AI product development: software people trust with real work
We design and build the AI software, the tests that prove it works, and the controls that let your team run it after we leave.
The demo worked. Everyone was impressed. It is still not in front of a customer, and nobody can quite say what is missing.
the demo that never shipped
A demo only has to work once, in front of a friendly audience, on an example somebody chose. A product has to work on the ugly input, the empty state, the customer who pastes in a whole email thread, and the Tuesday when the provider is slow. The distance between those two things is not model quality. It is everything around the model.
what these products usually do
- Draft a document, a reply, or a report that a person then edits and sends
- Read an incoming file and pull out the fields another system needs
- Answer a question from your own approved material, with the source shown
- Rank or route a queue so the hard cases reach the right person first
how we measure trust before release
- A test set drawn from your real cases, including the ones that went badly
- An agreed threshold the system must clear before anyone outside the team sees it
- A written list of failure modes, each with the behaviour we expect when it happens
- Cost and response time measured per task, not averaged into meaninglessness
where these systems fail
- Confident answers that are wrong, with nothing on screen to signal doubt
- Quality that drifts after a provider updates a model nobody pinned
- A workflow that assumes the happy path and strands anyone who leaves it
- Review queues that nobody has time to read, so nobody reads them
when this fits
A workflow is agreed, a person owns it, and the remaining value depends on the thing being built properly.
Nobody has decided which workflow matters yet. Answering that in consulting first is the cheaper order.
what you get
- The repository, the infrastructure definitions, and the deployment path
- The evaluation set and the thresholds, so quality is still measurable without us
- A runbook covering what to do when quality drops or a provider fails
- The prompts and retrieval configuration, documented rather than buried
the build we will not take
We will not ship a system that makes a decision about a person with no route for that person to reach a human. If the workflow cannot carry an appeal, it is not ready to be automated.
what people ask before starting ai product development
Can you work alongside our own engineers?
Yes. We can own a stream end to end, pair with your team so the knowledge stays in-house, or build the first version and hand it over.
How do you pick a model?
Against your task, using your test set and your constraints on cost, latency, and data handling. Public leaderboards do not know what your workflow needs.
What happens when the model providers change everything again?
The provider sits behind an interface we control, and the evaluation set tells you whether a swap actually helped. That is the point of building both.
What does a build cost?
Price follows scope, and scope follows the first slice we agree together. You get a written price for a defined stage rather than an open-ended rate.
Tell us which workflow is slow, and who does it today.