https://michaelmccabe-lang.github.io/howitworks/

The intelligence industry problem

Every platform in this sector
is downstream of reality.

The world is less stable than it was a generation ago. Conflicts are multiplying. Risks are moving faster. And the intelligence sector is still processing information that left the scene before it reached a wire, a feed, or a desk. To stay ahead of what's coming, we need to go to where it starts. We need to go to the crowd.

01 / 06Competitor modelSlow · Reactive
👤
Competitor Model 01
Desk Analysts

Twenty analysts. Reading the news.

The world has been running on the same model for decades — analysts at desks reading feeds, bulletins and social media, logging incidents one by one.

It works. The problem is the maths. 168 hours × 195 countries. So they cover what clients pay for, in business hours, in the time zones that matter.

And the desk doesn't sit in reality. It sits at the bottom of a funnel — reading whatever filtered down through social media and news.

Coverage gap
Generic coverage where clients have interest — and only as much as a small team can achieve.
Collectors of history. By design, they can only see what has already happened.
FIG.04·Desk analyst · downstream of social
02 / 06Competitor modelStale · Derivative
Competitor Model 02
LLM / AI Platforms

Recycled content, automated.

The market tried a shortcut. Pull from dozens of sources, run it through AI, publish.

The AI sits even further downstream — it can't tell a genuine report from a recycled video three years out of date.

Doing it well costs tens of millions a year. Cutting corners publishes lies as intelligence.

Coverage gap
As fast and accurate as the social media feeding it. Works in the West — goes dark in large parts of the world.
AI can only process what humans have already published.
FIG.04·AI aggregator · further downstream · automating misinformation
03 / 06Competitor modelModelled · Incomplete
🗄
Competitor Model 03
Data Aggregators

Standardise the world. Borrow the confidence.

The next attempt was more industrious. Scrape 911 logs, police bulletins, city PDFs.

It works — in cities where governments publish. In Venezuela they stopped years ago. In Niamey there is no 911.

The data aggregator sits downstream of a government-data layer that doesn't exist in half the places that matter.

Coverage gap
As broad as government data allows. Where official reporting stops, the model guesses.
Only as good as the government data it scrapes — which often doesn't exist.
FIG.04·Data aggregator · downstream of government data
04 / 06The thesisThe funnel

Everyone else is downstream of reality.

Reality is where events happen. People with phones see them. Then come the social platforms. Then come the LLMs. Then come the platforms that re-package whatever filtered down.

Every layer is slower, thinner and more vulnerable to misinformation than the one above it.

Crowd Threat doesn't sit at the bottom of that funnel. We've designed our model so we sit at the top.

FIG.04·The full picture · they're all downstream
05 / 06Our modelPrimary · Verified
Our Model · The Answer
The Crowd

We pay the people who saw it.

Crowd Threat starts from a different premise. The communities who see threats know them best — and they're the ones being scraped, uncompensated, by everything downstream of reality.

Instead of taking their content without permission, we build a value transfer. They file what they see. A human verifier checks it. We pay them.

Crowd Survey adds the pre-escalation signal — daily, weekly and monthly updates from residents who chose to share them.

The advantage
Every reporter we add is another corner of the world no algorithm, aggregator or analyst desk can see. The network grows. The coverage compounds. The gap only widens.
FIG.04·Crowd Threat · in reality · upstream of everyone
06 / 06Coverage

You have to be there first.

The world is less stable than it was a generation ago. Conflicts are multiplying. Risks are moving faster. And the intelligence sector is still processing information that arrived downstream — already delayed, already filtered, already owned by someone else. The only signal that changes that is a trusted human network, on the ground, verified by humans.

150+
Crowd Members · in-country, on the ground
68%
Of threats captured before onset, or within 2 hours
Real-time feedValue transferLive
22:31CARACASProtest forming · Plaza Altamira+£2.50
22:24NIAMEYCheckpoint added · Rte de Tillabéri+£3.20
22:18LAGOSPower outage · Victoria Island+£1.80
22:09CAPE TOWNSentiment ↓ · weekly survey+£0.50
Crowd Threat — local eyes on global threats
Products
One crowd. Three intelligence layers.

Every product is powered by the same asset — a global network of vetted local reporters submitting verified incidents the moment they occur. The same data feeds Crowd Threat, Crowd Survey, and Crowd Signal — each calibrated for a different analytical need.

Shared foundation

All three products are built on the same verified incident data. Subscribe to Crowd Threat, Crowd Survey, or Crowd Signal — or combine them for the full intelligence stack.

Book a Demo
Global Reporter Network
0+ local reporters.
Every corner of the world.

Our crowd isn't a database or an algorithm — it's a global network of real people, vetted and financially accountable, submitting verified incidents from the communities where they live. When something happens, they're already there.

United Kingdom
United States
Germany
Spain
Nigeria
Italy
Indonesia
India
Australia
France
0+
Reporters
10
Countries
↑ Growing
Network
Live · Expanding
The Proof
0%
Before they occur

Threats recorded before they have even happened — planned demonstrations, mobilisations, and pre-incident activity captured by reporters already embedded in those communities.

0%
Within 2 hours

Further threats captured within two hours of onset — when most intelligence platforms are still silent or scraping recycled feeds.

0+
Vetted reporters

And expanding. Every addition extends coverage into corners no algorithm, desk analyst, or satellite feed can reach.

Verification
From noise
to verified signal.
In minutes.

Every alert that leaves our platform has been through this. Raw signal from a reporter on the ground. AI triage. Human corroboration.

RAW SIGNAL
Signal received · coordinates unknown · source unverified
AI TRIAGE
Coordinates resolve · category assigned · timestamp locked
CROSS-REFERENCING
Reporter trust confirmed · 3 sources corroborated
VERIFIED
Alert fires · 94 minutes ahead of any public wire
94 minutes ahead of Reuters wire
STANDBY
CT · VERIFY · 2.1
SIGNAL ID???-????-???-????
COORDINATES██.████°N ██.████°E
TIMESTAMP████████ LOCAL TIME
CATEGORYUNKNOWN
SOURCEUNVERIFIED
DESCRIPTION
████████ ██████ ██ ██████████ ████████ ██ █████ ████████ ████████ ██ ████
CONFIDENCE---
UNVERIFIED
Intelligence Cycle
The data advantage
at every stage.

Three products. One crowd. The same verified data feeds different intelligence layers — each calibrated to where you are in the cycle.

Ground truth layerPHASE 3/5

High-volume verified incidents with precise geolocation and timestamps. The data your competitors are scraping — we generate it first.

01
Pre-escalation
02
Onset of instability
03
Active incident
04
Post-incident
05
Strategic horizon
Live Feed
No signal
Nothing in open sources
Low volume
Noise vs. escalation unclear
Ground truth
High-volume verified incidents
Declining
May mask ongoing instability
Historical baseline
Regional risk by asset class
Stability Vector™
↓ Alert · T−14d signal
Leading indicator
Stress before incidents appear
Confirms escalation
Security & sentiment drop together
Participation falls
Negative scores confirm severity
Recovery tracker
Real stability, not just quiet
Stability Vector™
Quantified country risk scores
Trader Product
↓ BEARISH9% YES
▲ 12 PP implied delta▲65 min edge
Position before consensus
Survey divergence flags early — enter before price moves
Confirm the move
Threat + Survey convergence kills false starts
Manage peak risk
Incident density shows when impact tops out
Exit before the crowd
Survey flags secondary escalation risk early
Build the model
Human-sourced data no other provider has
Verified incident data
Stability Vector™
Trader intelligence
Crowd Threat — local eyes on global threats
Integrations
Where does it sit in your stack?

Crowd Threat is a complementary data layer — not a replacement. Here's how different teams put it to work alongside what they already use.

Existing TI Platforms

Already using a threat intelligence platform? Crowd Threat's API sits alongside it as a complementary verified incident layer — covering the ground-level events that scrapers and aggregators miss.

Prediction Market Tools

Crowd Signal maps verified incident data directly to live geopolitical prediction markets — providing the information asymmetry layer that public information sources cannot supply.

Crisis & Emergency Management

Crisis management platforms and emergency response teams use the real-time feed to detect and respond to incidents in protected zones — with verified ground-truth rather than social media noise.

Risk & Insurance Underwriting

Political risk underwriters use Crowd Survey's Stability Vector as a leading indicator for country risk exposure, and Crowd Threat incident data as the evidentiary layer for event-driven claims.

Analytics & Data Science

Data science teams and analytical platforms ingest our high-volume, real-time incident stream as training data or input signals for predictive models and situational intelligence tools.

News & Media Monitoring

Newsrooms and wire services use Crowd Threat for early detection of breaking incidents in under-reported regions — identifying stories before they surface in any public channel.

Full REST API · Webhook support · JSON / CSV export · Custom field mapping

Get Started
See the data in action.

Request a personalised demo and we'll walk you through the use case most relevant to your team — GSOC monitoring, prediction market alpha, political risk, or API integration.

Contact
contact@crowdthreat.com
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United Kingdom
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