What changes between sectors isn't the model. It's knowing which work is worth automating, which is regulated, and which should stay human.
Document-heavy processes under audit pressure. We automate extraction and review while keeping a defensible trail of every decision.
Typical wins: underwriting throughput, KYC onboarding, claims triage, reconciliation.
Administrative load is where the hours go. We target it without ever moving patient data somewhere it shouldn't be.
Typical wins: clinical documentation, intake, scheduling, coding support.
Planning against forecasts instead of last month's numbers, and reacting when reality moves mid-week.
Typical wins: demand forecasting, route optimisation, exception handling, supplier comms.
Support volume that spikes and never quite comes back down, and merchandising decisions made on stale data.
Typical wins: support resolution, product content, demand planning, personalisation.
Quality and maintenance data that already exists but never reaches the person who could act on it in time.
Typical wins: predictive maintenance, visual inspection, production scheduling.
Expertise that doesn't scale because it's locked in documents and in people's heads.
Typical wins: proposal drafting, research synthesis, knowledge retrieval, time capture.
Half our engagements begin in a sector we haven't worked in yet. Discovery exists precisely for that: we learn your process before we propose anything, and we'll tell you early if we're not the right team.
Tell us about your sector →We sit with your team and map how the work actually flows — not how the process document says it does.
You get an opportunity map: where AI pays off, what it would cost, and what we'd leave alone.
Bring your bottleneck. We'll tell you whether it's an AI problem or something else.