Evolution AI for the production estate
AI products in production keep paying back their run cost. We start from an inventory of the live estate, then industrialise what works and retire what does not.
The pilot worked. Two years later, nobody can say if it still does.
Organisations that got past the pilot stage inherit a different problem. The estate of AI products grows one use case at a time, each with its own run cost, its own model drift and no shared view of which ones still produce what they promised.
We put the estate under management. Every product is measured against the business case that justified it: what clears the bar gets industrialised with monitoring, evaluation sets and a named owner, and what does not gets fixed or retired. The decision is no longer how much more AI to build, but which of the products you already run has earned the next round of budget.
From live estate to the next decision
A loop that never closes: everything in production is watched, judged and acted on.
Inventory
Every AI product in production, with its cost and its owner
Measure
Performance and value against the case that justified it
Industrialize
What works moved onto shared standards and platform
Retire
What does not earn its place shut down deliberately
What the engagement includes
Every product in the estate inventoried, scored against its business case, then industrialised, remediated or retired. Not for organisations still on their first pilot. This work needs AI products already running in production with real users.
AI estate inventory
Every AI product in production, with owner, cost, usage and the case behind it.
DeliverableAI estate inventory
Value assessment
Performance and business value measured against the baseline that justified each product.
DeliverableValue assessment per product
Industrialization
The products that earn it moved onto shared standards, platform and governance.
DeliverableIndustrialized products
Portfolio decisions
Scale, refresh, rebuild or retire, decided with evidence and recorded.
DeliverablePortfolio decision log
Where this has already been done
An AI-assisted platform that keeps evolving with the clinical need
- 01
The challenge
Rare disease diagnosis, where the analysis is complex and the cost of a wrong path is high.
- 02
What we did
We built Dx29, an AI-assisted platform that supports the analysis and diagnosis process for clinicians.
- 03
Outcome
A platform in use that continues to evolve as the clinical need does.
Proven across enterprise scenarios
Evolve the estate you already run
A half-day session plus access to your run costs, and you leave with the inventory and a ranked list of what to industrialise, fix or retire.
Book the estate review