Most engagements start in one practice and end up spanning three. That’s fine. It’s the same people either way.
Web and mobile products from zero to production, and legacy platforms brought back under control.
The usual failure here isn’t the build. It’s the second year. Teams ship a first version optimised for launch speed, then discover the data model can’t carry the features the business now needs. We design for the shape of the business, not the shape of the demo.
Multi-agent architectures, knowledge graphs, retrieval, evaluation and human review workflows.
Most AI features fail on trust, not capability. A system that returns a plausible answer with no way to check it is unusable in finance, healthcare or law. We attach provenance and confidence to each answer and design the review loop first.
Extracting structure from documents that were never designed to be parsed: bank statements, invoices, filings, contracts.
Teams treat this as an OCR problem and buy an API. It isn’t. Deciding whether a line is a new record, a wrapped continuation or a footer is structural reasoning under uncertainty, and it needs several weak signals combined, not one model asked politely.
Infrastructure, CI/CD, zero-downtime deploys, observability, and the on-call rota afterwards.
Deploys that are risky get done rarely, and rare deploys get big, and big deploys break. The fix is boring and structural: make the deploy reversible in seconds and the fear goes away.
No rate card. We’ll give you a number after one conversation.
Two weeks, fixed fee. Architecture, risks and a costed plan, yours to keep whether or not you continue with us.
Fixed scope where the scope can genuinely be fixed; time and materials where it can’t. We’ll tell you which one you’re in.
Senior engineers inside your repositories, alongside your team, monthly rolling. This is how most of our long engagements run.
We take on systems someone else built. Audit first, then maintenance and modernisation.
That’s usually what the first call is for.
Book a call