AI implementation at a roofing company

The project started with a day on site, not with a project plan written in advance. Together with the estimating team, I reviewed dozens of possible use cases and divided them into three groups: what could be built straight away, what required a connection to other systems, and what fell outside the scope of a short project. Three concrete cases were then selected from the first group.

The solution was built using the company's own documents, including files, historical emails, quotes and reference lists. I did not rely on generic examples. This material helped shape and refine the solution throughout the project, including support for a less common type of file that only became apparent later.

Testing was carried out on a broad set of company-specific files with known outcomes. This made it possible to compare each result with a real human decision and test whether the defined rules were being applied correctly.

A working solution was delivered in just three days of work.

The three selected use cases were turned into practical tools that the team can use without having to learn a specific command or formulate a technical prompt. Every output can be traced back to its source, with clear documentation of what has been demonstrated and what has not yet been proven.

The rules and knowledge used by the three tools are defined once and shared across them. This avoids repeating the same business logic in different places and having it gradually drift apart.

This first project also established the groundwork for the next phase. The same approach, business rules and technical foundations can now be reused as I develop additional AI use cases for the company.