AI engineering
Custom AI development
Software built around how your business already works, with the intelligence designed in from the start rather than added to a finished product afterwards.
What this is
The work, described plainly
Most AI projects fail at the seams, not at the model. The model returns something reasonable and then nobody knows where the output goes, who checks it, what happens when it is wrong, or how it reaches the system that needs the answer.
We start from the process, not the model. What decision is being made, who makes it today, what they look at to make it, and what it costs when it goes wrong. That determines whether the answer is a language model, a classifier, a set of rules, or a better query against data you already have — and often it is not a model at all.
What gets built is a system: the data path in, the inference step, the checks around it, the place a human signs off, and the write back into whatever you already run.
What you get
What is actually handed over
Concrete deliverables, not phases on a timeline.
A written architecture
Data flow, components, failure modes, and the decisions we made with the reasons attached — before anything is built.
The running system
Deployed, monitored, and integrated with the tools your team already uses, not a demo on a laptop.
Evaluation you can repeat
A test set drawn from your real cases, so a change to a prompt or a model can be measured instead of argued about.
Handover documentation
How it works, how to operate it, what to do when a component fails, and where the switches are.
Is this you
Signals this is the right fit
- You have a process that works but does not scale with headcount.
- Your data sits in systems that do not talk to each other.
- You tried an off-the-shelf AI tool and it did not fit how you work.
- You need the system to keep running when a model provider changes.
Typical stack
Chosen per project against your constraints, not applied by default. This is what the work usually involves.
- TypeScript
- Python
- Next.js
- PostgreSQL / MySQL
- Claude, GPT, and open-weight models
- Docker
FAQ
Questions people actually ask
Do we need our own data to start?
Not always. Some systems work from documents and rules you already have. Others need historical examples to be useful. We tell you which one you are looking at during scoping, before you commit to a build.
What if a better model comes out mid-project?
Model access sits behind an interface, so swapping providers is a configuration change rather than a rewrite. We design it that way on every project, because the model you start on is rarely the model you finish on.
Can it run without sending data to a third party?
Yes. Open-weight models can run on your own infrastructure. It costs more to host and the quality ceiling is lower than the largest hosted models, so we will tell you honestly whether your use case can afford that trade.
Start here
Tell us what is slow, manual, or breaking.
Answer a few questions and get a written brief back — scope, proposed architecture, and what it would take to build.