We do business process automation with artificial intelligence: we design and develop workflows that connect software, data, documents and operational decisions. The goal is not to automate everything, but to remove friction where the process is repeatable, traceable and measurable, and to leave people the choices that require judgement.
This page describes how we work on a single process, from analysis to maintenance. If you are looking for an overview of what can be automated, department by department, and which tools to choose, start from AI automation for businesses.
Which process should you start from?
From a concrete process, not from a tool. The most common starting points are managing sales contacts, quote requests, support tickets, incoming documents such as invoices and orders, internal requests between departments and periodic reports. Together we choose a first process that repeats often enough to be worth the effort and whose result is easy to check. The first project also builds trust: a first automation that is small, useful and controllable convinces the team more than a big project that arrives months later.
How do we analyse a process before automating it?
We start from the real process, not from how it is written in the procedures. Together with the people who run it every day we observe the activities, the order in which they happen, the data needed at each step, the people responsible and above all the exceptions: the supplier who sends invoices in a different format, the customer who writes on two channels, the case that stalls because a signature is missing. We also measure the starting situation: volumes, average times and errors, so that at the end we can say whether the automation worked. The result is a process map in which every step falls into one of three groups: what can be automated, what needs a human check and what must remain an explicit decision by a person.
How do we build the workflow?
We build workflows connected to the company's stack: CRMs, management software, document management systems, email and certified email inboxes, website forms, databases and external services. Where fixed rules are enough we use simple automations, which cost less and are more predictable. Artificial intelligence comes in only where a text has to be interpreted, a request classified, data extracted from a document, a reply prepared or several activities coordinated. Every workflow has explicit intermediate states, such as "received", "in review", "approved", "booked", so at any moment you know where a case is and why.
An example: from quote request to draft in the management software
An illustrative example, not a client case.
- A request arrives by email or from the website form.
- The workflow recognises it as a quote request and extracts customer, products, quantities and deadline.
- It looks up the customer in the CRM: if the customer exists it updates the record, if not it creates one.
- It prepares a draft quote in the management software with the current price lists.
- If a piece of data is missing or the model is not sure, the case goes to a review queue with a note on what is missing.
- A salesperson checks the draft, decides the final price and sends the offer.
- Every step stays in the log: what arrived, what was extracted, who approved.
The salesperson stops retyping data and spends their time on the part that matters: the price and the relationship with the customer.
How do you keep an automated workflow under control?
A useful workflow must be observable. For each run we log the input received, the decisions made by the model with their confidence level, the actions performed and any approval, with date and author. Notifications alert the right people when a case gets stuck or an integration stops responding. Confidence thresholds decide when a case proceeds on its own and when it goes to a person. Approval steps are mandatory when the automation touches sensitive data or performs actions that cannot be undone. Finally, there is always a manual route: if the automation stops, the work continues by hand, with the instructions written in a short operations manual.
What do you get?
- Faster processes that depend less on copy-and-paste, internal emails and manual steps.
- Explicit, documented operating rules that are easier to improve over time.
- A technical foundation ready for AI agents, future integrations and measuring operational indicators.
- The process map, before and after, with the starting measurements.
- The workflow in production, with a run log and a review queue.
- Technical documentation and the operations manual for your team.
How does maintenance work?
Processes change, and automations change with them. After go-live we check the errors and the cases that ended up in review, update the integrations when the APIs of the connected software change and check accuracy when switching to a new AI model. At agreed intervals we review the numbers with you: times, volumes, corrections. From there we decide whether to adjust the thresholds, extend the workflow to new cases or move on to another process.
Frequently asked questions
- What does business process automation mean?
- It means designing and building digital workflows that carry out repeatable activities across different tools, with clear rules, controls and integrations with the systems the company already uses.
- Is AI always needed in a workflow?
- No. AI is needed when content has to be interpreted, requests classified, replies generated or decisions made that depend on context. Where fixed rules are enough we use simpler automations.
- Where do we start?
- From a concrete process: for example contact management, tickets, documents, quotes, internal requests or reports. We analyse it and define a first automation that is useful and verifiable.
- How long does it take to automate a process?
- It depends on the number of systems to connect and the variety of cases. We estimate it after the analysis, and the estimate includes a shadow period in which the automation runs in parallel with the people.
- What happens if a connected software changes?
- Monitoring flags the error, cases switch to the manual route and we update the integration. That is why maintenance is part of the project from the start.
Related services
- AI automation for businesses: examples by department, tools, risks and limits.
- Business chatbots for customer support: answers based on your content.
- MCP servers for AI agents: the layer that connects agents to your tools.
Tell us about the process to automate
Describe how it works today, how often it repeats and which tools it involves. We will reply with a first assessment and, if it makes sense, with a proposal for a first automation.