AI consulting

AI agents for business processes, with your team in control

An AI agent works through a task in several steps: it reads a supplier invoice, compares it with the purchase order and delivery note, checks the ERP and hands you a finished proposal. Sortia Labs builds such agents for companies in Munich and across Germany and connects them to your existing systems. Your people make the decision.

Request a first call AI consulting

AI agents
Agentic AI
AI agents for business
Invoice checking with AI
Workflow automation

Document with a large preview and a note

AI agents

What an AI agent can take over in your company

Checking supplier invoices

The agent reads the invoice, finds the matching purchase order, compares quantities and prices and flags differences. If everything matches, the posting waits for approval; if not, accounting gets the reason with it.

Preparing quotes

A customer enquiry by e-mail becomes a draft quote: the agent picks out the requested items, pulls prices and availability from your system and writes the cover text. Sales reviews, edits and sends it.

Dunning and payment reminders

Matching open items, allowing for incoming payments, choosing the reminder level and drafting the letter. The agent prepares the batch, and a person approves it before anything reaches a customer.

Sorting and answering requests

Incoming e-mails and tickets are sorted by topic and urgency, enriched with customer data from the CRM and passed to the right person with a suggested reply.

Traceable steps

The agent logs its steps: which data it read, which tools it called and what it proposed. Every proposal stays explainable, and you see where the agent can improve.

Approach

How your AI agent gets built

1
Choose the task
Together we pick a task that comes up often, follows clear rules and still takes a lot of manual work. Half a day with the people who handle it is often enough.
2
Define the tools
The agent gets exactly the access it needs: reading in the ERP, searching the mailbox, drafting in the CRM. Actions that change data go through an approval instead of straight through.
3
Prototype on real cases
Within two to three weeks the agent runs on your real cases. You see which ones it prepares cleanly and where it reaches its limits.
4
Operation and improvement
Monitoring, model updates and a regular look at the exceptions: cases the agent could not resolve show where rules, data or instructions need sharpening.

From our own work

Software we build and run ourselves.

Fibu Bookkeeping with AIIn Fibu, our internal bookkeeping tool, a chain like this runs in daily use: the AI reads the receipt, rules check the totals, and payments from the bank statement are matched to receipts. Unclear cases go to review instead of being guessed. Omnia Document archive with AIOmnia, our document archive with AI, follows the same principle: the AI suggests title, sender, date and folder, and whatever a person changes stays the way they set it.

Questions about AI agents

What is the difference between an AI agent and a chatbot?

A chatbot answers questions in a conversation. An AI agent completes a task: it calls tools, reads data from your systems, works through intermediate steps and delivers a result, such as a posting proposal or a draft quote. Many solutions combine both.

Can an AI agent make decisions on its own?

We build agents that prepare and propose. They make no decisions about people, and actions with an external effect, such as a payment or an e-mail to a customer, are approved by a person. You set for each step how far that approval goes.

Which systems can an agent work with?

Usually anything with an interface: ERP, CRM, ticket system, mailbox, database or your own web application. A missing interface can often be added, and we build that too. Depending on the task we use models from OpenAI, Anthropic or Google and frameworks such as LangChain.

What happens when the agent makes a mistake?

That is what validation rules and approvals are for. Results that break the rules land with your team as exceptions instead of in the system. Since the steps are logged, the cause can be traced and the agent improved.

Read on.

AI automationMuch of office work consists of typing, searching, matching and forwarding.LLM integrationA language model is useful where your staff already work: in the ERP, the CRM, the ticket system or your own web application.AI document processingSupplier invoices, delivery notes, contracts and forms arrive as PDFs, scans or phone photos, and are often still typed in by hand.API developmentWhen data is typed from one program into another or sent back and forth as files, it costs time and leads to errors.AI chatbotA general language model does not know your products, price lists or internal rules.Speech to textMeetings, customer calls, dictation and voice messages hold information that rarely gets written down in full.

All about AI consulting All services and topics

Which task should become your first AI agent?

Describe a workflow that costs your team time today. We will tell you honestly whether an agent pays off or a simple script is enough.