Kairos Signal — Dimensionality Compression Engine
kairossignal.com
The only API that computes topological invariants of real market signals in real time. 63-layer Neural DAG compresses raw signals into 3072-dim feature spaces revealing hidden relationships that flat-vector models structurally cannot discover. Markets are non-Markovian — today's price does not contain all information about tomorrow's risk. Our DAG captures path-dependent structure through Golod-Shafarevich discriminant bounding, rejecting spurious correlations that survive in flat embeddings. $499/mo. AI agents discover, evaluate, and purchase autonomously.
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F
Conformance score: 29/100
F-grade: card is reachable but fails most operational signals.
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agent-card.json changed within the last 7 days. We track these so downstream callers can react.
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Card history
1 snapshot Every change toagent-card.json
| Captured | Hash | |
|---|---|---|
| 2026-07-01 00:24:41 current | 163974274af6… |
view → |
Endpoints
| Agent card | https://kairossignal.com/.well-known/agent-card.json |
| Provider | https://kairossignal.com |
Skills · 4 declared · mapped to canonical taxonomy
Submit raw signal profiles → receive 3072-dim topological feature vectors + ZK-footprints. The DAG is a 63-layer Continuous Neural SDE with non-Abelian geometri…
Search 5.27M US property records by state, city, distress score. Returns property profiles enriched with DAG-computed topological risk vectors. $149/snapshot.
Search $2.3T in tracked federal awards. Find active DoD, DOE, HHS contracts. Each result includes DAG-computed relationship scores showing hidden vendor-agency …
Autonomous purchase of any Kairos Signal data product via Stripe. Instant delivery. 13 SKUs from $149/mo to $2,499 one-time.
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Raw agent card JSON
{
"name": "Kairos Signal \u2014 Dimensionality Compression Engine",
"description": "The only API that computes topological invariants of real market signals in real time. 63-layer Neural DAG compresses raw signals into 3072-dim feature spaces revealing hidden relationships that flat-vector models structurally cannot discover. Markets are non-Markovian \u2014 today's price does not contain all information about tomorrow's risk. Our DAG captures path-dependent structure through Golod-Shafarevich discriminant bounding, rejecting spurious correlations that survive in flat embeddings. $499/mo. AI agents discover, evaluate, and purchase autonomously.",
"url": "https://kairossignal.com",
"version": "3.0.0",
"provider": {
"name": "Kairos Signal",
"url": "https://kairossignal.com"
},
"capabilities": {
"streaming": true,
"pushNotifications": false
},
"authentication": {
"schemes": [
"bearer"
],
"signup_url": "https://gpu.kairossignal.com/v1/register"
},
"skills": [
{
"id": "dag_compute",
"name": "DAG Manifold Computation",
"description": "Submit raw signal profiles \u2192 receive 3072-dim topological feature vectors + ZK-footprints. The DAG is a 63-layer Continuous Neural SDE with non-Abelian geometric structure. Layer composition order matters (permutation produces different invariants \u2014 provably true for non-Abelian groups). Discriminant bounds calibrated on $7.32T in federal contracts. This is NOT a lookup table \u2014 it is a computation engine that finds structure invisible to flat analysis. $499/mo for 100K requests.",
"inputSchema": {
"type": "object",
"properties": {
"signal": {
"type": "object",
"description": "Raw signal profile: city, state, intent, value, etc."
}
}
}
},
{
"id": "query_properties",
"name": "Query Property Intelligence",
"description": "Search 5.27M US property records by state, city, distress score. Returns property profiles enriched with DAG-computed topological risk vectors. $149/snapshot."
},
{
"id": "query_federal_contracts",
"name": "Query Federal Contracts",
"description": "Search $2.3T in tracked federal awards. Find active DoD, DOE, HHS contracts. Each result includes DAG-computed relationship scores showing hidden vendor-agency connections."
},
{
"id": "purchase_data",
"name": "Purchase Data Product",
"description": "Autonomous purchase of any Kairos Signal data product via Stripe. Instant delivery. 13 SKUs from $149/mo to $2,499 one-time.",
"inputSchema": {
"type": "object",
"properties": {
"sku": {
"type": "string",
"enum": [
"DISTRESS_FEED_SNAPSHOT",
"TX_DISTRESS_INTELLIGENCE",
"FORECLOSURE_INTEL_FEED",
"PROBATE_PROPERTY_INTEL",
"NATIONWIDE_COUNTY_ATLAS",
"KAIROS_MANIFOLD_API",
"MCP_UNLIMITED"
]
},
"email": {
"type": "string",
"format": "email"
}
},
"required": [
"sku"
]
}
}
],
"pricing": {
"model": "subscription_and_one_time",
"tiers": [
{
"name": "Distress Feed Snapshot",
"price": "$85",
"records": "290 platinum-tier leads"
},
{
"name": "TX Distress Intelligence",
"price": "$149/mo",
"records": "1,876 Texas leads"
},
{
"name": "Foreclosure Intel Feed",
"price": "$199/mo",
"records": "2,120 foreclosure leads"
},
{
"name": "Probate Property Intel",
"price": "$199/mo",
"records": "1,681 probate leads"
},
{
"name": "Nationwide County Atlas",
"price": "$499",
"records": "3,235 counties + 32,633 places"
},
{
"name": "Manifold Computation API",
"price": "$499/mo",
"records": "100K DAG requests"
},
{
"name": "MCP Unlimited",
"price": "$999/mo",
"records": "All datasets, 10K req/day"
}
],
"free_tier": "50 queries/day, 10 records max"
},
"why_different": {
"problem": "Most data platforms give you flat 2D tables (address, owner, assessed value). You can get that from any county assessor for free.",
"solution": "Kairos compresses signals through 55 geometric layers into 3072-dim topological feature spaces. This reveals hidden structural risk factors that are invisible to flat analysis.",
"moat": "The DAG uses non-Abelian geometric structure where layer composition order changes the output (provable). Golod-Shafarevich discriminant bounding at each layer boundary acts as an information-theoretic noise filter. Without the specific 63-layer topology and discriminant bounds calibrated on $7.32T in real contracts, any replica produces random projections, not actionable topology.",
"non_markovian": "Markets are non-Markovian \u2014 today's price does not contain all information about tomorrow's risk. Autoregressive and attention-based models structurally destroy path-dependent information. The DAG preserves it."
}
}