AI business automation.

What AI business automation is, and what it is not

When people say AI, most picture a chatbot chatting with visitors on a website. That is the shop window. Here we are talking about the warehouse: AI that does the work behind the scenes, where nobody is watching and the hours quietly drain away. It reads inquiries before you do, does the retyping for you and assembles a draft quote while you do the work your company actually lives from.

The rule for choosing what gets automated is deliberately boring: work that repeats in the same shape several times a week, with a clear input and a clear output. An inquiry arrives and needs sorting; a piece of data arrives and needs entering; a quote is requested and the format is always the same. That is what AI does well. What AI does not do well are decisions: what a job may cost, whether to make an exception for a client, what the company does next. Those we do not automate, and we will not sell them to you as automation.

Hence the second rule, which applies to everything we deliver: AI drafts, a human approves. A draft the AI produces stays a draft until someone in your company reviews it and releases it. A mistake in a draft costs a minute to fix; a mistake that reaches a client costs trust, and trust is not restored with a revision.

Inquiries: from fifteen emails to one list

The most common first process is the inbox. Inquiries arrive through the contact form, by email and through referrals, mixed in with supplier invoices, notifications and spam. Every morning someone reads them one by one, opens the attachments, judges what is urgent and answers the same questions with the same sentences, only from scratch each time.

Automated intake turns that around: AI takes every incoming email, summarizes it in three lines and sorts it: new inquiry, existing client, complaint, supplier, irrelevant. Next to each inquiry stands what is being asked for, by when and which details are missing, so the morning is spent deciding instead of reading. For questions that keep repeating, AI attaches a suggested reply assembled from your approved texts and price list, not from its own imagination: you review it, adjust it if needed and send it. A reply that used to arrive the next day arrives within the hour, written in your own vocabulary.

The same logic works for messages that are not inquiries: a first draft of a reply to a review or a social media message waits ready, instead of the blank page that keeps the answer postponed for days.

Retyping: data entered once, and not by hand

The other big consumer of hours is data entry. An order arrives by email and gets retyped into a spreadsheet; data from a PDF is copied into another spreadsheet; the same client is entered a third time into a third list. Retyping does not only burn time: every copy introduces the odd error, and errors surface only when the numbers refuse to add up. We saw what that reconciliation costs on the Moj Magacin project, where the company was losing two days a month matching warehouse records against invoices before a single system was introduced.

Here AI works like a patient typist: from the free text of an email or document it extracts the fields you care about: who, what, how much, by when and at what price, and writes them into the columns of the spreadsheet you already use. It does not fill in incomplete data by guessing; it flags what is missing so a person can complete it. If your company has a CRM, the inquiry goes straight into it instead of a spreadsheet, as a card with its source and a follow-up task, the way we describe on our custom software page.

To be precise: where the data already lives in another program, the right solution is a connection between the programs, not AI. AI is the layer above, for data that arrives as free text, written differently every time, where classic programming does not help because no two emails are ever the same.

First drafts: product descriptions and quotes from templates

The third process is writing that repeats. An online store with three hundred products needs three hundred descriptions, and nobody in the company has a spare week to write them; a quote is assembled in the same format ten times a week, with only the items and figures changing. From a spreadsheet of product attributes, material, dimensions, purpose, AI writes a first draft of the description in a tone you approved, and from the inquiry data it fills in your quote format down to the last field. A draft never goes out on its own: a person reviews and publishes the description, a person signs the quote.

Because we build online stores on our own CMS, the one behind Planika and our public Demo E-Commerce CMS, this step can live as a button inside the product editor itself: the suggested description appears exactly where the description is entered, with no copying between windows.

Here too we draw the line honestly: AI writes a usable first draft, not a finished text. For ten descriptions it is faster to write by hand; for three hundred, the difference is a week of work.

Privacy: what goes into AI, and what never does

Before the first automation we agree a written list: which data may enter the AI, and which must not in any form. Inquiry content, product attributes and quote text normally may; personal ID numbers, health data, salaries and card numbers do not, and where the gist of a message is enough for sorting, personal details are replaced with placeholders before processing.

The settings we use ensure your data is not used to train third-party models, and access to what the automation records is restricted by user accounts, like every other part of the system. If your requirements are stricter and data must not leave the company at all, there are models that run on your own server; that changes the price and the capabilities, but it is feasible, and it is discussed before the project starts, not after.

What systems look like when data protection is requirement number one we showed on the clinic system: role-based access, two-factor login and records that are versioned instead of deleted. The same habit carries over to automations: it is always known what went in, what came out and who approved it.

What we measure and what this costs

Automation is not sold on impressions but on a number. Before we start, we measure how many hours a week go to work that repeats: how many inquiries arrive, how many quotes are assembled, how long the data entry takes. After the trial period we measure the same things again, on real inquiries and real quotes, so the savings show up in hours, not in impressions. If the numbers say it does not pay off, we tell you so, before you pay anything.

The price depends on the process: on how many data sources there are, in how many formats they arrive and what needs to be connected, so we do not publish a figure up front. A concrete quote is free and follows a consultation where we walk through your processes, and the price ranges for our other services are on the pricing page. The smallest first step with a listed price is an AI chatbot for your website, from €500: it is visible immediately, it gets the company used to the approval rules and it often reveals on its own which questions actually repeat.

The fastest route to a proposal: describe two or three tasks that eat your hours every week through the contact form. We reply with a proposal for what to automate first, what to measure and a concrete quote within 24 hours.