AI assistants and agents in your company's processes

• Companies where the same tasks repeat dozens of times a week: customer enquiries, quotes, documents, tickets.
• Customer service, sales, back-office and admin teams.

These are typical scenarios we design. They are not results from a specific client.

• Form and email enquiries: AI recognises the topic, fills in data from your CRM and drafts a reply for approval.
• Documents and invoices: AI reads the data, checks that nothing is missing and routes the document to the right system or person.
• Company knowledge on hand: The assistant answers staff questions about procedures, offers and pricing based on your documents.
• Quotes and meeting notes: A draft quote or meeting summary based on CRM data and your templates.
• Operational reports: Data from several systems in a fixed format, every week at the same time.

An assistant prepares and suggests. An agent carries out several steps on its own, such as fetching data, checking it and saving the result. The more autonomy, the more safeguards: limits, approvals and an action log.

1. Role and boundaries. We describe what the assistant does, which data it can access and what it never does without human approval.

2. Prototype on real cases. We test on real enquiries or documents from your company before anything reaches the team.

3. Launch on part of the process. One case type or one team first.

4. Expansion. More case types once results meet the success criteria.

Every action is logged, so you can see what the assistant did and why.

Chosen to fit the process: AI models from different providers, automation tools, API connections and the open Model Context Protocol standard.

If the assistant talks to customers directly, they must know they're talking to AI. We design for that from the start.

We don't build AI that makes decisions about people on its own, for example in recruitment or credit scoring. These are high-risk areas under the AI Act and need a different approach.