Make knowledge easier to find
Sam could retrieve approved information and show the source, edition and jurisdiction behind an explanation.
Review gate: verify that the cited source supports the answer.BCAM / AI IN PRACTICE
Useful intelligence earns its place in the field.
What is demonstrated, what is promising, and what still needs proof? BCAM’s approach begins with that distinction.
AI IN PRACTICE / EVIDENCE FIRST
The field is where an answer is tested.
Photos, asset identity, inspection history and source documents can help AI surface the right question. BCAM keeps the source and the responsible professional visible when an answer becomes a decision.
See how evidence stays connected →
RESEARCH SPOTLIGHT · NIST · 10 AUGUST 2022
Forecasting flashover with machine learning.
NIST and its research partners developed FlashNet to investigate whether a neural network could forecast flashover across different residential floorplans.
This is a published research example. It is not evidence that a model can safely direct decisions in any building or fire.
Read NIST’s research reportPROPOSED BCAM APPLICATIONS
Three places to begin.
Give observations structure
Field notes could become draft asset observations, with missing details highlighted for the technician.
Review gate: the person who observed the condition confirms the record.Spot questions worth investigating
Repeated faults or incomplete records could be grouped for review across Fire and HVAC assets.
Review gate: a pattern is a prompt for investigation, not a diagnosis.BCAM’S DESIGN PRINCIPLE
Sources visible. Uncertainty explicit. Responsibility retained.
These applications describe the direction of the platform. Live AI, asset connections and expert video are not connected in this preview.
Explore Sam’s proposed experience