Custom AI Software
Products built around the intelligence, not bolted onto it
When AI is the core of the product rather than a feature, the architecture, the interface and the data model all have to be designed for it. We build those products end to end.
Software where AI is the point
Adding an AI feature to an existing application is a contained piece of work. Building a product whose central value comes from a model is a different discipline: the interface has to communicate uncertainty, the data model has to capture feedback that improves the system, and the architecture has to absorb the fact that model behaviour changes over time.
We work as a single team across the whole surface — product design, front end, back end, model integration, infrastructure and release. That matters because in AI products the hardest decisions sit exactly on the boundaries between those disciplines: how a confidence score should be shown to a user, how corrections get captured as training signal, how a failed inference degrades without breaking the page.
We build on conventional, well-supported foundations. The AI layer is where the novelty belongs; the rest of the stack should be boring, maintainable technology your own team can pick up later without a specialist.
What we deliver
AI product and MVP development
From concept to a working product real users can try, scoped to prove the core value before the surrounding features get built.
SaaS platforms
Multi-tenant applications with the parts that are tedious but essential — authentication, roles, billing, admin tooling and audit logs.
Web applications
High-performance, accessible web platforms engineered for scale and speed, and for the search visibility that depends on it.
Mobile applications
Native-quality iOS and Android experiences, including offline behaviour and on-device inference where it makes sense.
Cloud architecture and migration
Infrastructure designed for AI workloads — GPU capacity, vector stores, queuing, autoscaling and cost control — on AWS, Azure or GCP.
UI/UX design
Interface design that makes probabilistic systems feel trustworthy: showing sources, communicating uncertainty and making correction easy.
How we work
We ship something usable early and let real feedback direct the rest.
Shape the product
We define who it is for, which single problem the first version solves, and what evidence would prove it is working. Anything not serving that goes on a later list.
Design and prototype
Interface and technical design in parallel, with a clickable prototype to settle disagreements before code becomes expensive to change.
Build in increments
Short cycles with working software you can use at the end of each one. Model integration comes early, because that is where the unknowns are.
Launch and hand over
Deployment, monitoring and cost controls, plus documentation and a handover so your team can operate and extend the product without us.
How we usually engage
Every engagement is scoped individually, but these are the shapes that come up most often.
- A funded idea that needs a first version in front of real users
- An existing product that needs an AI capability added properly rather than bolted on
- A validated internal tool that has outgrown its spreadsheet or no-code origins
- A prototype that works in a notebook and now needs to become a real system
- A team that needs senior engineering capacity alongside their own developers
- A legacy application that needs rebuilding without losing years of embedded logic
Frequently asked questions
Who owns the code and the intellectual property?
You do. Ownership of the source code, models, prompts and documentation transfers to you, and this is written into the agreement before work starts. We also hand over the repositories, infrastructure definitions and deployment access so you are never dependent on us for continuity.
Can you work with our existing development team?
Yes, and it often produces the best outcome. We can lead delivery, provide specialist AI capability alongside your engineers, or embed with your team and transfer knowledge as we go. The mode is agreed at the start along with how code review and standards will work across both teams.
What technology stack do you use?
We choose per project rather than applying a house default, weighting long-term maintainability and your team’s existing skills heavily. In practice that usually means mainstream, well-documented frameworks and one of the major cloud providers, with the AI layer built to be portable across model vendors.
How do you price a project?
For well-defined scope we quote a fixed price against an agreed specification. For exploratory work, where the scope genuinely cannot be known upfront, we work in time-boxed increments with a fixed rate and a clear review point at the end of each. We will say which model we think applies rather than quoting a number that cannot survive contact with reality.
What happens after launch?
That is a decision you make, not a lock-in. Some clients take full ownership immediately with a handover period; others keep us on for support, monitoring and continued development. Either way the handover materials are produced as part of the build, not as an afterthought.
Related services
Have a product in mind?
Tell us what you are trying to build and who it is for. We will come back with an approach, the main risks we see, and what a first version would involve.