· New architecture for meaning in machine systems

Building the Interpretive Intelligence layer for modern systems.

Signal & Prism is building interpretive infrastructure—turning raw machine signals into contextual, explainable understanding. It sits above analytics and AI, enabling meaning to be shaped by human domain knowledge rather than inferred by opaque systems.

Domain-authored interpretation Runtime interpretive loop Human-aligned understanding over time
Or just follow along as Interpretive Intelligence takes shape.
Signal & Prism — Interpretive Intelligence Infrastructure • Patent Pending
Interpretive Loop
Signals → Interpretation → Understanding
Signal Layer
Events, logs, metrics
Raw telemetry from cloud platforms, identity systems, workflows, and operations enters the system as signals.
Cloud events
Identity activity
Operational traces
Interpretation Layer
Context & significance
Domain-authored interpretive frameworks shape how signals are understood—providing context and evaluating significance in a way that reflects real-world intent.
Domain frameworks
Alignment & drift
Cross-domain views
Understanding Layer
Human-ready output
The system produces explainable understanding that describes what is happening, why it matters, and how meaning evolves over time.
Explanations
Summaries
Guided decisions

Interpretation is not inferred. It’s designed.

Why now

Prediction is solved. Interpretation isn’t.

Modern systems overflow with signals — events, alerts, metrics, traces, behavioral logs, and model outputs. We’ve become excellent at collecting data and generating predictions.

What’s missing is a dedicated architecture that can say: “this is what it means, in context, and this is why it matters.”

  • Analytics describe what happened
  • AI predicts what might happen
  • Observability alerts when something breaks
  • Signal & Prism interprets what it means
Interpretive Intelligence Architecture
Built to sit above analytics & AI
Designed for complex, signal-rich domains
Architecture

Domain-based interpretation at the core.

Signal & Prism models complex problem spaces using domain-authored interpretive frameworks. These frameworks capture how experts understand entities, relationships, baselines, and change—without hard-coding meaning into models or pipelines.

Domain expertise → infrastructure

Modern AI systems fail not because they lack intelligence — but because the domain expertise that gives signals meaning lives in people, not systems. Signal & Prism turns domain expertise into infrastructure: knowledge is explicitly authored, versioned, and governed as part of the system — so meaning remains consistent, reviewable, and governed even as models change, workflows evolve, and humans step out of the loop.

  • Expertise is captured, not implied
  • Judgment is formalized, not absorbed by operators
  • Meaning is controlled upstream — before systems act

At runtime, the system:

  • Separates signal ingestion from interpretation
  • Applies domain context to evaluate meaning and significance
  • Produces explainable, human-ready understanding
  • Maintains continuity of interpretation over time

The goal is simple: meaning should be explicit, governable, and reviewable— especially in domains where context matters more than prediction.

Signal & Prism can operate in advisory mode — interpreting signals and delivering governed understanding without taking action or changing existing systems.

Stay close

For people who care about meaning in systems.

Signal & Prism is early-stage and intentionally focused. We’re engaging with thoughtful builders, operators, and design partners who care about trust, context, and human-first systems.