Invoiced the day of delivery.Every proof of delivery read and matched.

A first version of a simple app runs within hours, a complete one within days, depending on the systems you connect. In the AI Bootcamp your teams build their first apps with us in five days and roll them out.

Yes. There is no connector catalogue to wait for: each connection is built for your system through REST or MCP, including ERP, CRM, EDI, portals, older and in-house systems. Connections are set up once in the platform, and your apps read and write through them with the access rights you set, without secrets in the code.

Only if you decide so. Operaide runs on your own infrastructure, and for each step of an app you choose the AI model: a local model on your own hardware for data that must not leave, or a frontier model in your cloud tenant. An app can also use a local model to remove personal data before anything reaches a cloud model. Every run is traced, so you can see which data went where.

Yes. The apps process requests, orders and documents in parallel, so peaks such as a busy season or the hours after an outage don't need extra staff. Each app runs in its own process and can get its own Docker container, for maximum scalability. Your team only handles the cases the app flags.

Yes. The apps read emails, PDFs, Word and Excel files, scans, faxes and photos, including tables, handwriting and stamps. How well depends on the AI model behind the task, and today's models are very good at it. Documents the app is not sure about are flagged for a person to check.

PDF and scanned invoices, e-invoices such as XRechnung and ZUGFeRD, EDI messages, bank files, delivery notes and emails with attachments.

The app matches them line by line and across several documents, and shows what is still open. Unclear cases go to a person.

Domain experts build no-code in the App Builder, and developers extend the apps in code in Operaide Studio. IT keeps control of AI models, data and access. If you like, our engineers build the first version with you, for example in the AI Bootcamp.

Commercial, European and self-hosted AI models: OpenAI, Anthropic, Mistral, Azure, Google, Amazon Bedrock, OpenRouter and more, or open-source models via Ollama or LM Studio. Your administrators configure them, and every step of an app can use its own AI model, chosen for its task.

Only where you allow it. Typically it books clean matches within your tolerances, and your finance team releases the rest and decides on differences. Every step is traced, so you can show who released what.