Applied AI
AI where it saveshours. Not whereit sounds good.
We integrate models where the return can be measured: documents that stop being typed by hand, repetitive tasks that get automated, and internal questions that answer themselves.
Where it usually pays off
AI does not fix a broken process: it speeds it up, mistakes included. These are the cases where we see a clear return:
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Someone spends hours moving data from invoices, delivery notes or PDFs into a system.
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You get a lot of repetitive questions whose answer is already written down somewhere.
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A high volume of text needs classifying or summarising: emails, tickets, CVs.
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You have extensive internal documentation nobody can find when they need it.
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Quality checks are done by hand on data that follows a known pattern.
How we work
- 01
Start with the case, not the model
The question is not which model to use, but which specific task eats hours and can be measured. If we cannot work out how much it saves, it is not a good first AI project.
- 02
A bounded trial before investing
Before any full development we run a trial on your real data, at small cost and over a short period, to see whether accuracy is good enough. If it is not, it stops there.
- 03
A person in charge where it matters
In processes with consequences —invoicing, contracts, client data— the AI proposes and a person approves. Full automation is reserved for where a mistake is cheap.
- 04
Cost and data under control
We define from the outset which data leaves your infrastructure and which does not, and we monitor usage spend so there are no surprises at month-end.
What you get
- A trial on real data with accuracy measured before you invest
- The integration running inside your existing tools
- Control over which data is processed and where
- Usage cost tracking
- An honest estimate of the time it saves, worked out with you
How we integrate it
The AI lives inside your systems, not in a separate tool people have to log into.
- Backend
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- Java
- Spring Boot
- Python
- Flask
- Databases
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- PostgreSQL
- MySQL
- MongoDB
- Redis
- Messaging and search
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- Kafka
- RabbitMQ
- Elasticsearch
Common questions
Will our data end up training a model?
Not if you do not want it to. We define at design time which data leaves your infrastructure and which does not, and models can be deployed inside your own environment where data sensitivity requires it.
How do we know it works before spending the money?
With a bounded trial on your real data. We agree beforehand what accuracy would be acceptable, measure it, and decide with the figure in front of us rather than off a polished demo.
What happens when the AI gets it wrong?
We assume it will and design accordingly. In processes with consequences, the AI proposes and a person approves. Unsupervised automation is only considered where a mistake is cheap to correct.
Is this not very expensive to maintain?
It depends a great deal on the case. Usage cost is monitored from day one and can be capped. If the hours saved do not cover the spend, it is a bad project and we would rather say so up front.
Shall we talk about your project?
Tell us your idea and we will get back to you within 24 hours.