Custom AI Development
Custom AI / ML Model Development
Models trained for your specific problem
Models trained for your specific problem. We design and ship Custom AI / ML Model Development on your data, your rules and your stack, so it works inside the business, not beside it.
- Projects delivered
- 200+
- Clients served
- 160+
- Years building software
- 10+
- Models built on it, kept private
- Your data
What we do
AI built on your data, your rules and your stack.
Models trained for your specific problem. We design and ship Custom AI / ML Model Development on your data, your rules and your stack, so it works inside the business, not beside it.
Discovery defines the problem boundary, data sources and success metrics first. Then we build a focused release you can operate, measure and extend.
Product and engineering leaders
A partner who can ship production AI without leaving brittle prototypes behind.
Operations and domain owners
Models grounded in real documents, workflows and exception paths.
Founders and CXOs
Clear phases, ownership and ROI, not an open-ended experiment.
What we deliver
What Custom AI / ML Model Development includes.
- 01
Problem framing and data design
What to predict, from which data, and how to measure it.
- 02
Model training and evaluation
Offline and online checks against a baseline.
- 03
Inference API
A versioned endpoint integrated into your stack.
- 04
Retraining plan
A schedule and owner to keep the model accurate.
How we work
From first call to launch, in 4 clear steps.
Step 01
Discover & prioritise
We map the process, check whether the data can support the idea, and rank use cases by value against effort.
Step 02
Prototype & evaluate
A working slice on your real data, measured against an agreed evaluation set instead of a demo script.
Step 03
Harden & integrate
Guardrails, permissions, fallbacks, and integration into the systems your team already uses every day.
Step 04
Operate & improve
Monitoring for drift, cost, and quality, with a retraining rhythm and clear ownership after handover.
Technology
What we build it with.
Proven building blocks, chosen for your use case and your data rules.
- Language modelsGeneral models adapted to your task where they fit.
- Retrieval (RAG)Ground answers in your documents and databases.
- Python ML stackTraining, evaluation and data pipelines.
- Computer visionRead images, documents and video frames.
- Inference APIsVersioned endpoints your apps can call.
- Evaluation and monitoringQuality, drift and cost tracked after launch.
How we build AI
AI you can measure, control and own.
- 01
Measured, not assumed
Every use case ships with an evaluation set and a baseline, so improvement is a number rather than an opinion.
- 02
Humans stay in control
Irreversible actions need approval, confidence thresholds route to people, and every action is logged.
- 03
Your data stays yours
Access-scoped deployments, retention rules you define, and no training on your data without written consent.
- 04
Built to be handed over
Documentation, runbooks, and enablement so your engineers can own and extend what we build.
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FAQ
Questions, answered.
It depends on the workflows and integrations involved. After discovery we agree a first release you can run and measure, then expand from there.
It depends on the problem. Discovery includes a data readiness check, and some use cases work well with your documents and a general model.
No. Your data stays yours, with access controls and retention rules you define.
Yes, where the use case allows. See Private / On-Premise AI for setups where data never leaves your office.
Ready to scope Custom AI / ML Model Development?
Bring the problem and the data you have, and we will tell you honestly what AI can do with it.