Automation that gives time back
Models wired into your data and your systems - not another chat window that knows nothing about your company.
AI on your own data
Process automation: the difference is the data, not the model
A language model on its own is a commodity - everyone has the same one. Value appears only once the model is given your documents, your tickets and your system data, and the answers land where people actually work.
That is integration work, and integration is what we do. Connecting a model to a knowledge base, an ERP or a mailbox, handling errors, controlling the cost of requests and respecting permissions - all of it is ordinary programming with one new component in the middle.
We start from a distinctly unglamorous question: what does somebody at your company do every day that amounts to copying data between systems? The answer usually points at the first process worth automating - and often shows that AI is not needed for it at all.
What we automate and where AI fits in
AI over your documents (RAG)
An internal knowledge search that answers from your own documents and cites the source, instead of inventing an answer.
Support assistant
Suggestions for your support team based on ticket history and documentation. A person approves the reply; the model only shortens the way to it.
Document and ticket flow
Invoices, emails and orders read automatically, classified and pushed into a CRM or ERP without manual retyping.
Automation without AI
Rules, scheduled jobs and API integrations. Cheaper, predictable and unsupervised - we propose these wherever they are enough.
Audits of AI-written code
Reviews of applications built by vibe coding, looking at security, performance and whether the thing can be developed further at all.
Product content at scale
Generating and refreshing product descriptions and metadata for large catalogues, wired into a PIM or the shop database.
Advice on where AI fits
A look at your processes to see what is worth automating, plus the GDPR and EU AI Act questions, before any data leaves the company.
How we deliver an automation project
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1
One process first
We pick a single, measurable task instead of rebuilding the whole company. Easier to judge the result, cheaper to walk away if it does not pay off.
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2
Decisions about data
Before anything is built we agree what data may leave the company and what may not. That decides the provider, or whether the model runs on your own server.
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3
A pilot
We build a working slice on real data and measure how much time it actually saves. That number, not a promise, is the basis for deciding the scope.
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4
Delivery and limits
Integration with the target system, handling of bad answers, and hard caps on request costs so the bill stays predictable.
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5
Maintenance
Models and their APIs change every few months. A deployment left alone stops working within a year - this is not a project that ends.
What we work with
For anyone who wants to know what we work with. If these names mean nothing to you, that is fine - this part is our job, not yours.
- PHP 8
- Laravel
- Python
- OpenAI API
- Anthropic API
- self-hosted models
- RAG
- vector databases
- PostgreSQL
- pgvector
- Redis
- REST API
- n8n
- Linux
Questions and answers
What clients ask us most often
Where do we start if we do not know what to automate?
With one task somebody does every day that amounts to copying data from one place to another. It is usually the cheapest first step and the one with the most visible result. No workshops or five-figure audits required.
Do you build your own AI models?
No. We use existing models through their APIs and build what actually solves the problem around them: integration with your data, cost control, error handling and the interface. Training your own model rarely makes economic sense outside very specific cases.
What happens to the data sent to the model?
That is the first thing we settle. We pick the provider and the mode so your data is not used for training, and where regulations or company policy demand it, we limit what gets sent or use a model running on your own infrastructure.
What does it cost to run?
On top of our work there is the cost of model requests, billed by usage. During delivery we estimate it from your real volume and build in limits so the bill cannot run away.
Is AI even needed here?
Often it is not. A good share of the enquiries that reach us labelled "AI" can be solved by ordinary automation: a rule, an integration or a scheduled job. That is cheaper, predictable and needs no supervision. We say so before building anything.
Take back the time routine eats
Describe what repeats every day at your company. We will tell you whether automating it pays off - including when we think it does not.
Get in touchKnowledge base
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