Industries we serve
Your sector decides what we build first.
A hospital and a SaaS company both want AI. One needs data that never leaves the building; the other needs inference that does not outrun revenue. We start from the constraint, not the catalogue.
- 7
- Sectors with a dedicated practice
- 120+
- Projects delivered
- 5
- Countries served
- 10 yrs
- Building and running systems
9 sectors
Regulated data
Healthcare
Private AI and compliant data platforms for hospitals, diagnostics chains and health-tech — without patient records leaving your environment.
Product teams
SaaS
Ship the AI feature your roadmap promised, on infrastructure where the cost per customer goes down as you grow, not up.
Regulated data
Financial Services
Auditable AI for lenders, brokers and insurers — where every decision has to be explainable to a regulator, not just accurate.
Operations-heavy
Manufacturing
Vision inspection, predictive maintenance and edge inference that keeps running when the plant network does not.
Operations-heavy
Engineering
Design-data search, drawing intelligence and simulation compute for engineering, EPC and construction-tech firms.
Product teams
Technology & AI
For teams building AI products: the infrastructure, evaluation and security work that sits underneath the model.
Operations-heavy
Enterprise
Consolidate a sprawling estate, automate the back office and give every department one AI platform instead of nine pilots.
Operations-heavy
Logistics
Route optimisation, document automation and fleet telemetry. Practice page in build.
Regulated data
Energy & Utilities
Grid analytics, asset inspection and forecasting. Practice page in build.
Different sectors, the same questions.
Whatever the vertical, the first engagement answers these before anyone writes code.
-
01
Where does the data live?
Residency, ownership and who is allowed to see it decide the architecture more than the model does.
-
02
What does a wrong answer cost?
A wrong product recommendation is a nuisance. A wrong dosage flag is a serious incident. That gap sets the review process.
-
03
Who runs it on Tuesday?
Most AI projects fail after launch, not during it. We agree the operating model before the build starts.
-
04
What does month 13 cost?
We size infrastructure for the second year of usage, not the demo, so the bill does not surprise anyone.
Not sure which practice fits your business?
Tell us what you are trying to change. We will point you at the right team — or tell you honestly that you do not need us yet.