Leave the routine work to automation.Focus on growth.
Automate repetitive tasks and connect the tools you already use. Start with one task and see the payback first: the advisor finds a proven automation pattern and, if the numbers work, sets you up with a free audit.
We don't sell you a lie.
Most AI help sold today is a chatbot with a good prompt, or a quickly wired automation. It works on the demo. Then the first case that doesn't fit arrives, and it guesses, stops without telling anyone, or does the same thing twice. We build the system around the AI instead: where a step has to be exact, we write it as code, and the AI only chooses between tools we built and tested for you. That's how a fully autonomous agent that gets better over time becomes possible: step by step, and we tell you where the line is.
These are the methods we work with. Not every project needs all of them: we look at your case in the free audit, agree which ones it needs, and write them into the scope.
Sums, IDs, dates and system updates are done by code we write and test, not by the AI. The AI only decides what needs judgement, and only through tools we built, so it can't go off-script.
- Calculations and lookups in code, not in the prompt
- Tools with checked inputs: the AI can only call what it's given
- n8n workflows, plus custom code where n8n isn't enough
We agree a test set with you: your real cases, and the ones it must never get wrong. Every prompt, model or tool change runs against it first. If the score drops, the change doesn't ship — even when the AI provider swaps the model underneath.
- A test set of your real and must-never-fail cases
- Automatic checks, plus an AI grader with a written rubric
- Run before every change; test cases and results kept in Langfuse (EU)
Before the agent acts, a separate check sorts each case and rates how sure it is. When it isn't sure, the case doesn't fit, or information is missing, the agent stops and asks — you or your customer — instead of making up an answer. Every handover is logged, so we see what to teach it next.
- A classification step with a confidence score
- A threshold you agree on; below it, a person decides
- Approval where you already work (email, Slack, Teams, Telegram or WhatsApp) before anything is sent
The same email arriving twice is handled once, and a retry never sends a message or books an invoice a second time. If the main AI model is down, a backup takes over. When a step fails, the person who can fix it is alerted — not everyone.
- Duplicate detection on everything that comes in
- Retries that can't repeat an action (idempotency)
- A backup model from the same AI providers, and an error workflow with targeted alerts
When you correct a draft, the agent proposes one new rule. It's added only if the tests still pass and you say yes. What it has learned and remembers is written down where you can read it — not hidden inside a chat account.
- Corrections turned into proposed rules
- A rule is kept only if the test set still passes
- Long-term memory you can read and delete: an n8n data table, Google Sheets or Airtable
Each step gets only the access it needs. We use run and trace data only to monitor your system and handle incidents, then delete it after 30 days. Your business data and reports stay as long as you decide, and we delete them when you ask. Data stays in the EU where the tools allow it. Before launch and after every change, we attack your agent on purpose — trying to trick it into leaking data or doing something it shouldn't — and fix what gets through.
- Least-privilege keys for every connected system
- Traces only for monitoring, deleted after 30 days; EU data where available
- Attack tests on your agent before launch and after changes
Tap a point, or hover on a computer, to see how it works.
This level of service is normal in the US. In Estonia it isn't. We went through the local market one competitor at a time and saw prompts and quick setups everywhere, and rarely the engineering underneath: DevOps, QA, monitoring, a backup when something fails, a safe way to improve. So we built the partner we would have wanted to hire: one that starts from the tools you already use and picks the simplest one that works.
An example of what monitoring shows: every run, the retries that fixed themselves, and the cases handed to a person.
We build on tools you may already use.
No rip and replace. If it has an API, a webhook, or even just a CSV export, it can be wired in. Already on Zapier or n8n? We coexist with both.
Automations we build — and how they actually work
Each service is a real workflow you can inspect: the steps, the tools, and the data that moves between them.
Lead capture → CRM
Every enquiry validated, de-duplicated and logged in your CRM — after a human confirms.
- Webhook
- n8n
- AI agent
- Twenty CRM
- Google Sheets
Invoice & email agent
Invoices arrive by email; the agent reads them, files the data and asks you to approve.
- Gmail
- n8n
- AI agent
- Google Sheets
- Telegram
Knowledge advisor (RAG)
An assistant that answers from your own documents and says when it does not know.
- AI agent
- pgvector
- Telegram
- Twenty CRM
Payback in weeks, not faith in quarters.
Move the sliders. The same deterministic formula the advisor runs in chat.
Annual savings = hours × 52 × rate. Payback = setup ÷ weekly saving. No adjustment factors, no optimism.
Monitoring where it's worth it
Every build handles errors and recovers on its own. Ongoing monitoring with alerts is something we agree in the audit: worth it for an automation your business runs on, not needed for a weekly report. Where it's in place, we know about a stalled sync before you do.
How we monitor →From “can this be automated?” to a plan — in one chat.
Describe the task
Two minutes in the chat. Bring messy details — that's usually where the work is.
Get a plan and the payback
A concrete workflow grounded in real patterns — with annual savings and payback period.
Book a free audit
A 30-minute call. We scope the build in writing; you pay only from the pilot onward.
The questions skeptics ask.
How fast can something go live?+−
Most single-flow automations ship inside a week of the audit. Multi-step flows with custom analytics take 2–4 weeks.
Where does my data live?+−
You don't need an account, and we don't ask who you are. Chat messages are stored in our database in the EU so the advisor can follow the conversation and we can check and improve its answers; our monitoring tool (Langfuse, EU-hosted) keeps a copy for the same reason. Chats in our database are deleted automatically after 12 months. Your IP address and a Cloudflare browser check are used to keep out bots and abuse, and the IP log is cleared after 24 hours. A client record is created only after you explicitly share your details. Want a conversation deleted sooner? Email hello@aitsepp.com. To generate answers, chat messages go to an AI model provider (currently OpenAI via OpenRouter, both in the US). For your own automations you choose the model: a commercial one, an EU-hosted one, or an open-source model such as Qwen running on your own server or an EU server.
What happens when an automation breaks?+−
Every build handles errors and retries on its own, and if monitoring is in your scope, a stalled sync reaches us before it reaches you. For the first 2 weeks after launch, fixes and tweaks based on your real cases are included in the pilot price; after that, they run under the maintenance agreement.
What does it cost?+−
The chat and the audit are free. Single-flow builds land between €600–1800; you pay only from the pilot onward.
Will it work with my tools?+−
If it has an API, a webhook, or a CSV export — very likely. Already on Zapier or n8n? We coexist with both.
One conversation. One automation. One less thing to think about.
Ask the advisor, or go straight to a 30-minute scoping call. Either way, you'll see the payback in writing before you spend anything.