Building the Interpretive Intelligence layer for modern systems.
Signal & Prism is a new architecture that transforms raw machine signals into context, meaning, significance, and narrative. It sits above analytics and AI, giving systems the ability to interpret what is actually happening.
Prediction is solved. Interpretation isn’t.
Modern systems are overflowing with signals — telemetry, alerts, metrics, traces, behavioral logs, model outputs. We’ve built incredible tools to collect, store, and even predict from this data.
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 all means.
Domain-based worldviews at the core.
Signal & Prism models each problem space as a Domain Pack — a structured worldview containing entities, relationships, interpretive rules, baselines, and narrative templates. These packs drive a runtime that:
- Ingests and enriches signals with domain context.
- Assigns meaning and evaluates significance across domains.
- Produces narrative outputs that humans can act on.
- Stores interpretive history in an interpretive memory layer.
Early focus areas include cloud infrastructure, identity & access, workflow operations, and cost governance — domains where understanding the meaning of change is as important as detecting it.
For people who care about meaning in systems.
Signal & Prism is still early. As the architecture evolves into a platform and concrete domain packs, there will be space for conversations, collaborators, and early design partners.