Find Semantic Truth in Your Data
AI-Powered Identity Resolution and Semantic Custom Audiences
Turn fragmented customer data into a single source of truth through DarkMath that understands context, not just text-strings
Why DarkMath
Not just better. Fundamentally different.
Traditional Matching Fails in a Complex World
Nearly 1 in 4 customer profiles contain critical errors. DarkMath is a first mover using semantic intelligence to understand context, resolve ambiguity, and unify fragmented data into accurate Golden Records. Watch the videos below to see real world examples of DarkMath in action.
John Smith Jr. vs. Sr.: Semantic Age Resolution
Go to DarkMatchTraditional systems struggle to distinguish identical names in a single household. DarkMath uses generational behavioral signals, from app usage to syntax, to separate father and son with 99% accuracy without needing explicit suffixes.
Expand Your Audience Through DarkMath's Proxies
Go to DarkMirrorTraditional banking systems see Sarah as invisible: no income, no credit score, just an "authorized user" to be deleted. DarkMath uses contextual proxies like her Range Rover and 90210 zip code to reveal the truth: she's The Household CFO, the gatekeeper who controls where her family's wealth is spent.
The DarkMath Platform & Services
Probabilistic identity resolution, anomaly detection, and semantic expansion, powered by contextual intelligence.
PRODUCT DEMO
See DarkMath in Action
Watch how DarkMath resolves complex identity matching challenges in real-time. This demo walks through:
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Uploading and processing customer data
Semantic matching across datasets
Reviewing match results and confidence scores
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DarkMatch
Probabilistic Identity Resolution
Semantic matching resolves edge cases like shared households and name variations with 86.44% F1 accuracy. Increases matches by 30–40%.
DarkMirror
Semantic Audience Expansion
Transforms sparse deterministic data into rich semantic profiles. Behavioral signals fill the gap, extending reach by ~50% with precision.
DarkWatch
Stochastic Anomaly Detection
Real-time fraud detection using contextual analysis to catch anomalies others miss, without excessive false positives. Context, not just rules.
DarkLabs
Vector Database Sandbox
Secure sandbox for testing DarkMath on your data. Vector-only ingestion keeps PII in your environment. Clean room with vectorization.
Legacy Systems vs. DarkMath
Dimension
Legacy Deterministic Systems
DarkMath Vector Intelligence
Matching Mechanism
Static text-based and fuzzy matching
Dynamic Multi-dimensional vector embeddings (semantic match)
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Matching Scope/Capabilities
Breaks on typos, nicknames, format variations, missing fields
Understands context, homonyms, behavioral patterns, and semantic relationships
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Profile Accuracy
Up to 1 in 4 profiles contain critical errors
86.44% F1 accuracy in head-to-head testing
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Outcome
Fragmented identities, wasted spend, missed opportunities
Unified Golden Record, reduced fraud risk, 360° customer view
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From 5 to 1: Unifying the Fragmented Financial Identity
DarkMatchHow a national bank used Semantic Gravity to turn 85 million fragmented records into a single source of truth, uncovering hidden risk and slashing storage costs.
Key Result: $240M in hidden credit exposure identified.
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The Semantic Firewall: Eliminating "Zombie Data"
DarkMatchCombating churn by using the DarkMath Identity Spine to validate, repair, and enrich customer data in real-time.
Key Result: 12% reduction in customer churn.
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Beyond Demographics: Synthesizing Gen Z Audiences
DarkMirrorHow a global brand used behavioral proxy modeling to identify Gen Z consumers without relying on third-party cookies or explicit age data.
Key Result: 35% lower cost per acquisition (CPA).
