Guides to implementing artificial intelligence in your company

Automation, agent or application

Automation, AI agents and applications solve different problems. This is how I choose based on the task, not the product label.

Customer service agents

A customer service agent resolves enquiries at any time. Its value depends on knowing when and how to hand over to a person.

Intelligent document processing

AI can read invoices, contracts or case files and turn them into structured data. I explain how I design it so the result is reliable.

What RAG is and how it works

RAG lets an AI model answer from your company’s documents. I explain how it works and what makes its answers reliable.

Automatic opportunity qualification

AI can analyse every incoming request, score it and assign it to the right person. I explain how to design it so the team trusts the result.

What it takes to integrate AI

Integrating AI isn’t giving people a chat window. It means bringing capabilities such as interpreting or searching into existing processes, with data, permissions and oversight.

AI agents for estate agencies

An AI agent can respond to every enquiry instantly and qualify it. I explain where its work ends and the sales agent’s begins.

An assistant that knows your documents

A private assistant on your documentation starts with your team’s real questions, not with connecting the whole archive.

Corporate knowledge with cited answers

An AI knowledge system is only reliable if every answer points to its document, with title, date and permissions respected.

An AI assistant for professional firms

An assistant that searches the firm’s documentation saves a lot of time, as long as it respects each client file’s permissions and leaves the decision to the professional.

Privacy and data in AI projects

Before an AI system processes documents or client data, you need to know which provider handles them, where and for how long.

Before implementing AI in a process

You don’t need to arrive with a technical solution. Here is the information I need to propose something that works in your operations.

What to measure before automating

Without a prior measurement, nobody can say whether an automation has paid off. This is what I measure before starting.

Whether an automation pays off

Development is only part of the cost. This is how I calculate the return on an automation with visible assumptions and comparable scenarios.

Where work stalls between systems

When nobody knows the status of a request, the problem usually lies in the hand-off between systems. This is how I locate it before proposing a solution.