Services
AI & Data Engineering
Most AI projects never make it past the demo, because they tackle the model before the data. We start by putting your corporate data sources in order, then build the model on that foundation.
What we build
Enterprise assistants (RAG)
Assistants that work across internal documents, contracts and procedures, and cite their sources. They respect permission boundaries: no answer is generated from a document the user is not allowed to see.
Document intelligence
Structured data extraction from invoices, delivery notes, contracts and application forms — with a confidence score that flags the cases needing human review.
Forecasting & scoring
Demand forecasting, inventory optimisation, churn and risk scoring models. Model output is delivered where the decision happens: the screen your team already works in.
Data infrastructure
Collecting, cleaning and unifying data from scattered sources into a single analytical layer — a foundation that pays off in reporting even before any model exists.
How we work
Every AI project starts with a measurable success criterion: which decision, made how fast, at what accuracy. A short proof of concept tests whether the model genuinely adds value, and only if the result holds up do we move to a production architecture.
- 01Inventory and quality assessment of data sources
- 02A defined success metric and evaluation set
- 03Proof of concept on real data before the investment grows
- 04Production monitoring: output quality is tracked over time
Technologies we use
Technology choices follow the lifespan of the product, the size of your team and the cost of maintaining it.
- Python
- PostgreSQL
- Claude API
- OpenAI
- Docker
- AWS
What you receive
- Data inventory and quality report
- Evaluation set and model performance measurements
- Model service and API deployed to production
- Monitoring dashboard and alerting rules
- Model card: boundaries, risks and known weaknesses
- Usage and intervention guide for your team
Frequently asked questions
01Will our data leave the company?
That is an architectural choice, made with you up front. Where data must not leave your premises, we deploy open models on your own infrastructure. Where a cloud provider is used, data processing agreements, retention periods and no-training guarantees are put in writing.
02How do you manage the risk of wrong answers?
In three layers: the model answers only from documents it can cite, low-confidence cases are routed to a human, and accuracy is monitored in production through continuous sampling. For critical decisions we position AI as a recommendation layer, never the decision maker.
03Which model do you use?
It depends on the task, and it will keep changing. That is why we build model-agnostic systems: switching providers does not mean rewriting the application layer.
04Our dataset is small — is that enough?
For document-grounded assistants it usually is; that approach does not train a model, it searches and reads your existing documents. Forecasting models do need meaningful historical data — and if there is not enough, we say so during the proof of concept, before the budget grows.
Industries we deliver this for
The same service means different requirements in different industries. Start with the one closest to yours.
Let's talk about your project.
The first conversation is led by the engineering team that would build it. We work through the technical approach and an indicative timeline together.