Websocket state, collector health, reconnect behavior, and runtime status are surfaced as operational state, not ecosystem facts.
Infrastructure surfaces
for evidence-aware
AI observability.
This registry describes SIGNALWATCH architecture boundaries: what is ingested, what is observed at runtime, what is derived, and what must remain unavailable until real data exists.
Claims need source records or ingestion events.
Confidence and detections come from inference.
Missing data stays visible instead of guessed.
Protocols do not ship with conclusions.
Research, policy, release, forum, and repository sources are normalized with timestamps and source references attached.
Detections, confidence, empty frames, and continuity markers come from browser-side model output history.
The system separates facts with sources, summaries, conceptual demos, and unavailable states.
Next.js / TypeScript / Tailwind / Framer Motion
FastAPI / WebSockets / provenance-aware storage
Docker-ready services / Railway deployment path
browser-side model runs / evidence packet export shape
papers, releases, policy pages, ingested records
browser-side detections, confidence, timing, empty frames
timestamps, frame history, continuity markers, exported JSON
missing data stays unavailable; outcomes are not prewritten
Claims need source records or ingestion events.
Confidence and detections come from inference.
Missing data stays visible instead of guessed.
Protocols do not ship with conclusions.
Operational surfaces should show where information came from and what transformation touched it.
Runtime behavior, collector state, source activity, and model-output gaps stay visible.
Perception workflows are evaluated under degraded conditions without synthesizing outcomes.
The interface stays calm and instrumented so evidence is easier to inspect.