Signal Scout scans the market for emerging topics, scores them, and routes the strongest through review. Users can then structure approved signals into outputs for content and GTM media production.
Signal Scout is a market intelligence tool. It watches a configurable set of external sources, identifies which topics are worth acting on, and passes the strongest to a person before anything is produced. Once a signal is approved, users can structure it into the components for content and GTM media production: talk tracks, beat maps, shot lists, storyboards, hooks, and post copy.
It runs through two paths. An automated pipeline scans sources on a schedule and scores each item before it surfaces. A manual pipeline lets a user drop in a note, file, or link and route it straight to output, skipping the scoring queue because a person has already vouched for it. Both end at the same step: choosing how to frame the signal and what to produce.
The pipeline is easiest to read as two connected halves. The first decides what is worth paying attention to. The second decides how to say it and what to make. Each has its own breakdown below.
Where signal comes from and how it earns its way to a person: the source landscape, the three ingestion methods, the scoring engine, and the review queue.
Read the breakdown Half twoFramework and format selection, and the per-beat outputs users can review for each format.
Read the breakdown Links to the review consoleIngestion is the market-intelligence half. Its job is to notice the right topics early and pass only the strongest ones forward, so that human attention is spent on decisions rather than scanning.
Sources are tiered by how much the system trusts them. Research and academic feeds start high, trade press sits in the middle, and community sources start lower. New sources enter at neutral trust and earn or lose standing over time based on whether their signals get approved and whether outputs built from them perform.
The automated pipeline reaches sources three ways, each on its own cadence. Whatever the method, every item lands in the same shape and passes through scoring before a person ever sees it.
A 12-layer engine scores how much a topic matters right now. Five layers feed a weighted base score, others apply multipliers, and two act as overrides: named voices auto-elevate, off-topic keywords zero out. Weights are configurable per deployment.
base = emergence 0.30 + relevance 0.25 + authority 0.20 + question gap 0.15 + velocity 0.10 → multipliers → overrides
Scored signals land in a queue where a person makes the call. A threshold slider controls what surfaces, each card links back to its original source, and every approve or purge feeds back into source trust over time. This is the human-in-the-loop checkpoint.
Once a signal is approved, users choose how to frame it and what to produce. Signal Scout structures the approved signal into per-beat outputs for each selected format.
A framework sets the narrative structure and voice. SPARK is the primary one, and thirteen others cover short-form hooks through long-form explainers. Style modes and a set of anti-patterns keep outputs consistent and away from hype.
Users can select several formats for one signal, and each produces its own structured output. Select talking-head and sizzle, and both are generated.
Reviewed as beats. The review console opens each output as a sequence of beats. Each beat shows only the production lanes that apply to it: on-screen text, visual, camera, audio, voiceover, talking points, guest questions, and mood. A beat or document toggle switches between the per-beat view and the full write-up.
Beat labels and timestamps sit on each beat, live web sources sit under every output, and any output the engine stopped early is flagged.
Signal Scout ships as open source with an empty or example configuration. Nothing is hardwired to a single industry. The core handles ingesting, scoring, queuing, and structuring; each deployment supplies the parts that make it specific.
A pip-installable Python package with the scoring engine, the ingestion connectors, the framework and format libraries, and default settings. Users clone it, configure it, and it runs end to end.
Its own source feeds, its own search queries and tracked communities, and its own scoring keywords. The same core tuned to enterprise AI and data surfaces different signal than one tuned to, say, consumer health.
Signal Scout does two jobs. As market intelligence, it surfaces which topics in a domain are emerging before they saturate, where practitioner and analyst views diverge, and what questions keep going unanswered. Users can then structure those findings into outputs for content and GTM media production, so work starts from a scored signal and a structured brief.
Scoring is transparent and the review queue keeps a person in the loop, so it stays a market intelligence tool, not an automation that publishes on its own.