Trellum · Practice 02 · AI Engineering
AI Engineering
Building AI tooling into FM workflows. Custom GPTs trained on the contract or the regulation. Prompt libraries calibrated to the team's role. Agentic workflows that turn unstructured operational data into structured outputs the team can defend. Automation pipelines that sit underneath the work the practitioner is already doing.
Built on whatever platform the team already owns — Microsoft 365, Google Workspace, the CAFM, custom integration. No vendor capture.
Built into the platform you already own
Microsoft 365 environments — Copilot, Power Automate, the Power Platform, custom GPTs lodged in the team's tenant.
Google Workspace environments — Gemini, AppSheet, Apps Script.
CAFM and operational systems — direct API integration, working registers, automation pipelines.
Trellum has no platform interest in the answer. The recommendation can honestly conclude "don't replace your CAFM" — which a CAFM vendor's advisory cannot.
Where AI Engineering applies
PFI. Custom GPTs trained on the operative paymech. Agentic workflows that surface deduction exposure from CAFM exports.
Block management. Custom GPTs trained on BSA Part 4 and the HRB regulations. Evidence-mapping workflows that produce the structured record the regime requires.
Offerings
FM AI Engineering · FM Digital Adoption.
Start a conversation · How Trellum works