Our data is confidential. Where is it processed?

This is the first question to ask, and the answer depends on your requirements. For standard data, we use providers who contractually commit not to train their models on what you send them. For sensitive data, we can run an open model on infrastructure hosted in Switzerland, without any information leaving the country. The second option costs more, and we will tell you frankly whether your situation justifies it.

What happens when the AI makes a mistake?

The system is designed for this. Cases where the model is not confident go into a human validation queue instead of being processed blindly, and every decision is logged with its input data. You can therefore find the root cause of an error, correct it, and adjust the behavior. Automation that does not account for errors is not serious automation.

How much does model usage cost?

Providers charge pay-as-you-go based on the volume of text processed. This cost is separate from our fee and generally remains modest compared to the time saved. We estimate it during scoping based on your actual volumes, and set up spending caps to prevent any budget overruns.

Do we need to change our current tools?

No, and doing so would even be counterproductive. Automation connects to what you already use, via APIs or existing connectors. Projects that require changing tools usually fail because resistance comes from work habits, not technology.

How long does implementation take?

A targeted workflow can be implemented in two to four weeks, including the observation phase. A broader pipeline involving multiple systems and teams takes longer. We always start with a single, measurable process rather than an overhaul of everything.

How is return on investment measured?

Before building anything, we calculate the time actually spent on the task and its monthly volume. This initial measurement serves as the baseline. After deployment, the comparison is factual, allowing you to make an informed decision on whether to expand or stop.

What if the AI provider changes their terms?

This is a real risk in a fast-moving sector. We build workflows with an abstraction layer between your process and the model, so switching providers is a matter of adjustment rather than a rebuild. Your data and business logic remain yours.

Will AI replace positions in my company?

That is not what we see in the projects we deliver. What disappears are hours of data entry, sorting, and copying that no one wants to do. The teams involved gain back time for tasks requiring human judgment. If your goal is explicitly to reduce headcount, it is best to say so upfront, as it changes how the system is designed.

AI Automation - Intelligent Workflow Automation

What is AI automation?

AI automation goes beyond simple rule-based automation. It uses artificial intelligence to handle complex tasks requiring understanding, context, decision-making, and adaptability.

Whether processing unstructured data, generating content, analysing sentiment, or making intelligent decisions, AI automation transforms manual workflows that couldn't be automated with traditional tools.

What you get

Intelligent Workflows

AI-powered automation that understands context, makes decisions, and adapts to variations in input data.

End-to-end Automation

Connect multiple systems, process data, and trigger actions across your entire tech stack automatically.

Massive Time Savings

Free your team from repetitive tasks. Hours of manual work reduced to a matter of seconds.

Unstructured Data Processing

Handle emails, documents, images, audio. AI understands and extracts what matters.

Human Augmentation

AI handles the tedious work; your team focuses on high-value decisions and creativity.

Fully Customisable

Every workflow is tailored to your specific processes, tools, and business rules.

Real-world examples

Here are a few concrete use cases where AI automation makes a real difference.

Automatically extract and structure data from invoices, receipts, or contracts in any format

Classify and route customer support emails based on sentiment, urgency, and topic

Generate product descriptions or personalised marketing content from raw specifications

Monitor social media mentions and automatically reply to common questions or flag critical issues

Analyse and summarise long documents, extracting key information and action items

Monitor competitor prices and automatically adjust your strategy according to defined rules

When AI is not the answer

The topic is trending, which leads to many projects being launched for the wrong reasons. We prefer to be clear about what works and what does not.

AI adds value when there is sufficient volume and the task requires understanding unstructured content: hundreds of invoices in different formats, emails to sort based on their actual topic, documents from which information must be extracted that is never in the same place. In these cases, the gain is immediate and measurable.

However, if your process follows fixed rules and the data arrives already structured, traditional automation will do the same job for far less money, with an identical result every time. And if the task occurs three times a month, the design time will never be recouped: doing it manually remains more practical.

Our first step is therefore always to measure the real volume and time actually spent. We regularly conclude that there is no project needed, or that a simple script is enough.

Reliability and supervision

A language model makes mistakes sometimes, and it does so with confidence. This is the main objection to AI automation, and it is legitimate. The answer is not to promise infallibility, but to design the system with error handling in mind.

We therefore proceed by confidence levels. When the model is confident and the automated checks pass, processing continues on its own. When there is doubt, the case goes into a human validation queue rather than being handled randomly. Every decision is logged, along with the input data and the output produced, making it possible to trace errors and make corrections.

Deployment also happens gradually: first in observation mode, where the system makes suggestions without taking action and you compare its answers to yours, then autonomously on cases where accuracy is verified. You know what the machine is doing before handing anything over to it.

The tools we connect

Automation is only useful when connected to what you already use. You do not need to change tools to benefit from it.

Outlook / Gmail

Slack / Teams

Bexio

SharePoint / Drive

SQL databases

APIs and webhooks

Notion / Airtable

Shopify

How does it work?

01

Process Analysis

We identify your repetitive manual tasks and assess which ones can be automated with AI.

02

Flow Design

We map the automation flow, define triggers, AI processing steps, and actions.

03

Implementation

We build and integrate the AI workflows with your existing tools and systems.

04

Testing & Optimisation

We test, fine-tune accuracy, and optimise performance before full deployment.

Ready to automate smarter?

Let's talk about your repetitive tasks and explore how AI can transform your workflows. We offer a free initial consultation to assess your automation potential.

Book a free consultation

Automate the unautomatable