NIST AI Risk Management Framework
Authoritative risk, measurement and governance framework.
Learn more: NIST AI Risk Management FrameworkApplied AI · Engineering practice
AI for meaning, deterministic systems for precision
Use probabilistic systems to interpret ambiguity; use deterministic systems to enforce measurable constraints and release conditions.
Two different jobs
A model may be useful for recognizing a wall, extracting a clause or proposing a category. Geometry, calculations, rules and validation need explicit constraints that can be inspected and repeated.
Keep evidence states separate
A robust workflow distinguishes observation, hypothesis, model output, validation and release. Provenance labels such as observed, inferred, assumed and human-corrected make uncertainty visible.
Blocking gates
In engineering and compliance workflows, a plausible output is not enough. Constraint checks should block release when dimensions conflict, required evidence is missing or a transformation cannot be justified.
Applied lesson
AutonomousPlan2CAD is an applied R&D example: multimodal observations feed a semantic building model, deterministic geometry and QA before an editable DXF is produced. FEMA integration similarly separates spatial alignment from engineering-document output.
Where this pattern helps
- CAD and technical-file interpretation
- Document intelligence with citation or field validation
- Geospatial overlays and coordinate transformations
- Rules-driven compliance and operational automation
Limitations
Deterministic code does not make uncertain inputs certain. It makes assumptions and failures inspectable. Domain review remains necessary where source evidence is incomplete or consequences are high.