Skip to content

S / 4

Custom AI Solutions

Where off-the-shelf tools fall short, we build AI agents and systems tailored to your data and your rules.

  • Custom-built AI agents and pipelines
  • Tailored to your data structures and edge cases
  • Configurable to your compliance and business rules
  • Designed to replace where generic tools fall short
Artist's illustration of an AI microchip design
Photo by Google DeepMind on Pexels

Off-the-shelf AI tools are built for the average use case — and most real businesses aren't average. When a generic tool can't handle your data, your edge cases, or your compliance rules, we build the system that can.

That means custom agents and pipelines trained and configured around your actual data, your actual rules, and your actual definition of correct.

PROCESS

How a custom solution is built

45 MINUTES

Problem Call

We talk about exactly where off-the-shelf tools fall short, and what a 'correct' answer means in your context.

  • Boundaries of the use case
  • Initial feasibility read

1–2 WEEKS

Data and Feasibility

We examine your data, the edge cases and the compliance constraints, then turn the success criterion into a number.

  • Data quality and access analysis
  • Written success criterion and test set

2–4 WEEKS

Prototype

We build a narrow working prototype and measure it on your real data — the go/no-go decision comes from results, not guesses.

  • Accuracy measured on real data
  • A clear threshold for go / no-go

6–16 WEEKS

Production and Handover

We take the prototype to production with monitoring, logging and a feedback loop, then hand it over with the source code.

  • Monitoring and quality dashboard
  • Source code and handover document

FAQ

What clients ask about custom AI

Generic tools are designed for the average case. The moment your data structures, edge cases or compliance rules diverge from average, a generic tool either gets it wrong or stops short. We test that by measuring it in the feasibility stage, not by assuming it.

Where each piece of data goes is defined in the contract. Our default is that your data is excluded from model training, and where needed the solution runs entirely on your own infrastructure.

Every solution ships with a test set: accuracy is measured on real examples and anything below the threshold is routed to a human. Being able to say 'I'm not sure' is part of the design.

Yes. The source code and documentation are handed over to you — you're not locked into a closed box.

Feasibility produces a per-transaction cost estimate and the architecture is built around it — small models, caching, and a large model only where it earns its place. Monthly usage stays visible on a dashboard.

Ready to talk about your process?

Book a Call