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DarkMath is going to IAB 2026 AND NADA 2026!
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

DarkMath

Enhancing identity and custom audiences by turning fragmented customer data into a single source of truth and extending the reach of your audiences through novel semantic attributes. This is not just a better way to do identity resolution, it’s a whole new, novel approach that leverages AI and LLMs at scale.
Semantic Gravity
DarkMath converts every data point into a vector embedding, a numerical fingerprint capturing its semantic meaning. Related data naturally clusters together: a luxury car purchase, wealth management browsing, and premium credit card all orbit the same "High-Net-Worth" profile. This is Semantic Gravity: the force that pulls fragmented data toward its true identity.
Video: DarkMath Uncovers Hidden Data Through Context

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.

The DarkMath Platform & Services

Probabilistic identity resolution, anomaly detection, and semantic expansion, powered by contextual intelligence.
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
Legacy deterministic systems matching mechanism is static text-based and fuzzy matching
DarkMath's matching mechanism uses Static text-based and fuzzy matching
Dynamic Multi-dimensional vector embeddings (semantic match)
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DarkMath's matching mechanism uses Static text-based and fuzzy matching
Matching Scope/Capabilities
Breaks on typos, nicknames, format variations, missing fields
Darkmath's failure point: Breaks on typos, nicknames, format variations, missing fields and Understands context, homonyms, behavioral patterns, and semantic relationships
Understands context, homonyms, behavioral patterns, and semantic relationships
.
Darkmath's failure point: Breaks on typos, nicknames, format variations, missing fields and Understands context, homonyms, behavioral patterns, and semantic relationships
Profile Accuracy
Up to 1 in 4 profiles contain critical errors
Legacy deterministic systems profile accuracy is up to 1 in 4 profiles contain critical errors
DarkMath's profile accuracy is Up to 1 in 4 profiles contain critical errors and 86.44% F1 accuracy in head-to-head testing
86.44% F1 accuracy in head-to-head testing
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DarkMath's profile accuracy is Up to 1 in 4 profiles contain critical errors and 86.44% F1 accuracy in head-to-head testing
Outcome
Fragmented identities, wasted spend, missed opportunities
Legacy deterministic systems outcome is fragmented identities, wasted spend, missed opportunities
DarkMath's outcome is Fragmented identities, wasted spend, missed opportunities and Unified Golden Record, reduced fraud risk, 360° customer view
Unified Golden Record, reduced fraud risk, 360° customer view
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DarkMath's outcome is Fragmented identities, wasted spend, missed opportunities and Unified Golden Record, reduced fraud risk, 360° customer view
Legacy deterministic systems failure point is  breaks on typos, nicknames, format variations, missing fields

Case Studies

Read through real life examples of how DarkMath works.

From 5 to 1: Unifying the Fragmented Financial Identity
DarkMatch
How 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.
The Semantic Firewall: Eliminating "Zombie Data"
DarkMatch
Combating churn by using the DarkMath Identity Spine to validate, repair, and enrich customer data in real-time.
Key Result: 12% reduction in customer churn.
Beyond Demographics: Synthesizing Gen Z Audiences
DarkMirror
How 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).
Invest in the future of DarkMath
The data landscape is at an inflection point; legacy systems fail with fragmented identities and unused insights, costing billions. DarkMath offers a fundamental paradigm shift: a new operating system for customer intelligence that unlocks unprecedented value and builds the future of knowing. We invite visionary investors to join us in shaping this next dimension of growth in a privacy-first world.

Frequently Asked Questions

What is identity resolution?

Identity resolution is the process of determining whether multiple data records refer to the same real-world entity (person, household, or business). Traditional approaches use deterministic matching (exact field matches) or fuzzy matching (statistical likelihood). DarkMath adds a third layer: semantic matching, which uses AI to understand context and meaning, resolving identities that text-based methods miss.

How is DarkMath different from traditional CDPs?

Customer Data Platforms typically rely on deterministic logic, if identifiers match exactly, records are linked. DarkMath uses vector embeddings to understand semantic relationships. We don't just check if "123 Main St" equals "123 Main Street", we understand that both represent the same location, that "Cathy" is likely "Catherine," and that behavioral patterns can confirm identity even when explicit identifiers differ.

What accuracy can I expect?

In head-to-head testing against 15 leading identity resolution providers, DarkMath achieved 86.44% F1 score accuracy, a 19.82% improvement over the nearest competitor. Match rates improved by 28.68%, and false duplicate rates decreased by 32-47%. Results vary by data quality and use case; we recommend a proof-of-concept evaluation on your data.

How does Semantic Gravity Work?

Semantic Gravity is DarkMath's core innovation. Every data point is converted into a vector embedding, a numerical representation in high-dimensional space. Related concepts cluster together: a luxury car purchase, wealth management browsing, and premium credit card usage all orbit near each other. When new data enters the system, it's "pulled" toward existing profiles with the highest semantic affinity, creating self-reinforcing identity clusters we call Golden Records.

Is my data secure?

Yes. DarkMath offers vector-only ingestion, meaning you can preprocess data into vector representations before transmission, raw PII never leaves your environment. DarkLabs provides a sandboxed testing environment isolated from production systems. We support secure file transfer (SFTP), S3 bucket sharing, and Snowflake table sharing with encryption in transit and at rest.

How long does implementation take?

Most customers see initial results within days, not months. DarkMath is designed to complement existing systems, not replace them. We accept data via API, direct file upload, FTP, S3, or Snowflake, no custom connectors required. A typical proof-of-concept runs 2-4 weeks; full production integration depends on your data architecture.

What industries do you serve?

DarkMath serves any industry where identity matters: AdTech and audience platforms, retail and e-commerce, financial services and fraud prevention, automotive, lending, healthcare, and data providers. Our semantic approach is particularly valuable when deterministic identifiers are sparse, inconsistent, or frequently changing.

See how we can map your customer universe

Transform your strategy and discovery the clarity and growth that fragmented data hides. It’s time to truly know your customers.