Data readiness
Before building, we assess what data exists, where it lives and what's missing. AI runs on context — fixing the data feed often unlocks more value than the model itself.
Process
Three steps. One operational system.
Initial systems are scoped and launched in phases, with timelines that depend on the complexity of the workflow and the readiness of your data.
01
Diagnose
We map the relevant workflow, qualify the bottleneck and agree on the highest-leverage system to build first. Fixed scope, written plan, clear success criteria.
Scoped plan and success criteria
02
Build
I implement the system end to end: data, model, automation, interface. Regular demos, working software at each step, your team involved as it grows.
Operational system in production
03
Operate & evolve
Optional ongoing partnership: monitoring, support and iteration as the business changes. Or a clean handover to your team with full documentation.
Owned, observable, evolving
Underlying pillars
What makes the process actually work.
Scope definition
Every engagement is scoped against a specific workflow with measurable success criteria. We agree on what 'done' looks like before any code is written.
Handover & documentation
You receive architecture diagrams, runbooks, source code, observability dashboards and recorded handover sessions. Your team can run, audit and evolve the system.
Support after launch
Optional retainer for monitoring, iteration and new use cases. Or a clean exit if your team is ready to own it fully.