AI agents and automation
The agent connects to your systems: it takes data from the CRM or accounting, does the routine part of the work and returns the result where it is expected. Wherever a mistake is expensive, a person checks the result.
What we automate
A good candidate for automation is repetitive data work a person now does by hand: read, sort, move, prepare.
The agent reads incoming emails, forms and messages, identifies the request type, extracts the data and passes it on — into the CRM or to the right person.
Documents and reports built from accounting data: the agent gathers the data, fills the template and highlights discrepancies.
Product cards, reference lists, customer records: the agent fills gaps, normalises and flags duplicates, and the service checks quality before anything is written.
The agent closes repetitive questions from customers or colleagues and hands complex ones to a person together with the context it has already gathered.
Collection from websites, open APIs and Telegram by your criteria: the agent filters the stream and sends only what needs attention.
A model as part of your service: image or text analysis whose result goes back into the user's workflow.
Control
AI fails differently from ordinary code: confidently and unpredictably. So the agent gets its boundaries before it reaches production.
The agent gets one process and a success criterion agreed before the start.
Where a mistake is expensive, the agent prepares and a person decides: the person sends the draft and publishes material after moderation.
A set of real examples that every prompt or model change has to pass. Quality does not slip unnoticed after the next update.
Cost telemetry for every model call, secrets kept out of code and prompts, and the agent's access limited to the data it needs.
Experience
We do not disclose client names — only the problem and the technology.
On a platform with an 800-thousand-item catalogue, agents are part of everyday work: per-repository agent configs, spec-driven tasks, eval gates in CI, cost telemetry and secrets-guard hooks. Only a human merges.
Four exchange APIs stream into TimescaleDB, with a rules engine of 40+ risk parameters on top. The system was built end to end with an agentic pipeline.
25 site-specific parsers, source-URL deduplication and failure monitoring. Material flows through a REST contract into an editorial moderation queue and is published after review.
Patient images go to Azure AI for analysis, and the result returns into the doctor's clinical workflow — with access control over sensitive medical data.
Technology
Only what is already running in production.
Questions
Yes. The agent works through the same integrations as any other system: REST, OAuth, webhooks, message queues. The accounting system is not rewritten — the agent reads data from it and writes the result back.
We choose the model provider with you: Anthropic, OpenAI or Azure AI. The agent receives only the data the task needs, and keys and secrets are kept out of code and prompts.
Where a mistake is expensive, the decision stays with a person: the agent prepares a draft or puts material into a review queue. Quality is watched by eval gates — a set of real examples every change has to pass.
It depends on volume and model. That is why the cost of every call goes into telemetry from the first day of the pilot — before scaling, you see the real price of one processed request.
With a short audit: we look for one repetitive task where automation has a measurable effect. Then a pilot of a few weeks with a success criterion agreed before the start.
Every day: per-repository agent configs, spec-driven tasks, eval gates in CI, cost telemetry, secrets-guard hooks. Merges into the main branch are made by a human.
Contacts
Tell us in two paragraphs what's not working. We will tell you whether we take it on and where we suggest starting. Meetings online, or in person in Uzhhorod or Kyiv.
