Byte Insights is one person
The same person designs it, builds it, deploys it and writes the documentation. There is no account manager, no handover, and no subcontractor — which is the reason a proof of concept takes four weeks rather than four months.
Stephen Henry
GenAI Solutions Architect
Most AI consultancies advise, hand over, invoice and leave. I build things and then keep them running, which turns out to change what you learn.
Byte Insights exists because of a pattern I kept seeing: AI pilots almost never fail because the model was not clever enough. They fail at the boundary where they meet the systems a business already runs — the ticketing system nobody wants to touch, the statement format that varies by bank, the sign-on that has to work before anyone will approve anything.
So that is what a four-week proof of concept tests. Not whether the idea sounds plausible in a workshop, but whether it survives contact with your estate — and whether it is worth building at all. I am not bidding for the build that follows, which means I can tell you when the answer is no.
What that looks like in practice
One person is a fair thing to ask about. This is the answer.
Three client AI projects, three in production
Documentation time cut by 95% across six languages for a global financial services firm. Bank statements turned into structured data, with an extraction pipeline built per bank because generic OCR does not survive real statements. A support desk taken from hours to minutes at 92% categorisation accuracy. All three run daily.
Nookaly — built, shipped and operated
A UK property analysis product live on the App Store and Google Play, with a browser extension alongside it. Behind it sits a microservice backend with asynchronous workers, its own billing, and self-hosted infrastructure. It is maintained daily, which is a different discipline from delivering a project and leaving.
The unglamorous half
Infrastructure, deployment pipelines, identity and tenancy work, and the security and compliance documents that let a project actually get approved. This is usually where AI projects stall, and it is not something that can be subcontracted around.
No coordination risk
The usual alternative for a project this size is three suppliers — one for the model, one for the application, one for the infrastructure — and you in the middle holding the seams together. That is the risk being compared against, and it is worth comparing honestly.
- Web, iOS and Android product builds
- Document and PDF extraction into structured data
- Classification, triage and routing
- Retrieval systems over your own documents
- Custom model training and fine-tuning
- Infrastructure, deployment and data pipelines
- Microsoft 365 and Azure estate work
- Security and compliance documentation
Got an idea worth testing?
Four weeks from now you could have something working, the documentation to take it forward, and an honest answer about whether to build it.