Services
AI app development, by a company that runs its own apps
We build and operate Nure AI OS, Desi and Logi. When we build an application for you, it is the same engineering, by the same people, against the same standard we hold our own software to.
Why most AI applications disappoint
Almost every AI application that fails does so for the same reason. A language model was put in the middle of the system and asked to do work that ordinary code does better: branching, validation, lookups, arithmetic, the decision about what happens next. It is slower at all of them, more expensive at all of them, and the only one it is better at is the one nobody specified.
The result is an application that demos well and then breaks under load, costs more every month than it saves, and cannot be reasoned about when it gets something wrong.
The fix is not a better model. It is putting the model where judgement is genuinely needed and leaving everything else to software that is fast, cheap and identical every time it runs.
What we build
AI-native business applications
The system your operation runs on: records, workflow, messaging and documents, with intelligence inside the parts that need judgement rather than wrapped around the outside.
Agentic workflows under governance
Agents that carry out real work end to end, with approval lanes on the consequential actions and an audit trail on every one. Autonomy you can widen because you can see what it did.
Retrieval over your own data
Answers composed from your records rather than from a model's recollection. The store is designed first, which is the part most projects skip and most failures trace back to.
Fine-tuned models for the repeating work
Where one decision happens constantly and has the same shape every time, a small model tuned for it beats a large general one on latency, cost and consistency.
Voice, WhatsApp and telephony
The channels a business actually gets contacted on, wired into the same record rather than living in a separate inbox nobody reconciles.
Integration with what you already run
Accounting, CRM, scheduling, payments and the spreadsheet that quietly runs half the company. The application has to meet the operation where it is.
Why us
We are not advising from the sidelines. Nure AI OS is our operating system for AI-native business, and we run it. Desi and Logi are ours. Every architectural opinion on this page is one we pay for ourselves when we get it wrong.
That changes what you get. We have already made the expensive mistakes on our own time: where the model belongs, what memory costs, which parts of an agent need a human in the lane, what breaks at the seams between calls. You get the version after those lessons.
We publish the engineering rather than asserting it. The research library on this site is open, cites its sources and says plainly where our argument goes beyond what we can prove.
How an engagement runs
- 01
Understand the operation
Not the brief, the operation. What actually happens, how often, who it waits on and where the time goes. Most of the value is decided here, before anything is built.
- 02
Architect before intelligence
What must be deterministic, what data has to exist, where judgement is genuinely required. The decision about where the model goes is the decision that sets your cost and latency for the life of the system.
- 03
Build the core first
Records, rules and workflow before agents. A model on top of a system that cannot be relied on produces unreliable work faster.
- 04
Deploy with governance on
Approval lanes and an audit trail from the first day in production, not retrofitted after an incident makes them urgent.
- 05
Evolve it
The operation changes and the data compounds. The parts worth tuning become obvious only once there is real usage behind them.
Questions
- What does an AI app development company actually do?
- It designs and builds software where machine intelligence is part of the system rather than a feature bolted to the side. In practice most of the work is ordinary, careful product engineering: data model, workflow, integrations, reliability. The AI-specific part is deciding where a model belongs, what it is allowed to do, and what happens when it is wrong.
- Are you an agency?
- No. AI Gen Apps is a product company. We build and operate Nure AI OS, Desi and Logi, and we take on a limited amount of client work because building systems inside real operations is where the problems worth turning into products come from. You are hiring the team that runs its own software, not a vendor that only ships other people's.
- How much does it cost to build an AI application?
- It depends almost entirely on how much of the operation the application has to cover, and we will tell you which parts do not need to be built before we quote for them. What we will not do is price by headcount or hours, because that prices the wrong thing: you are buying a working system, not a number of people.
- Do we need our own data to build an AI application?
- You already have it. Every operation generates records, messages, documents and decisions, and in most businesses they are scattered across tools that do not talk to each other. A large part of what makes an AI application work is giving that information one home first. Without it a model has nothing to be right about.
- Should we use a general model or a fine-tuned one?
- Both, in different places. A general model is the right tool for the open-ended and the unexpected. For a decision that happens constantly and has the same shape every time, a small tuned model is faster, cheaper and more consistent, and the economics of tuning one have changed enough to make that practical.
- How long does it take?
- A focused system inside one function is usually a matter of weeks rather than quarters, because the deterministic core is well-understood engineering and that is most of the build. The timeline risk is almost never the AI part. It is integration with systems nobody has documented.
- Where are you based?
- AI Gen Apps Limited is registered in England and Wales, company number 16824575, with a registered office in London. We work with businesses in the UK and internationally.
- What happens to the code we pay for?
- It is yours. We build on standard, portable foundations rather than on anything that makes leaving expensive, and we document the architecture so another team could pick it up. A system you cannot take elsewhere is a system you are not really the owner of.
The engineering, published
- AI implementation is an architecture decisionWhy the model is rarely the constraint, and what is.
- TypeSafe's System One: code orchestratesThe case for keeping control flow in software, read against a real system.
- Governed autonomyApproval lanes and audit trails, and why they widen autonomy rather than narrow it.
- Read and write memoryDesigning the store before reaching for a bigger model.
What we build against
- The Model Context Protocol specificationThe interface an application's tools are exposed over, and what it does and does not guarantee.
- OWASP Top 10 for LLM ApplicationsThe failure modes an application that calls a model has to be designed against.
- WCAG 2.2An application people use has to be usable. Keyboard, focus and contrast are part of done.
Tell us what the operation actually does
The useful first conversation is about how your business runs, not about which model to use. Bring the process that costs you the most time.