Product engineering, for the AI era.
New builds, modernisation, AI-assisted delivery, and production operations under one delivery org. Frontend, backend, mobile, data, AI — shipped by an engineering team that operates what it builds.
Most programmes don’t fail on technology — they fail on the seams between teams.
Legacy modernisation paralysis
Rip-and-replace is too expensive; doing nothing is more expensive still. Programmes stall in a planning phase that never converges, because no slice is small enough to ship without breaking the system it depends on.
Full-stack capability gap
Frontend, backend, mobile, data, and AI sit in different vendors and different procurement contracts. Every release becomes an integration project — and the seams show in production.
AI added as an afterthought
Models are bolted onto products designed before evaluation harnesses, retrieval, and drift monitoring were table stakes. The result: demos that impress and a production surface that quietly degrades.
Operate-vs-build divide
Builders hand to operators, and the architecture chosen for the demo becomes the architecture an on-call team has to live with. Reliability and unit economics suffer in proportion to that gap.
Sovereignty constraints
Data residency, regulator audit, and on-premise deployment requirements arrive late in delivery. Cloud-default architectures get re-engineered under deadline pressure — or quietly out of compliance.
Six capabilities, one delivery org — shipped, not theorised.
Greenfield product engineering
Co-build digital products from a blank repository. Product strategy, design, full-stack engineering, and AI-assisted delivery shipped by one team — demo-ready, pilot-ready, enterprise-evaluation-ready.
Legacy modernisation
Modernise estates without a rip-and-replace gamble. Strangler patterns, sidecar services, and incremental migrations — each slice paid back in cost, velocity, or risk before the next slice starts.
AI-assisted delivery
AI embedded into how we build — code generation, review, test synthesis, refactoring — and into the products we ship: RAG, agentic flows, document understanding, with evaluation harnesses built in.
Mobile & multimodal experiences
Native iOS and Android, cross-platform RN/Flutter, voice, vision, and document interfaces. Engineered for offline-first, low-bandwidth, and on-device inference where the network can't be trusted.
Data platforms & APIs
Ingestion, lakehouses, streaming, and API surfaces that the business and the model both rely on. Access controls preserved end-to-end; telemetry that operators read on day one.
Production operations
Once live, we operate. SLA-backed incident response, AI pipeline monitoring, retraining cycles, FinOps controls, and quarterly reliability reviews — so platforms get more reliable with usage, not less.
Three phases — discover, build, operate.
Sprint zero
A focused discovery sprint that converges on architecture, scope, success metrics, and a delivery plan you can defend in front of a finance committee. Output is a build plan, not a slide deck.
- ·Architecture decisions logged
- ·Success metrics agreed
- ·Delivery plan committed
Build phase
Full-stack delivery against the committed plan. Frontend, backend, mobile, data, and AI shipped by one engineering org — under your security model, in your environments, with handover material kept current as we go.
- ·One delivery org
- ·Continuous handover
- ·Security-cleared environments
Production handover
Either the team you built with stays on as managed operations, or hands over cleanly to yours. SLA-backed coverage, AI observability, retraining cycles, and FinOps controls — explicit choices, not afterthoughts.
- ·SLA-backed coverage
- ·AI observability
- ·Quarterly reviews
Where else our engineering org ships for clients.
Applied & Generative AI
Fraud, forecasting, document and email processing, RAG knowledge assistants, agentic operations — engineered into your workflows.
AI Talent
Production-grade AI engineers embedded into your team. Built, not benched — the bench you embed from is the bench that builds our own AI systems.