We build small language models, agents, and the orchestration that keeps them running.
BrainField is an AI, machine learning and data engineering practice. We work with teams who already have data and now need something dependable on top of it.
A compact model trained on your own documents, tickets and product data. It runs on hardware you control, costs a fraction of a frontier API call, and does not leak anything to a vendor. We handle data preparation, fine-tuning, evaluation and the serving stack.
Fine-tuning, distillation, evaluation harnessesMost agent demos fall apart on the second turn. We design agents around real tools and real permissions, with the retries, guardrails and audit trail that let a business actually depend on the output.
Tool use, retrieval, guardrails, evaluationOne agent is a prototype. Twelve agents sharing state, budget and failure modes is a system. We build the routing, memory and observability layer so your team can debug it at 2am without calling us.
Multi-agent routing, state, tracing, cost controlFeatures, pipelines and warehouses that feed the models. Usually the least glamorous half of the work and the half that decides whether anything above it survives contact with production.
Feature stores, pipelines, warehouse modellingTwo weeks. We read the data, talk to the people using it, and come back with what is worth building and what is not. You keep the write-up either way.
Four to six weeks to a working thing on your data, measured against a metric you agreed to before we started.
Serving, monitoring, cost controls, rollback. The unglamorous months where a demo becomes something on call.
Your engineers own it. We document, pair, and step back. A good engagement ends with you not needing us.
Twenty years of shipping data systems, most of them before anyone called it AI.
BrainField is led by an engineer who has spent two decades building data and machine learning platforms for large retail and e-commerce organisations — feature stores serving live traffic, warehouse migrations, recommendation systems, and multi-agent analytics platforms on Google Cloud and AWS.
That history is why we tend to argue for the smaller model, the boring pipeline and the shorter dependency list. It is usually what is still running two years later.
Send a short note about the problem — the data you have, what you want it to do, and the deadline you are up against. You will get an answer from an engineer, not a sales team.
hello@brainfieldllc.com