Domain experts stay yours
Your process engineers, clinicians, risk officers and network operators are the acceptance criteria. We build the system around their judgement rather than trying to replace it.
The architecture is transferable. The constraints are not. What separates a working AI system from a demo is almost always domain-specific — the data lineage, the control requirements, the people who have to trust the output.
How we approach domain work
We bring the engineering. You bring the domain. Neither works alone.
We do not claim to know your business better than you do. We claim to know what breaks when AI meets a regulated, high-volume or IP-sensitive environment — and to design for it up front.
Constraints first, capability second
Data residency, explainability, latency and IP boundaries change the architecture. We surface them in the readiness audit, before anything is designed.
We will scope you out of bad ideas
Some use cases are not ready, and some should not be automated at all. Saying so early is cheaper for you than discovering it in month three.
Where we go deep
Four sectors we know well.
We work outside these too. These are the four where we can be specific about what usually goes wrong.
Banking & financial services
AI that survives a model risk review.
In regulated finance the hard part is not building the model. It is proving, on demand, why it decided what it decided — and being able to turn it off.
- Fraud and AML triage — Reduce false positives on alert queues so investigators spend their time on genuine cases, with every suppression logged and reviewable.
- Credit and underwriting support — Decision support with reason codes attached, structured so the reasoning is auditable rather than inferred after the fact.
- Servicing and contact-centre copilots — Retrieval grounded in current policy documents and the customer's actual account state, not a generic knowledge base.
Telecom
Network scale breaks ordinary AI operations.
Telecom generates more machine data than almost any other sector, and it arrives continuously. That makes AI attractive and makes naive AI operations collapse under load.
- Alarm correlation and noise reduction — Collapse alarm storms into probable root cause so operations acts on incidents rather than symptoms.
- Predictive maintenance and field dispatch — Prioritise interventions by likely failure and customer impact, then feed the outcome back into the model.
- Care and retail copilots — Frontline assistance grounded in plan, device and account context, with escalation paths that stay auditable.
Healthcare & life sciences
Clinical and safety work has no tolerance for a confident guess.
AI can absorb an enormous amount of administrative burden in healthcare. It can also cause real harm if it is deployed where a plausible-sounding wrong answer is dangerous.
- Clinical documentation support — Draft and structure notes for clinician review and sign-off — always assistive, never autonomous.
- Pharmacovigilance case intake — Triage and structure adverse event reports from unstructured sources, with full traceability to the original.
- Evidence and literature synthesis — Retrieval across internal and published literature where every claim carries a citation.
Semiconductor & hi-tech
Where a percentage point of yield is the entire business case.
Semiconductor and hardware engineering produce dense, high-quality process data and operate on margins where small analytical gains are worth a great deal.
- Yield analytics and excursion detection — Detect process excursions earlier and localise probable cause across tools and steps.
- Test optimisation — Reduce test time and cost while holding escape rates inside agreed limits.
- Design and verification productivity — Assist with RTL review, testbench generation and regression triage under engineer supervision.
Outside these four
The pattern travels further than the sector knowledge does.
Retail, logistics, insurance, manufacturing, public sector — the reference architecture and the delivery framework are the same.
What transfers
The four-layer architecture, the MLOps and evaluation discipline, the readiness audit, the pod model, and the habit of measuring before scaling. None of that is sector-specific.
What does not
Regulatory obligations, data topology, domain vocabulary, and who has to sign off. In a new sector we spend longer on the readiness audit and we say so in the proposal.
Ready when you are
Tell us the sector and the workflow.
Forty-five minutes is usually enough to say whether the constraint is the data, the architecture, the governance, or the people.