The approach
A model that works in a notebook is a demo. We take the problem from framing through to a deployed, monitored service. We'll also tell you early if machine learning is the wrong tool here, which happens more often than you'd think.
What you get out of it
- A clear baseline before any model is trained
- Deployed inference with measured latency and cost
- Drift monitoring so quality doesn't decay silently
Inference pipeline
What you receive
- Feasibility assessment
- Training pipeline
- Deployed inference service
- Evaluation & monitoring suite
Typical stack
Indicative, not fixed. We pick for what your team can maintain after handover.
Every two weeks, something you can open.
Six phases. Each one ends in working software, in an environment you can log into and judge for yourself. Never a status report as the only evidence.
Discover
We map the problem, the constraints and the people. You leave with an architecture direction, a scope and an estimate you can hold us to.
Design
Flows, interfaces and data models get settled while they are still cheap to change. Prototypes meet real users before anyone writes code.
Build
Two-week increments, each ending in a demo. Working software in an environment you can log into. Not a status report.
Harden
Load testing, security review, accessibility pass, and the failure cases nobody enjoys writing. This is the step most projects skip.
Launch
Staged rollout, monitoring live, rollback tested in advance. Someone is watching the graphs on the day.
Evolve
Support under an agreed SLA, and a next round driven by what usage data actually shows.
More in AI & Data
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Computer Vision
Inspection, detection and OCR that run where the work happens.
Intelligent Process Automation
Remove the manual step, keep the audit trail.
Need ai & machine learning?
Send over the problem and any constraints you already know about. We will come back with an approach, a rough shape of the work and an honest view on feasibility.