How Signals turns research into foresight.
Foresight at the speed of change — for organizations that need to reveal emerging trends, weak signals, and strategic disruptions with precision and a transparent audit trail. This guide walks through the core ideas, how a research session unfolds, and what you can do at each step.
What Signals is
A signal is a candidate indicator of change — something worth tracking because it might tell you where a market, technology, or behavior is heading. Signals helps research teams discover those indicators, source them against real-world evidence, score them against the dimensions that matter, and synthesize them into reports.
The platform combines a structured research workspace with AI agents that do the heavy lifting — searching the web, deduping findings, judging supporting and contradicting sources, and writing up the results. You stay in the loop the whole time: you can edit signals by hand, chat with an analyst agent, and curate findings across sessions.
Core concepts
Each concept has its own page — open it to unfold the details, gotchas, and how it relates to everything else.
- Organization
- The top-level account that owns every workspace, session, and signal.
- Workspace
- A container for related research that defines the structure every session inside it uses.
- Playbook
- A reusable research blueprint — categories, metrics, and workflow — deep-copied into a workspace.
- Session
- A single research engagement inside a workspace, with its own briefing, signals, and reports.
- Briefing
- The session’s objectives, scope, and constraints — the prompt that steers the research.
- Workflow
- An ordered sequence of pipeline tasks defined on the workspace and triggered per session.
- Signal pipeline
- The six-stage AI pipeline that produces signals and reports from a briefing.
- Frame
- A reusable lens applied to signals — a way of grouping or projecting findings without changing them.
- Tag
- A free-form label attached to a signal, drawn from a workspace-defined tag set.
- Metric
- A scoring dimension applied comparatively to every signal in a session.
- Signal
- A candidate indicator of change — the structured atomic finding of Signals.
- Source
- A URL with supporting context attached to a signal, judged for or against the claim.
- Grounding
- The four-state lifecycle that tracks how well-supported each signal is.
A session, end to end
- Create a workspace from a playbook. This sets up the categories, metrics, report types, and workflows your research will use.
- Start a session inside the workspace. Give it a title and write a briefing — what are you investigating, for whom, with what constraints.
- Run a workflow. Pick one of the workspace’s workflows and choose auto (runs every task end-to-end) or manual (you click through, task by task).
- Review the signals as they accumulate. Edit titles, summaries, categories, tags, and sources by hand whenever you want.
- Read the report when the pipeline writes one. Re-run the workflow whenever you want to refresh.
- Curate across sessions by pulling standout signals into collections.
The signal pipeline
A typical workflow chains six stages — each is a task you can configure on the workspace, re-order, or omit. Open the pipeline page for the per-stage detail.
Search the web and synthesize candidate signals against the briefing.
Consolidate duplicates; keep originals soft-merged for audit.
Re-check sources, judge each, commit to a signal-level verdict.
Score every active signal comparatively against each metric.
Render a per-signal image from a prompt template.
Synthesize all active signals into a markdown narrative.
Tasks skip work that’s already complete by default — re-running a workflow only redoes what changed.
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