Atlas PM
An operations platform for commercial moving & logistics, with an embedded AI operator.
Atlas PM was designed from months of field research inside a real commercial moving and logistics operation. We built the full platform — field walkthroughs, quoting, client e-signature, job execution, automatic invoicing, warehouse and payroll — and validated it end-to-end against demonstration data. It has never run a live operation. That is the next test this research points to: hardening the confirm step described below from a prompt-level instruction into an independent backend state machine, before anything real touches it.
Read across every module
Atlas answers questions over jobs, quotes, invoices, warehouse and payroll — with tenant isolation enforced at the tool layer, so the AI cannot cross data boundaries by construction.
Proactive scanner, coded rules
Business rules run against the live schema — overdue invoices, jobs pending confirmation, unapproved timecards — each with its own threshold and severity, surfaced without being asked.
Bounded tool-calling loop
A hard cap on iterations, explicit handling for rate limits and permission errors, required-field and format validation before anything reaches the database.
Two-step-confirm writes
Every write action returns a preview first and executes only after explicit human confirmation. That is the lab’s thesis — AI as a partner, not a slave — implemented in a full operations platform, not a toy example.
The full lab note — designing an assistant that acts, but only with permission, and the honest limit of confirm-by-prompt — is published in the research section.
→ Lab note: Embedded AI in a real operation