Products designed around AI, not retrofitted with it.
For organisations building a new digital product, internally or for their customers, we engineer it with AI at the centre: the data model, the interface and the operating costs all planned for it.
AI-native is an architecture, not a feature.
Adding a chat box to an existing product is quick. Building a product where AI does real work for the user is a different exercise, and it changes the data model, the interface, the cost structure and how you test.
Teams that treat AI as a feature usually discover this late: inference costs that don't fit the pricing, outputs that can't be evaluated, and interfaces that don't show users what the AI did.
We work through those questions at the start, alongside your product owners, and engineer the product to answer them.
What we build and run for you.
Product discovery
Problem framing, user research and an AI feasibility check before committing to a roadmap.
MVP engineering
A first version in market quickly, with evaluation and cost tracking from day one.
AI platform layer
Model routing, retrieval, evaluation and observability shared across product features.
Scale-up
Multi-tenancy, performance and reliability work as usage grows.
Applications people and agents work in together.
Designed around the real workflow, engineered for the systems it has to reach, and supported by the team that built it.
From idea to product, with the economics checked.
Frame
discoveryWho the product serves, what job the AI does for them, and how you will know it works.
Prototype
2–4 wksA clickable, AI-backed prototype tested with real users.
Build
MVPProduction engineering with evaluation, cost and usage tracking built in.
Grow
iterateRelease cadence driven by usage data and evaluation results.
- Cost per request tracked from the first release
- Evaluation suite in CI
- Model choice abstracted, so it can change
- Tenant data isolation
- You own the code and IP
Where it earns its keep.
Illustrative patterns from the sectors we work in. Client details stay anonymised.
Citizen-facing digital service
A new service where an assistant guides applicants through requirements in two languages.
Advisor workbench
A product that prepares client reviews and suitability notes for advisors to finalise.
Vertical SaaS with AI at the core
A sector platform where document intake, matching and reporting are AI-driven.
Internal AI product
An internal tool rolled out across business units, with usage-based chargeback.
Chosen for the job, not the vendor.
Work this builds on.
A governed digital platform and partner API layer for a national education authority
Real-time event streaming feeding AI and ML pipelines across business units
Client details anonymised per delivery agreements.
Questions we get asked.
Can we own the resulting code?
Yes. The default commercial model is custom delivery on the accelerator pattern, with the resulting platform fully owned by the client. We are not running a subscription-revenue play on these; the accelerators exist to compress delivery time, not to create a vendor-lock relationship.
How do you keep AI running costs predictable in a product?
We measure cost per request from the first prototype, route simple tasks to smaller models, cache where it's safe, and set budgets per tenant or feature. Pricing and architecture decisions are made with those numbers in view, not after launch.
Can you work alongside our in-house product team?
Yes. Most product engagements are blended: your product owner and designers set direction, and we provide the AI, platform and engineering capacity. We plan the handover to your team from the start if that is the goal.
What would you hand to an agent first?
Bring one workflow. In 60 minutes an AI architect maps where an agent helps, what it needs to reach, and what a first working version would take.