Right now, funds are making DePIN allocation decisions with data that doesn't exist anywhere else. 350+ project profiles. 254+ variables each. 800,000+ physical sensors cross-validating every claim. 17 analytical layers separating signal from noise. If you're still scraping dashboards and guessing at network health, you're already behind.
KAIROS Signal provides data and analytics for informational and research purposes only. Not investment advice. Past performance does not guarantee future results.
There is no Bloomberg for DePIN. No Refinitiv. No S&P feed. Every fund, every VC, every researcher is flying blind — scraping dashboards, trusting marketing claims, and allocating capital based on vibes. That era is over.
Every crypto data platform repackages the same exchange feeds and calls it "alternative data." We built something that actually doesn't exist: a unified data layer connecting 350+ DePIN projects to 800,000+ physical sensors across NOAA, EIA, Maritime AIS, and 15+ government agencies. You could try to build this yourself. It would take you 2 years and $2M+ in engineering. Or you could have it tomorrow for $200/mo.
NOAA Space Weather, US Energy Grids, Maritime AIS, FAA aviation data, and 15+ government agencies. We correlate physical-world conditions with on-chain claims — exposing networks that lie about their infrastructure.
Econophysics, game theory, spectral analysis, statistical mechanics, and information theory — operating simultaneously across hyper-dimensional feature space. Each layer sees what pure price analysis can't.
The metric that changes everything. We measure the divergence between a token's market cap and its verified physical utility. Every overvalued DePIN project has a high Reality Gap. Every undervalued one has a low one. This single number has identified structural mispricings months before the market corrected.
Macro, Contrarian, Risk, and Pattern engines evaluate every data point independently. Nothing enters the pipeline unless multiple AI brains agree. Result: institutional-grade signal with noise stripped out.
Helium, Render, Filecoin, Akash, Hivemapper, and 340+ more — updated every 3 minutes. Node counts, uptime, throughput, earnings, churn, geographic coverage. Each metric verified against physical ground truth — not self-reported claims.
5-layer Z-score decomposition across 15m to 6h windows. Separates structural regime changes from random noise — so you enter positions on real moves, not fake breakouts.
Not a mockup. Not a roadmap. This is the actual schema your API key unlocks today. Redacted fields are proprietary analytical outputs — subscribe to see what everyone else is trading on.
{
"project": "HNT",
"timestamp": "2026-02-15T09:30:00Z",
"network": {
"active_nodes": 382941,
"uptime_30d": 0.9847,
"throughput_mbps": ████████,
"geographic_entropy": ██.████
},
"reality_gap": 0.34,
"regime": "accumulation",
"composite_score": ██.████,
"layer_outputs": {
"L1_sieve": ████,
"L2_regime": ████,
"L3_nash": ████,
"...": ████,
"L17_synthesis": ████
},
"sensor_refs": ["NOAA:SPW", "EIA:ELEC", ████]
}SELECT project, timestamp, reality_gap,
regime, composite_score,
layer_outputs:████ AS l3_nash,
layer_outputs:████ AS l7_ising,
sensor_correlation:████
FROM kairos_signal.public.depin_vectors
WHERE project = 'HNT'
AND timestamp >= '2026-02-01'
AND reality_gap > 0.25
ORDER BY timestamp DESC
LIMIT 1000;See the complete schema, authentication guide, and code examples.
Read Full API Documentation →While everyone else runs the same RSI/MACD signals, we're applying Nobel Prize-winning physics to financial markets. Econophysics. Statistical mechanics. Game theory. Stochastic calculus. Here's a glimpse — with proprietary implementations redacted, because our subscribers would prefer to keep their edge.
Nash payoff matrix — models strategic equilibria between market participants across DePIN token markets using modified cooperative game theory.
Measures divergence between market capitalization and verified physical utility — weighted across multiple real-world performance dimensions.
Ising model adaptation — applies spin-glass dynamics from condensed matter physics to model collective behavior and phase transitions in token ecosystems.
Fractal Z-Cascade — multi-scale Z-score decomposition across nested timeframes. Isolates structural regime changes from stochastic noise.
Full technical brief, SQL schema, and integration guide available now.
Read the Technical Brief →We ingest 100 million data points per day from 198 sources. By the time it reaches your API, it's been cleaned, validated, enriched across 17 analytical layers, and tagged with regime context. You get the finished product. We handle the impossible part.
530K symbols · 198 sources · ~100M new/day
20% anomaly gate · Poison filtered · Deduped
Regime-tagged · Reality Gap scored · AI-graded
Your format · Your cadence · Your models
Also available on Snowflake Marketplace — query with standard SQL, no rate limits, plug directly into your data warehouse.
Get Access Now →A single mispriced DePIN allocation costs more than a year of KAIROS data. The question isn't whether you can afford this — it's whether you can afford not to have it.
Everything you need to start building on DePIN data today.
API key delivered instantly. Integrate in under 1 hour.
Real-time data for teams that can't afford to be 24 hours behind.
See regime shifts and anomalies as they happen — not hours later.
The full analytical arsenal. Nothing held back.
Most subscribers choose this. One avoided bad trade pays for a year.
For funds that need data delivered their way — and an AI analyst on call.
Built around your exact workflow and compliance needs.
We're training specialized models on our 1B+ data points. Each model is an expert in its domain — ask it anything about DePIN markets, and it answers with data, not hallucinations. Enterprise subscribers get first access.
Cross-market regime analysis. Correlates DePIN network health with macro conditions, energy markets, and monetary policy shifts.
Training on 400M+ data pointsDetects crowded trades, sentiment extremes, and consensus failures. Finds alpha where everyone else sees noise.
Training on 250M+ data pointsTail-risk detection, drawdown prediction, and portfolio stress testing. Trained on every DePIN crash, exploit, and black swan event.
Training on 180M+ data pointsFractal pattern recognition across timeframes. Identifies structural setups using Z-Cascade, Hurst exponents, and spectral decomposition.
Training on 200M+ data pointsEnterprise subscribers will choose which model to query — or use all four. Ask a question, get a data-backed answer in seconds.
Request Enterprise Preview →We're selectively onboarding funds, VCs, and data teams. Subscribers get API access within minutes. Every day without this data is a day your competitors have it and you don't.