Agents that read invoices, emails and documents, act in your systems and ask you whenever a case calls for it.
Projects from €6,000Reply within one business day
Every invoice is read on arrival: supplier, amounts, VAT and GL account.
What is certain is posted. What does not add up lands in your inbox.
Projects from €6,000. Reply within one business day.
Let's talk about your automationSupplier, tax ID, amounts, VAT and GL account come straight from the PDF as it arrives, scanned or not. Every field carries its confidence level.
If a figure does not add up or the invoice is over the agreed amount, it stops and asks you, with what you need to decide.
Post the invoice with this VAT?
The VAT charged is not 21% of the taxable base.
What came in, what was extracted, which rule applied and who approved it. You can check it whenever you want.
A supplier invoice arrives. The system reads the PDF, checks the data, asks for approval because it is over the threshold and posts it to the ERP.
*Sample data. Any resemblance to real names is unintentional.
Something like this for your company?
Let's talk about your automationClassic automations stop at the first unexpected case. An AI agent reads the context and can handle common variations (a different format, a missing field, an ambiguous request) without anyone rewriting rules; what it is unsure about, it asks.
A process can start with an email, a message or an attached PDF, with no forms to fill in. The agent works out what is being asked and carries it out in your systems.
The agent looks things up, checks them and completes the task across several systems: find the order, validate the amount, register the invoice. Each step is recorded.
When a case is unusual or exceeds an amount or risk threshold, the agent stops and hands it to the right person, with all the context ready for a quick decision.
Every correction your team makes is recorded and used to refine instructions, examples and thresholds. With use, the share of cases that need a person tends to shrink.
Every decision is logged: what came in, what the agent decided and why. High-impact actions require human approval before they run.
Finance and admin
The agent reads PDF or scanned invoices, extracts supplier, amounts, VAT and line items, matches them against purchase orders and registers them in the ERP. Anything that does not match, or exceeds an amount, goes to approval.
Customer service
Every incoming email is classified by intent and urgency, linked to the right customer or order, and arrives with a draft reply ready to review and send.
Operations and legal
The agent reads contracts, delivery notes, forms and reports, extracts the key data, validates it and files it in the right place. Nobody has to retype it.
Management
The agent gathers data from your systems, highlights the variations that matter and delivers a ready-to-read summary on the schedule you choose.
The AI works within clear limits and a person reviews the sensitive steps. You save time and quality stays under your control.
The agent only acts on its own when its confidence is above the agreed threshold. Below it, the case stops and waits for review.
High amounts, new suppliers or inconsistent data are routed to the person responsible, with the information prepared to approve or correct.
A record of what came in, what was extracted, what was decided and who approved it. You can audit it at any time.
We use providers with data processing agreements, EU hosting options and no training on your data. Only what is strictly needed is sent.
The timeline depends on scope and is set in writing in the proposal.
We review your day-to-day operations and look for tasks that are repetitive but variable, which rules handle badly. We rank them by volume, impact and risk.
We define how the agent reads each input, what it may do in your systems, which thresholds trigger human review and what gets logged. All in writing before any code.
We build the agent and test it on real examples from your company, hard cases included. You see how it behaves and approve it before it goes into production.
We deploy gradually and monitor quality, cost and escalated cases, tuning thresholds and instructions with the data from the first weeks.
Indicative price for a typical project, VAT not included. Fixed written quote after the first call.
Usual stack: OpenAI, Anthropic Claude, Google Gemini, LangChain, Python, Node.js, TypeScript, PostgreSQL, Redis, Docker, AWS, REST APIs.
Traditional automation follows exact rules: if an input does not fit the expected pattern, it fails. An AI agent interprets context. It can work with real documents and messages, even when they come in different formats or with data missing, and passes to a person whatever it is unsure about.
We design for that case. The agent works with a confidence threshold: if it is sure, it acts; if not, it stops and asks for review. High-impact actions require human approval, and every decision is logged so it can be reviewed or corrected.
We work with AI providers that offer data processing agreements (DPAs), EU processing options and a commitment not to train their models on your data. We send only the information each task needs and, where the case requires it, we can use models hosted on your own infrastructure.
High-volume, repetitive tasks that require interpreting something: invoices, emails, documents, customer queries, reports. If today they depend on a person reading and deciding, they are usually good candidates: the agent prepares the work and a person reviews what is unclear.
It depends on the scope and the systems involved; the timeline is set in the written proposal, with milestones. We start with a well-defined case, prove it works on your data and expand from there.
No. The agent connects to what you already use (ERP, email, CRM, shared folders) through their APIs or, where there are none, through custom integrations.
Tell us which process eats the most hours or causes the most errors. We will explain in writing how an AI agent would handle it, which controls it would have and what it would cost.