Most companies do not need a research lab. They need AI software that ships and keeps working once it is in production. That is the work we do.
We take a custom AI project from the first conversation to a running system: scope the problem, prove it with a quick prototype, then engineer it into reliable, monitored software inside your own stack.
What “custom” means
Off-the-shelf tools are good until your workflow, your data or your accuracy bar stops fitting their template. Custom means the system is built around your problem: your documents, your edge cases, your integrations, and your definition of correct.
It does not mean building everything from scratch. We use the best available models and components, and spend the custom effort where your business is different. When off-the-shelf is the better choice, we say so.
How an engagement runs
1. Discovery, week 0 to 1. We pick the use case with the most leverage, audit the data it depends on, and define success as a number: accuracy, hours saved, tickets deflected. If AI is the wrong tool for the job, this is where we tell you.
2. Prototype, weeks 1 to 4. A narrow, working system on your real data. The point is to retire the biggest risk early: can a model do this task at a usable quality? You either see it working, or you find out cheaply that it will not.
3. Production, weeks 5 to 12 and beyond. The prototype becomes software. Integration with your systems. A test harness that catches regressions. Guardrails against bad output. Monitoring for quality, latency and cost. A rollout with a way back.
4. Handover, or we stay on. Clean code, tests, documentation and a working session with your team. Some clients take the keys. Others keep us on for monitoring and improvement. Either way, it is your system.
What you get
A working AI system deployed in your cloud. The test suite that proves it works. Documentation your engineers will use. Every line of code and every model artefact under your ownership, with no licence back to us and no lock-in.
Why teams pick us
We are a small senior team, so the people who scope the project are the people who build it. We show working software every week rather than a status deck every month. And we build our own products, WeddingBestie, Pillpal and Policy Simulator among them, so the production lessons in your project were learned on our own users first.
Curious what this costs? Read the 2026 cost guide, or tell us what you are building. We reply within one working day.
Where this fits
Custom development and fine-tuning is usually the third step. Many teams start with an AI-native product build, add agents as workflows settle, then move to custom models. When privacy or latency demands it, the same models go on-device. It is the same team at every step.