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Methodology

How Mosaic works

Mosaic turns the world's public information into analyst-grade intelligence. With AI, speed is table stakes. Every product can summarize the news fast. What separates intelligence from aggregation is nuance against baselines: knowing what's unusual, what changed, and why it matters.

Live system statusEvery number on the site links back to the signals that produced it.

The engine

Five stages, every item

1CollectWorkers poll 500+ sources on their own cadence. Breaking-value Telegram channels every two minutes, ship (AIS) and aircraft (ADS-B) sensors continuously. Every source carries a credibility tier and a state-affiliation label.
2EnrichEach item is translated if needed, then structured by an LLM against a fixed analytic schema. Claim status, confidence, significance, geography, entities, market bias. The prompt enforces tradecraft: say what changed, name specific places, separate claims from corroboration, no filler.
3Corroborate & clusterAn engine matches the same event across multiple outlets, credits the first reporter, and adjusts source credibility over time. It never upgrades an evidence status from outlet counts. Related signals cluster into Situations. One synthesized headline carrying the full multi-source story.
4Baseline & detectFor every actor, source, and strategic zone, Mosaic keeps rolling 30-day baselines and recomputes deviations every 15 minutes: coverage spikes, sources going silent, ships going dark in chokepoints. And fusion alerts when a physical and a narrative anomaly align in the same place and window.
5ConnectAn entity graph links every actor to its evidence. Who appears with whom, which markets react, which situations involve them, what changed this week.

Trust

How every score is computed

Mosaic Score (1–10): a weighted composite of how credible the source is, how significant the event is, the model's confidence, and how well the core the model rates the claim. It is a priority for ranking, not an evidence status or a measure of truth. It always sits one hover from the reasoning and one click from the sources.

Source credibility is earned, not assigned. Sources start at an editorial tier, then (once they have enough history) are continuously re-blended by their corroboration rate: how often their claims are later reported by other outlets. Reliable sources rise; sources that publish unconfirmed claims sink. Capped at 0.98. Nothing is ever beyond question.

Evidence status is computed from the evidence, never from the model's own label or from outlet counts. Reported: at least one admissible published item supports it. Corroborated: at least two separate source groups support it, with syndicated, duplicate and shared-origin copies counted once. Confirmed: corroborated, plus a primary, official or documentary source. Verified is reserved for deterministic checks and is never assigned because news sources agree. Missing, conflicting, stale or failed evidence reads Unknown. A Situation never shows a status stronger than its weakest key fact, and confidence is capped by the evidence status.

Anomaly severity (0–10) is driven by the size of the deviation from a subject's own 30-day baseline, log-scaled and volume-damped. A 10× spike on 6 mentions is interesting; a 10× spike on 60 is an event. Fusion alerts (aligned physical + narrative indicators) take the highest component severity plus a bonus, because they are the strongest class of early warning.

Design principle: scores compress evidence. They never replace it.

Differentiation

What makes it different

Baseline-aware, not headline-aware

“Iran is in the news” is noise. “Iran coverage is 13× its 30-day norm and the dominant theme shifted from Geopolitics to Conflict” is intelligence.

Multi-INT fusion

650K+ live ship/aircraft observations correlated with narrative anomalies. Dark vessels in the Strait of Hormuz while regional coverage spikes produce a single fused alert. No consumer product does this.

Market causality, not quotes

Every notable price move links to the situation driving it; the risk-on/risk-off regime label lists its pressure drivers.

Honesty as a feature

Claim status and confidence are first-class fields, detection methodology is published, and sensor alerts state their own limits (receiver gaps). Nothing is presented as more certain than it is.