Our approach
AI is a capability layer.
Not a project.
AI fails not because of models. It fails because organisations don't know where it belongs. Our job is to place it — deliberately, safely, and measurably.
01 · The problem we see
Every enterprise has an AI strategy.
Almost none have an AI place.
- Symptom 01
Priority, but unclear
AI on every board deck. Roadmap vague. Owners unnamed.
Diagnose - Symptom 02
Systems won't move
Ten-year-old systems running the business. AI asked to sit somewhere — nobody says where.
Map placements - Symptom 03
Disconnected pilots
Three teams. Three vendors. Three POCs. None compound.
Operating model - Symptom 04
No ownership
Who owns the eval? The drift? The off-switch? Orphans don't survive audits.
Embed delivery
02 · Our insight
AI is a layer that sits across your operation —
not a product inside it.
Four surfaces: decisions, systems, data, operations. Miss one — the layer falls over.
03 · Principles
Four commitments. Non-negotiable.
- 01 / No hype
We don't sell AI. We sell placement.
If an LLM isn't the right tool, we say so. The ceremony of AI is not our product.
- 02 / Systems-first
The system comes first.
Map the stack before the model. Legacy is a constraint, not a failure. No rip-and-replace.
- 03 / Governance-first
Owned on day one.
Every placement ships with a named owner, review cadence, eval harness — or it doesn't go live.
- 04 / Long embed
We stay past go-live.
Through production, through drift, through the second and third placement — until you own it.
04 · How it plays out
DATS — five stages, one system.
Discover → Diagnose → Operating model → Pilot to Production → Scale & Run.
From the blog