Compact, fine-tuned models that run on your own hardware — matching frontier quality on scoped tasks at a fraction of the cost, with your data never leaving the building.
The reflex to reach for the biggest available model is understandable, but on a scoped task it is often the wrong call. A model in the one-to-eight-billion parameter range, fine-tuned on your own data and quantized to run on commodity hardware, frequently matches a frontier API on the specific job you care about — while costing a fraction as much and keeping every token inside your own walls.
Three forces make small models compelling right now: fine-tuning tooling has matured, quantization has become nearly lossless for many tasks, and hardware — even ordinary CPUs and small NPUs — has caught up. Together they move the crossover point where self-hosting beats per-token API pricing far lower than most teams assume.
For regulated data the argument is not really about cost at all. A model running on your own hardware never sends a customer record, a medical note or a piece of privileged information to a third party. That single property removes an entire category of compliance and vendor risk, and it is often the reason a project can happen at all.
We start by proving the small model can actually do the job. Before any long engagement, we fine-tune a candidate on a slice of your data and benchmark it head-to-head against the frontier model you would otherwise pay for. If it cannot close the gap, we tell you — and sometimes the honest answer is that a hybrid, routing the hard tail to a larger model, is the right design.
Once the approach is proven, we build the full pipeline: data preparation, fine-tuning, quantization and packaging, all reproducible so you can retrain as your data grows. We size the model to your actual hardware, whether that is a datacenter GPU, an ordinary server CPU or an embedded NPU in a device in the field.
You leave the engagement owning everything — the weights, the training code and the documentation — with no dependency on our infrastructure to keep running.
Assistants that run fully on a phone, laptop or embedded device, with no network round-trip and no data leaving the hardware.
Offline classification and extraction over sensitive documents, where sending text to an external API is not an option.
Autocomplete, drafting and rewriting that feels instant because inference happens locally.
Summarising medical, legal or financial records inside your own environment.
Compact models embedded in field devices, machinery and IoT hardware that must work without connectivity.
Cost-controlled triage and routing at volumes where per-token API pricing would be prohibitive.
We map the exact task, latency target and hardware budget, and agree what "good enough" means in numbers.
We assemble and clean the domain data the model will learn from, and set aside a fair evaluation hold-out.
We select a base model family and design the fine-tuning and quantization strategy for your constraints.
We fine-tune, quantize and package the model to run on your target hardware, with a reproducible pipeline.
We benchmark accuracy, latency and cost against both the target and a frontier baseline, honestly.
We hand over the weights, the pipeline and the documentation, and support the rollout to production.
We work with the open model families and tooling that give you portability and control, chosen to fit your task and hardware rather than a house favourite.
On a scoped task, a fine-tuned small model frequently matches a large general model. We benchmark both against your data before recommending an approach, so the decision rests on numbers, not opinion.
Yes. Quantized models run well on modern CPUs and NPUs. We size the model to your hardware budget, whether that is a server, a laptop or an embedded device.
You own the weights and the pipeline outright. There is no lock-in to our infrastructure, and you can retrain whenever your data changes.
A working prototype, benchmarked against a frontier baseline, typically lands within two to four weeks.
Then we say so. Often the right answer is a hybrid that handles the common case locally and routes the hard tail to a larger model.
Book a 30-minute call. We will tell you honestly whether we can help.
Book a call