Got a process that should run itself?

Starting with a single process audit and a pilot, not a major contract. We demonstrate a working system before asking for your trust—this reduces risk for both sides.

Yes. We feed data into the models through a single auditable router, and we mask PII before sending it to the cloud. Sensitive pathways are handled locally (self-hosted LLM + BGE-M3). For us, security and GDPR compliance matter more than any single feature.

We don’t just advise—we build and maintain systems on our own infrastructure, backed by proof of performance (live telemetry, real case studies with metrics). We design to enable switching providers, never the other way around.

Not necessarily. We select the option based on the actual workload and budget—from small models to a cluster. What matters is predictable cost, not the maximum hardware.

Yes. We operate from Podkarpacie, but we work internationally and communicate in English. We are also interested in technological partnerships and white-label solutions for agencies.

A pilot of a single process typically takes weeks, not months. First, the smallest change with the greatest leverage, then gradually expanding the scope—with verification at every step.

We start with a fixed, low-cost audit and pilot so you see value before a bigger investment. The implementation quote is set after the audit, based on real workload and scope; we aim for predictable cost, not a surprise bill.

We don't have a fixed price list — we work in ranges that depend on scope and load. The entry point is a 4–6 week pilot at a fixed, low cost with one measurable KPI: it shows the value within a limited budget. A smaller budget usually means a pilot or a narrow deployment (e.g. an assistant on your knowledge, or one automated process); a larger one means a full production deployment with observability and backups. We set the exact scope and range after a short audit — fastest via our tools (the ROI calculator, the stack selector) or by contacting us. We don't quote fixed prices upfront, because they depend on the real scope.

Yes. We sign an NDA before accessing sensitive data and work under a clear contract with scope and milestones. Security and GDPR take priority over any feature.

Every phase closes with proof (a test, log, report), and the pilot shows a working system before we ask for trust. We ship modularly with a rollback - risk stays low and controlled on both sides.

The best first process is repetitive, time-consuming and has a measurable result — e.g. classification and data extraction (invoice coding, ticket categorization, reading data from CVs), handling the most common customer questions, or generating repetitive content. We start with one such process because the result is measurable (e.g. percentage of correct classifications) and the process already exists manually. The rule: first one to three concrete processes, then the tooling — never the other way round. Our automation finder and ROI calculator help with this.

Process automations that eat a lot of hours today pay off fastest. If a process consumes a dozen to several dozen hours a month, the deployment usually pays back in 2–4 months. For a small business it's worth it the more repetitive manual work it does; but if a process takes less than a few hours a month, it's better to start with something bigger. You can calculate your concrete return in our ROI calculator, and we start with a fixed-cost pilot so you see the numbers before a larger investment.

Yes. From August 2026 the AI Act requires transparency for limited-risk systems — the user must know they're talking to AI, not a human. That's why our assistants clearly introduce themselves as AI and can hand the conversation to a human at any time. If a system profiles customers or makes significant decisions, higher-risk obligations apply: human oversight, documentation, sometimes an impact assessment (DPIA). We design compliance in from the start, not bolt it on after.

You don't have to accept that. Model input goes through a single router that masks personal data (PII) before anything reaches the cloud, and sensitive paths run locally on our own infrastructure (self-hosted LLM and local BGE-M3 embeddings). Sensitive data can stay within the country. We choose the model and the place of processing to fit your GDPR and confidentiality requirements.

It depends on the solution. A pilot and a one-off deployment have a fixed project cost; running production systems (assistant, agent, infrastructure) usually have a maintenance component, because they're living systems we monitor, update and keep running. We fit the cost structure to scale and aim for predictability, not a surprise bill. We set the exact model after the audit.

That's the first question we ask ourselves. The assistant answers from your sources (RAG with citations), and on a weak match it says "I don't know" and hands the matter to a human instead of guessing. Output guardrails make sure it never promises what it shouldn't: prices only as ranges, no false deadlines. Hallucinations can't be zeroed out, but they can be brought down to a trustworthy level.

Yes — that's usually where the value is. Agents and automations connect to your systems (CRM, email, calendar, databases, n8n) through controlled, allow-listed tools, and irreversible actions require confirmation (a human-gate). We design the integration around your stack and don't require replacing what already works.

It doesn't have to be perfect, but tidy data improves the results. We often start with a data audit and pick a process that works on what you already have: documents, FAQs, ticket history. RAG can work on your existing knowledge without rebuilding everything. Where the data is weak, we first tidy a narrow slice for the first process, not the whole organization at once.