Software & AI
We build the application and the intelligence in the same codebase, under the same tests. The model is a component with a measured error rate, not a feature with a launch date.
Custom applications and the models inside them. Every model ships with an evaluation set, a confidence threshold and a human-review route, because a prediction nobody can check is a liability.
What you get
- An application built on your data model, with tests and a deployment pipeline
- An evaluation set for every model, written before the model ships
- A stated confidence threshold, and a human-review route for anything below it
- Provenance on every model output: model, version, confidence and timestamp
- A documented cost per thousand inferences, measured rather than estimated
How it works
Find the measurable task
We look for work that is repetitive, currently manual and countable. If we cannot count it, we cannot show the model helped.
Build the evaluation first
A labelled set drawn from your real data, agreed with the people who do the task today.
Ship behind a threshold
The model runs in production on the confident cases; everything else routes to a person.
Widen on evidence
The threshold moves only when the measured error rate says it can.
Reference architecture — source systems write once into a single modelled record; every surface reads from it.
We will not deploy a model into a decision that affects a person’s money, employment or health without a human in the loop. That is not negotiable on our side.
Asked before you ask
Whichever the evaluation favours for the task, including a non-model baseline. A regular expression that solves 80% of a problem is a better answer than a language model that solves 82% at a hundred times the cost, and we will tell you when that is the case.
Only if you decide it does. We state which processor sees what before anything is built, and we can run open-weight models inside your own infrastructure where the data or the jurisdiction requires it. That choice changes cost and latency, and we quantify both.
It is designed to be wrong sometimes, which is why every deployment has a review route and an audit trail. Errors are visible, attributable and correctable, and the correction feeds the next evaluation set.
Tell us what is not working
Describe the system you are running on now. We will tell you what we would change, what it would cost and how long it would take.