AI Automation & Agents
Work that repeats every single day: sorting, looking things up, pulling data together, passing it on. Software can take it over.
Where it usually goes wrong
The work has been digital for years, but someone still sits in front of it: reading requests, gathering the right answer, moving it from one system into the next. None of it is hard. It is simply there every day, it never stops, and it ties up exactly the people who are needed elsewhere.
What you get out of it
- Not just an answer — the task actually finished
- Before anything irreversible happens, it asks first
How we do it
The difference between a chatbot and an agent
A language model on its own can guess. An agent gets to look things up: in the database, in the code, in the logs, in the ticket system. It fetches what it needs, verifies, and only then decides. That is why it completes work a chatbot cannot.
Not every request needs the expensive model
Most cases are straightforward. A small, cheap model triages them; the large one only reasons where things get genuinely tricky. On a system running continuously, that is the difference between a bill you can plan for and one that surprises you.
What happens when a data source fails?
Nothing dramatic, if you planned for it. We fetch data from several systems in parallel and continue with whatever arrives. A hanging third-party API call then does not stall the entire pipeline.
People stay in the loop where it matters
Before a system does something hard to undo, it stops and asks. That pause survives a container restart — otherwise the next deploy leaves someone stranded mid-process.
Every decision can be explained afterwards
Which data was available, which model decided, on what grounds. Without that trail no agent earns lasting trust inside a company — and untrusted systems get switched off.
Switching providers without a rebuild
The language model sits behind an interface and can be swapped by configuration. Pricing in this market moves fast. You want to react without touching half the application.
What we work with
- Python
- FastAPI
- OpenRouter
- PostgreSQL
- Docker
- AWS
Demonstrated by
The first step
Name one task that repeats every week. In the first conversation we will tell you whether automating it pays off — and if not, why not.