Demand, load, throughput, fault. Calibrated models with intervals operators can act on.
Capability
AI / ML Decision Systems
A model is not a product. We build the systems around the model — the data pipelines feeding it, the guardrails containing it, the monitoring catching its drift, the operator interface accepting or overriding its recommendations. The decision is the unit of work, not the prediction.
Components
What we build.
Each engagement assembles a different combination of these components, but the parts are stable.
Routing, dispatching, scheduling, allocation — at the speed the operation needs.
Signals worth waking someone for. Designed against false-positive fatigue.
Where the model writes back. Carefully — with override paths, audit trails, and rollback.
Stack
Tech we deploy with.
The list is descriptive, not prescriptive — the stack meets the operation.
Python PyTorch scikit-learn XGBoost MLflow BentoML Ray Postgres + pgvector
Detail
How it deploys.
The model ships last, not first. We build the pipeline feeding it, the guardrails containing it, and the operator interface accepting or overriding it before a single prediction reaches production. The decision is the unit of work — the prediction is just one input.
It earns autonomy in stages. Recommendations run in shadow mode first, then assist the operator, then — only where it’s warranted — close the loop with override paths, audit trails, and rollback. We never start with the model in control.
We instrument the worst case, not the average. Drift detection, calibration checks, and false-positive budgets are part of the build, not an afterthought. A decision system is judged by the worst decision it makes — so that is what we measure.
Domains
Where we apply this.
Engage
Start a project around decision systems.
Tell us the operational gap. We'll respond with the shape of the engagement within one business day.
Other capabilities