We connect your CRM, messaging apps, databases, ERP, and LLMs into one workflow, with no manual handoffs between systems. We build on n8n or write it fully custom — whatever the job needs. Once it ships, your team owns the logic.
Teams burn hours a day moving data by hand between email, the CRM, and the accounting system. Every extra hand-off is a lost lead or a new mistake. Automation cuts out the manual steps.
Leads come in from Telegram, WhatsApp, email, Instagram, and site forms, and some slip through the cracks. A rep sees the inquiry in the morning, after the customer already went to a competitor.
Invoices, contracts, delivery notes, resumes. Someone copies fields from a PDF into the system of record by hand: 15–30 minutes per document, ten a day — up to five hours gone.
Friday night, an analyst pulls data from ERP, Excel, and GA4 into a dashboard. Management sees the numbers a week late and makes decisions on stale data.
HR and accounting explain how to file for leave or pull a document ten times a day. Across a day, those answers add up to an hour torn out of real work.
A process runs from a trigger or schedule to a record in the target system on its own, with a human only on the edge cases.
AI teammates work in Telegram, web chat, or Slack: access to the knowledge base through RAG, to your systems through APIs.
Conversational AI agents and chatbots for customers are a separate service: AI agents & chatbots.
Site → bot → CRM chains where AI walks the customer from the first touch to the deal.
Best fit: B2B SaaS, EdTech, real estate, the premium segment.
We match the architecture to the job: low-code on a ready platform, fully custom development, or a mix — orchestration on a proven engine plus custom services for the critical parts.
When a workflow needs to "read" unstructured data — a query, a document, a ticket — we add RAG: vector search over the knowledge base plus an LLM with context.
Every B2B automation comes down to two questions: what if it makes a mistake, and what if the data leaks. The answers to both ship in the base build, not in a separate package. The OWASP Top 10 for Agentic Applications (2026) names excessive agent permissions and supply-chain compromise among the top risks.
Every workflow spells out what it does on its own and what needs a person. High-risk actions — a payment, a deleted record, an important email — go through human-in-the-loop: the task lands in a queue, and the owner confirms.
Every node validates its input. Wrong structure — it stops with an alert instead of failing silently. Retries on HTTP errors. Deduplication so the same ticket or lead never gets processed twice.
Every LLM response gets checked: JSON format, no personal data, a confidence score. Low confidence → escalation. That keeps the risk of hallucinations in production close to zero.
Every run is logged: inputs, sources, decisions, result, timing. The logs are available in real time and kept for compliance.
Six automation case studies with measurable results. The full catalog lives in the Case studies section.
Every number comes from a project we've already shipped. Not industry averages — real results.
Numbers from our projects: Telegram HR bot, HR AI Assistant, Retail Analytics, AI Sales Funnel, Job Scraper.
Open a stage to see the details.
We map your processes and count how many hours a week each one burns. We find the bottlenecks and form automation hypotheses. If there's nothing worth automating yet, we say so: automation won't fix a chaotic process.
We agree on the stack, the integrations, and the success metrics. We define the workflow's boundaries: what it does, what it doesn't, how it escalates. We confirm which actions run autonomously and which need human-in-the-loop. Then we sign off on the technical spec.
We build the workflow, wire up the integrations, train the RAG layer on your knowledge base, and test it on historical data. We adjust iteratively after each run.
We launch on a limited slice — 10–20% of real traffic. We watch the metrics, adjust how the workflow behaves, and ramp the share up gradually. Bugs get caught in a controlled loop, not across the whole flow.
We hand over the documentation and train your team to run the workflow. After that, your team can change the logic on its own. Support runs on the package you pick.
Every price is locked at the time you book. Anything beyond it is an agreed change of scope, nothing else. The ROI calculator estimates payback on your own numbers in a minute.
Optional on Start, standard from Business up. We own the new workflows, error monitoring, and updates.
at 10 hrs of busywork per week per person, €14/hr · An estimate, not an offer
In five years of automation work, we've seen the same mistakes across dozens of projects. They're almost never about the technology — they're about how you approach it. We flag them with you early and design around them.
The most common one. If a process isn't documented and works badly with people, n8n won't fix it. First we check: is the process repeatable? are the metrics measurable? is the data available? If not, we put it in order, then automate.
A workflow that tries to handle every case at once lands at 60–65% accuracy. Three specialized ones, each scoped to a specific task, hit 90%+. We decompose, every time.
A workflow that goes straight to production at 100% of traffic catches its bugs in the open. A pilot on 10–20% is required, not optional.
A workflow with no explicit human-in-the-loop on critical actions will eventually fail where a mistake costs real money. There's always an owner.
Six months on, a workflow with no documentation becomes a black box. By contract, you get the documentation, access to the source code, and team training.
A workflow needs monitoring. Knowledge bases go stale, provider APIs change, business logic drifts. With no upkeep, it degrades. Support isn't required, but it's a good idea.
Eleven questions we get the most. Don't see yours? Message the CTO or CEO directly.
It's handing repetitive operations between systems over to software: on a trigger or schedule, it pulls data, processes it by rules or with AI, and writes the result into the target system. The goal is to remove manual data entry, speed the process up, and cut the error rate.
Any repeatable process with available data. In practice, most often: document automation and recognition (invoices, contracts, resumes), sales and marketing automation, lead generation and CRM work, reporting, system sync and integration, routine HR and IT requests from staff. What you shouldn't automate: the rare, one-off cases where every situation is unique.
Classic RPA is "screen robots" that mimic a person's clicks in the interface. Brittle: a button moves and the robot breaks. We work at the data and API level: an n8n workflow with an AI layer hits the systems directly and makes decisions instead of clicking pixels. It's more reliable and it actually understands context. We add RPA only where a system has no API.
Three things. Self-hosted: your data never goes to a third-party provider, which matters in regulated industries. Cost: Zapier charges per task run, while self-hosted n8n only costs you the server (from €5/mo on Hetzner or DigitalOcean). Flexibility: n8n lets you write arbitrary JavaScript inside a workflow. The downside: a higher barrier to entry — n8n needs basic technical understanding, where a manager sets up Zapier in half an hour.
Yes. We connect through REST/HTTP APIs, with data exchange via the database and file gateways. In most projects, financial operations are best left inside the ERP — it lowers the risk of errors and keeps the audit simple. The workflow plugs in "alongside": it reads data and sends write commands through explicit gateways. We support SAP, Oracle, Microsoft Dynamics, NetSuite, Odoo, and any other system with an open API.
The workflow keeps running in degraded mode. The LLM node returns an error and a fallback kicks in: the task goes to a person, or to an alternate provider if one is configured. For example, Claude as primary, Gemini as backup. The switch is automatic, on a timeout or an error code.
For most SMB projects — €50 to €500 a month at typical load. You pay the token cost directly to the provider (OpenAI, Anthropic, Google); we don't sit in the middle of those payments. We give an exact estimate at the architecture stage, based on conversation volume and average context length. For self-hosted models, it's GPU infrastructure cost instead of tokens.
Yes, and it's part of the base build. We hand the project over with full documentation and train your person to run the workflow and integrations. From there you can edit the logic yourself. You can keep support on our side as a safety net (monitoring from €250/mo) or pull us in only when you need it.
Self-hosted on your servers by default. Document-level access control, tenant isolation in the RAG layer, an audit trail on every workflow, human-in-the-loop on critical operations. GDPR-compliant. If your compliance officer has specific questions, we're ready to answer them.
Automation is a process: data follows a predefined route from a trigger to a record in a system. An AI agent is a conversation: a bot talks with a person, answers questions, carries a thread. In practice the two often work together. Conversational AI agents and chatbots for customers are a separate service: see AI agents & chatbots.
Usually with whatever annoys your team most: document handling, manual data entry, weekend reports, routine HR questions. The exact priority comes out of the audit: we count the hours and the revenue, then rank by ROI. Sometimes the most irritating task isn't the most profitable one to automate, and the other way around.
We'll go through your processes, find 2–3 automation points with fast ROI, and send a short report. We won't chase you.