Artificial intelligence and process automation

Staff spend hours on repetitive operations: moving data between systems, processing documents by hand, answering the same requests. It's slow, expensive, and error-prone — and you're the one answering for missed deadlines and mistakes. AI and automation close exactly those gaps — but only when they're deployed for a specific task, not "because everyone else is."

Integration of language models and AI agents into your processes. Automatic document processing (recognition, data extraction, validation). Automation of document flow and operational chains. Computer vision and predictive models for business tasks. Connection to your accounting systems.

AI and automation cut labor costs — and we carry that saving into your estimate, not into our margin. Before deploying, we calculate the ROI: exactly what comes back, and over what period.

We start with a free assessment: which areas are actually worth automating and which aren't (often it turns out you need simple automation, not AI — and we'll say so plainly). Then a prototype on your scenario, then deployment in stages. All on the "10% Path" → How we work.

An AI agent isn't just a chatbot — it's a system that carries out a chain of tasks: it reads a request, finds the data, performs an action in your system, and passes the result on. We build this kind of automation on the principle of managed autonomy: the agent takes on the routine steps, but for risky actions (a payment, a large refund, sending to a client) there's always a point where a human confirms the decision (human-in-the-loop). This is a deliberate rejection of the trendy but dangerous "fully autonomous agent with no oversight" — because per Gartner, more than 40% of such projects risk being scrapped due to uncontrollability and unclear ROI. Where agents truly pay off and where it's hype — we break it down honestly. A separate matter is AI-agent security: an agent with the rights to act becomes a new point of vulnerability, and we build in access control from day one.

LLM integrations · AI agents (n8n, orchestration, MCP) · RPA · computer vision · predictive models · data pipelines. (see the plain-language glossary — in the FAQ below)

Free assessment and ROI calculation → prototype on your scenario → deployment in stages. The cost is fixed before we start.

The method we use to calculate BEFORE deploying — plug in your own numbers:

1. Operation volume: how many times per month it runs (N) × average time per one (T, min).
2. Current monthly cost: N × T × (employee hourly cost / 60) + the cost of errors (rework, fines, lost clients).
3. After automation: what share of operations goes to the bot/model (usually not 100% — part stays with a human reviewer) and how much manual time remains.
4. Monthly savings = current cost − residual manual cost − cost of running the solution.
5. Payback period = cost of deployment / monthly savings.

Honesty rule: if by this calculation the payback runs longer than a reasonable period or the automation share is low — we don't recommend deploying. Sometimes the conclusion of the calculation is "automation won't pay off here, leave it as is."

Document flow is manual, reporting takes ages and comes out with errors, the systems aren't connected. How we usually solve this: automatic processing of incoming documents with data extraction and entry into the accounting system; ROI is calculated before deployment, based on time saved and fewer errors.

• Where do we start if we're not sure we even need AI? With a free analysis. Often you need simple automation, not AI — we'll say so plainly.
• How do you calculate ROI? By time saved and fewer errors on a specific process. We do the calculation before deployment.
• How does RPA differ from an AI agent? RPA is a rules-based bot; an AI agent, built on a model, carries out chains of tasks.
• Are AI agents reliable or just hype? Both: on narrow tasks with a human in control they pay off; on "full autonomy with no oversight" they often fail. We build the former. In detail — in the article on agentic automation without the hype.
• Is our data safe? Security by default: access control and protection from day one; hosting in the cloud or on-premise per your requirements.