signalscope
how it works

You give it a brand. It gives you back a report you can check.

Monitoring tools hand you a feed of mentions and leave the reading to you. Nobody reads 200 headlines on a Tuesday, so the feed goes unread and the useful bit never surfaces. SignalScope does the reading, drops what does not matter, and writes up what is left with every claim tied to the source it came from.

Three stages, about a minute

brief → collect → triage → synthesize → report
01 / collect

Pull what is actually being said

Four public sources, fetched at the same time: Google News, Hacker News, GDELT global media, and Tavily web search when a key is set. No paid scrapers and no login, so the run works the first time you press the button.

Sources
Google News RSS, Hacker News, GDELT, optional Tavily
Failure
Each source fails on its own. Three up, one down, the run continues
You see
A live status line per source with its count
02 / triage

Throw most of it away

A fast model reads every raw signal and scores it 0 to 10 for relevance. Brand-name homonyms, spam, and off-topic posts get dropped here. This is the step that turns a firehose into something a person would read.

Model
A cheap fast one. Claude Haiku, or a GPT mini model
Scores
0 to 10 per signal, kept on the card so you can see the call
Typical
About 70 fetched, about 40 kept
03 / synthesize

Write the report, cite every claim

An analyst-grade model writes to a fixed schema, so the output has the same shape every time: summary, sentiment, themes, competitor moves, opportunities, risks, and an action table with an owner and a timeframe on each row.

Model
The stronger one. Claude Sonnet, or GPT
Shape
Enforced by a schema, not by asking nicely in a prompt
Citations
Checked on the server. A made-up source id is removed

One run, start to finish

pulled from the bundled sample report
the brief
brand
Anthropic
competitors
OpenAI, Mistral
category
AI foundation models
objective
Track competitive positioning and find messaging angles we can use this quarter
what it wrote back

The most decision-relevant development this window is OpenAI's 30% API price cut, which resets enterprise pricing conversations across the category. Anthropic's counter-narrative is strong but under-amplified: developer sentiment around agentic coding performance and the 1M context window is organically positive on Hacker News, and the new compliance certifications open regulated-industry doors. Mistral is consolidating a 'European sovereignty' position that is worth monitoring but is not yet a direct threat in US enterprise deals.

theme 1 of 3

Price war reframes the enterprise conversation

OpenAI's across-the-board price cut shifts buyer attention to cost-per-outcome. Expect procurement teams to reopen pricing discussions this quarter; sellers need a value framing ready that moves the conversation from per-token price to total cost of reliable completion. 36

first action

Build a cost-per-completed-task comparison asset for sales to counter price-cut objections

marketing · this week · OpenAI's price cut will surface in active deals immediately.

what those chips open
[3] The Informationrelevance 9/10

OpenAI cuts API prices across the board as model competition intensifies

The 30% price cut puts pressure on rivals' enterprise margins…

news · 2026-06-10
[6] VentureBeatrelevance 7/10

Enterprise AI buyers say model choice now driven by reliability, not benchmarks

A survey of 300 CIOs found uptime and predictable behavior outrank leaderboard scores…

news · 2026-06-06

In the app these are one click away. The chip scrolls the evidence panel to the card and flashes it, so checking a claim takes a second rather than a search.

What is in every report

executive summary

Three to five sentences, leading with the most decision-relevant finding.

sentiment

How the brand is being talked about, and where.

themes

The dominant narratives in the window, up to five, each cited.

competitor moves

What a rival did, plus a "so what" for your GTM.

opportunities and risks

Ranked, with the evidence attached to each.

actions

A table with an owner and a timeframe on every row.

The whole thing exports to Markdown, citations included, so it can go straight into a doc or a Slack message without being retyped.

What it will not do

  • No X, Reddit, LinkedIn, or Instagram. All four block unauthenticated access or charge for it, so leaving them out was the honest call.
  • GDELT gives headlines only, and snippets everywhere are short. The report reasons over titles and descriptions, not full article text.
  • Nothing is stored. You get one report and a Markdown export, with no week-over-week comparison yet.
  • Recency windows differ by source, so "this week" is not exactly the same week everywhere.
  • A brand with no recent coverage returns little. The run tells you that instead of inventing findings.