Signal extraction
Models separate meaningful movement from background noise across every connected source, continuously.
Fract transforms complex data into clear signals and actionable intelligence through AI-powered analysis.
Signal strength
98.2%
Confidence
HIGH
Latency
12ms
Sources
1,284
99.2%
Signal precision
Rolling 30-day
1,284
Connected sources
Streaming + batch
12ms
Median resolve time
Ingest to signal
4.8B
Events processed
Last 24 hours
Figures are illustrative placeholders pending production telemetry.
Fract applies layered AI analysis to raw, high-volume input and returns the small number of things that actually warrant attention — each one scored, explained and traceable back to its sources.
Models separate meaningful movement from background noise across every connected source, continuously.
Every signal carries a scored confidence interval, so you know what to act on and what to watch.
Related events are correlated into a single explained narrative instead of a stream of disconnected alerts.
Define what matters once. The engine keeps aim on it and surfaces only what crosses your threshold.
Four stages run continuously. Everything that reaches you has already survived all of them.
Streaming and batch inputs are normalised into one continuous timeline.
1,284 sources
Noise, duplication and low-confidence events are removed before analysis.
99.4% discarded
Surviving events are scored for confidence, novelty and relevance to your targets.
Scored 0–100
Correlated events become a single explained signal, delivered where you work.
12ms median
Input
18,420 events
Unranked, unlabelled, arriving faster than any team can read.
Output
3 signals
Scored, explained, and traceable to the sources that produced them.
Read it, aim it, or build on it. Each module runs against the same resolved intelligence layer.
A single view of every resolved signal, ranked by confidence and filtered to the targets you defined. Built for people who need the answer, not the raw feed.
Coverage is only useful if it is trustworthy. Every source is normalised, versioned and attributable before it is allowed to influence a signal.
Illustrative coverage pending production connectors.
4.8B / 24h
Events normalised
Every signal links back to the exact records that produced it. No unexplained output.
Inputs and model versions are pinned per signal, so any result can be reproduced later.
Your targets, thresholds and history stay scoped to your workspace and never train shared models.
Fract exists because the hard part of modern data work is no longer collection — it is knowing which of it matters. To fract is to break a whole into its parts. Everything we build is in service of breaking down what you collect until only the one thing worth acting on is left.
More output is not more intelligence. The measure of the system is how little it has to show you.
A score without reasoning is a guess. Each signal carries the evidence that produced it.
Versioned inputs, reproducible results, and honest confidence — including when confidence is low.
What the platform does, where its data comes from, and what is still a placeholder.
Fract reads high-volume input — market feeds, on-chain activity, public disclosure, news and social streams — and returns the small number of developments that cross the thresholds you defined. Each one arrives scored, explained, and linked back to the records that produced it.
The network asset associated with Fract. The platform is the product — token details are published here for transparency.
Ticker
$FRACT
Network
TBA
Total supply
PlaceholderTOTAL SUPPLY — TBA
Contract address
PlaceholderCONTRACT ADDRESS — TBAAI-powered intelligence designed to turn complex information into clear, actionable signals.
Contact and channel links are placeholders until launch