AI and data systems on data you control.
Most companies already have the data. It's just spread across systems nobody joined up. We build the pipelines that bring it together, the reports your managers actually read, and the AI features (search, scoring, document reading, forecasting) that make your product smarter.
This sounds like you
- Your reports are built by hand every week, and nobody fully trusts them.
- You want AI in your product but not a black box you don't own or understand.
- You have documents, tickets or records that people search manually today.
- You need forecasts for demand, stock or delays that are better than gut feel.
Concrete deliverables, not a proposal deck.
Data platforms & reporting
One clean source of truth across your systems, with dashboards and scheduled reports built on top.
Predictive analytics
Demand, delay, churn and stock forecasts trained on your own history, with the accuracy stated honestly.
AI features inside your products
Smart search, supplier or lead scoring, document extraction, assistants, shipped as part of your software, not a separate tool.
Your data, your infrastructure
Models and data stay in your cloud account. No training on your data by third parties, no per-query surprise bills.
PartsAxis: aviation parts intelligence
One searchable global inventory of aviation parts with a confidence score for every supplier and compliance checks built into each request.
See the work →Four steps, every project.
- 01
Understand first
We map how you work today before writing any code.
- 02
Agree the data
We fix how information moves and who owns it.
- 03
Build security in
Access control and audit trails from day one.
- 04
Hand you the keys
Code, infrastructure and docs, all yours.
Asked on most first calls
- Do we need a lot of data to start?
- Less than you'd think. Reporting and search work from day one. Forecasting needs history, usually a year or two of records, and we tell you upfront if what you have isn't enough.
- Will our data be used to train someone else's model?
- No. We run models inside your own cloud account, and where we use an external AI API we choose providers with no-training terms and keep sensitive fields out of the request.
- How do you measure whether the AI actually works?
- We agree a plain success measure before building (hours saved, error rate, forecast accuracy against last quarter) and report against it. If it doesn't move the number, we say so.