AI built on your data, not somebody else's
The useful AI in a business is rarely a general assistant. It is search that actually finds the right contract, a system that reads invoices correctly, or a feature inside your own software that saves an hour a day. We build those, on your data, with the privacy decisions made deliberately.
- Your data, your control
- Private deployment available
- Built into your software
- Evaluated, not guessed at
- Where the data lives
- Who may see what
- How accuracy is measured
- What happens when it is wrong
- What it costs to run
Before any model is chosen, we look at the data, the privacy constraints, the accuracy you need and how it will be measured.
How we help with custom ai development
Each piece can stand on its own, or run as one engagement with a single point of contact.
Private knowledge systems
Search and answers over your documents, with citations and permissions respected.
- Retrieval augmented generation
- Source citations
- Access control per user
Document intelligence
Reading invoices, contracts, forms and reports, and turning them into structured data.
- Field extraction
- Validation rules
- Review queue for exceptions
AI features in your product
Drafting, summarising, classifying and recommending, inside the software your team already uses.
- Built into existing screens
- Usage limits and costs controlled
- Feedback captured
Model integration
Connecting commercial or open models to your systems, with the glue that makes them reliable.
- Prompt and context pipelines
- Caching and cost control
- Fallbacks when a model fails
Private deployment
Open models running on servers we manage for you, when data must not leave your control.
- On your own infrastructure
- No third-party data sharing
- Monitored and updated
Evaluation and safety
Test sets, measurement and guardrails, so you know whether it works before customers do.
- Accuracy measured on your data
- Regression tests on changes
- Refusal and escalation behaviour
Most of the work is data, not models
Choosing a model is the easy decision and rarely the one that matters. What decides whether an AI feature is useful is the state of your data: where it lives, how clean it is, who is allowed to see which parts, and how you will know when the answers are wrong.
- We start with an audit of the data and the permissions around it
- Retrieval is built and measured before any prompt is tuned
- Accuracy is tested against examples you agree are correct
- Costs per request are modelled before launch, not discovered after
- If AI is not the right answer, we say so and suggest what is
Four steps, and you can see all of them
No long silences. You see progress as it happens and can change direction between stages.
- 1
Audit the data
What exists, where it lives, who may see it, and what shape it is in.
- 2
Prove it small
A narrow prototype on real data with a measurable question: does it find the right answer?
- 3
Build for production
Retrieval, permissions, monitoring, cost control and the interface people will actually use.
- 4
Measure and improve
Evaluation sets, feedback from users, and changes tested against both.
We would rather run a two-week prototype that proves the idea than a six-month project that assumes it.
What we build with
Chosen to suit the job and to be maintainable by whoever comes next.
- Commercial APIs
- Open models
- Embedding models
- Fine-tuning where it helps
- Vector databases
- Hybrid search
- Chunking strategies
- Re-ranking
- Laravel and Node.js
- Our cloud servers
- Monitoring
- Evaluation harnesses
Models change every few months. We build so the model can be swapped without rewriting the application around it.
Where this work usually starts
Firms with document archives
Contracts, case files and reports nobody can search properly.
Back offices processing paper
Invoices and forms typed into systems by hand.
Product teams adding AI
Software that needs a genuinely useful AI feature rather than a novelty.
Organisations with privacy limits
Data that cannot be sent to a third-party service.
Support and sales teams
Knowledge that exists but cannot be found when a customer is waiting.
Anyone burned by a pilot
An AI project that demoed well and never reached production.
Custom AI Development, answered
What is retrieval augmented generation?
Giving the model your own documents at the moment of the question, so the answer comes from your material and can cite where it came from, instead of from whatever the model memorised.
What is retrieval augmented generation?
Giving the model your own documents at the moment of the question, so the answer comes from your material and can cite where it came from, instead of from whatever the model memorised.
Bring us a question your data should answer
Tell us what people keep failing to find, and we will tell you whether AI can find it and what it would take to prove.