Two expensive problems share the same data

General contractors lose time scheduling inspections, handling corrections, and keeping the paperwork trail complete. They also lose money when a subcontractor repeatedly fails work that has to be reopened.

InspectPilot handles both sides. On the surface, it is a concierge agent that files and schedules building inspections with the Los Angeles Department of Building and Safety. Underneath, it is a compliance engine that scores contractors on how they actually perform.

The same records used to move a permit forward can also improve the next hiring decision.

The evidence base is roughly 11 million records

The engine runs on roughly 11 million public inspection records covering every Los Angeles inspection over 13 years. It looks at first-pass rates, violations, and permit history.

That does not make the score a verdict. Different project types and scopes need context. But it replaces an uncheckable reputation claim with a record that can be inspected.

The useful AI work begins after the records are collected: matching entities, normalizing inconsistent names, connecting permits to contractors, and turning a large public archive into a reviewable signal.

Document automation should improve the next decision

Many AI projects stop after a document is created. InspectPilot uses the document trail as an operating dataset.

Inspection requests, corrections, permits, and outcomes are not just paperwork to finish. Together they form a history of who did what, where the process failed, and which contractor consistently got work through review.

Finance firms can use the same idea with reconciliations. Legal teams can use it with clause changes and review outcomes. Estimators can use it with bid revisions and change orders. The recurring document becomes evidence for the next decision.

A checkable signal is stronger than a confident answer

InspectPilot does not ask the user to trust a generated paragraph. It exposes the record behind the signal.

That matters because AI can make weak evidence sound certain. A reliable system should do the opposite: make uncertainty visible, preserve the source, and let a person inspect the path from record to recommendation.

Reputation is what a contractor says. First-pass rate is what the public record says. One of those is checkable.