BCBC TechnologiesRepeatable model tests
Capture prompts and responses to investigate model behavior and identity claims.
AI observability · Active proof of concept
PRISM — Evidence Fusion
AI doesn't just need more intelligence. It needs better accountability.
The problem
Claims are not evidence.
AI responses may confidently describe which model answered, which provider served it, or what infrastructure ran it. Those statements alone cannot establish what happened.
PRISM explores how independently recorded gateway and execution evidence can be compared against model-generated claims.
Evidence Fusion
Making multiple AI systems accountable to independently recorded facts rather than to one another's opinions.
Proof of concept
What PRISM is investigating
Cross-model review
Compare responses and surface disagreements without assuming that a majority answer is correct.
Gateway attribution
Correlate available request identifiers, routing records and returned metadata with generated claims.
Development status
Working research, clearly distinguished from the roadmap.
PRISM 0.03
Observer and Evidence Lab experiments, prompt library, captured test responses and comparative review.
PRISM 0.04
Automated individual-model testing, deterministic evidence checks, cross-gateway comparison and provenance tracking are planned—not presented as completed functionality.
Evidence standard: A configured model name, a model's self-report and a gateway's routing record are distinct evidence types. None independently proves the identity of the model weights that served a response.
BCBC Technologies
Engineering accountability into AI systems.
PRISM is an experimental engineering initiative exploring practical AI observability, infrastructure integration and defensible verification workflows.
Discuss the work
Interested in AI infrastructure, integration, evaluation or collaborating on evidence-driven systems?