structured Output Engineering
Schema-constrained generation, function/tool definitions and validation layers so downstream systems get parseable, typed data every time — not prose that mostly looks like JSON.
Prompt Systems, Not Prompt Strings
Versioned prompt assets, template composition, few-shot selection and A/B infrastructure, so prompts are reviewed, tested and rolled back like any other code.
Evaluation Harnesses
Golden datasets, LLM-as-judge scoring, regression gates wired into CI, and dashboards that tell you the day quality drifts rather than the quarter.
A sequence built to de-risk, not to impress
We measure before we optimise and ship in slices, so you can stop, redirect or scale at any step with evidence rather than instinct.
- 1
Task decomposition
Break the business outcome into discrete model tasks, each with a measurable success criterion.
- 2
Baseline & eval set
Build a golden dataset from real examples and establish a baseline score before changing anything.
- 3
Model & prompt iteration
Sweep models, prompts and retrieval settings against the eval set; keep only what moves the number.
- 4
Fine-tune where it pays
Adapt a smaller model once the task is stable and the data exists to justify it.
- 5
Harden & ship
Fallbacks, timeouts, caching, cost guards and observability before a single user touches it.
- 6
Monitor & re-tune
Track live quality and drift; feed real failures back into the eval set on a schedule.
What we have shipped on this stack
We build our own products on this stack. When we recommend an approach, it is one we already run in production and pay the bills for.
The things clients ask before signing
Start with retrieval and prompt engineering — they are faster to iterate and easier to correct. Fine-tuning earns its place when you need a consistent format or tone, when you want to move a stable task to a cheaper model, or when domain language is genuinely outside the base model’s distribution. We measure both against the same eval set before recommending either.
Yes. We deploy open-weight models such as Llama, Mistral and Qwen on your VPC or on-prem GPUs using vLLM or comparable serving stacks, which is often a requirement in regulated environments where data cannot leave your boundary.
Model right-sizing per task, semantic and exact-match caching, prompt compression, batching, and routing easy requests to smaller models while escalating only the hard ones. We instrument cost per request from day one so the number is visible before it becomes a problem.
A golden dataset built from your real inputs, task-specific scorers, LLM-as-judge for subjective dimensions, and human review on a sampled slice. Those scores run in CI, so a regression blocks a release the same way a failing unit test would.
Ready to put this into production?
Tell us the problem you are trying to solve. We will tell you honestly whether AI is the right tool for it, and what it would take to ship.