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 probabilistic matching (statistical likelihood). DarkMath adds a third layer: semantic matching, which uses AI to understand context and meaning, resolving identities that string-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.

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.

Find Semantic Truth in Your Data

Resolve Who's Who, Infer Who Matters.

DarkMath improves marketing performance by fixing identity first and scaling the right audiences second. Powered by DecisionLinks data and continuously learning from real-world outcomes.

See How It Works View Benchmark Results

86.44% F1 accuracy+28% match rates47% fewer false duplicates~50% more reach

WHY DARKMATH

Not just better. Fundamentally different.

Your Audience

0 records

01 Deduplicate

Same person.
Different records.

Traditional systems miss duplicates when the data doesn't match exactly. DarkMath's semantic matching recognizes fragmented records as the same person — merges the data and eliminates wasted spend.

20%more duplicates caught

02 Expand

Find the audience
you're missing.

DarkMath discovers semantic look-alikes who share behavioral signals with your best customers — people traditional queries can't reach.

~50%more addressable reach

03 Enrich

Demographics describe.
Behaviors predict.

DarkMath's AI converts static attributes into behavioral intent signals — turning your audience from a list into a precision targeting weapon.

AIintent signals per record

EXPLAINER VIDEOS

Traditional Matching Fails in a Complex World

Nearly 1 in 4 customer profiles contain critical errors. DarkMath addresses both problems fixing fragmented identities and uncovering valuable audiences through contextual understanding.

John Smith Jr. vs. Sr.: Semantic Age Resolution

Traditional systems struggle to distinguish identical names within the same household. DarkMatch uses contextual record linkage, behavioral patterns, device usage, and cross-signal relationships, to correctly separate individuals without relying on explicit identifiers.Result: cleaner identity, better targeting, and more accurate attribution.

GO TO DARKMATCH →

Expand Your Audience Through DarkMath's Proxies

Traditional systems ignore customers when key attributes are missing. DarkMirror uses proxy audience intelligence to identify high-value individuals based on behavioral and contextual patterns, even when explicit data isn’t available.Result: expanded reach without sacrificing precision.

GO TO DARKMIRROR →

THE DARKMATH PLATFORM & SERVICES

The DarkMath Platform & Services

Probabilistic identity resolution, anomaly detection, and proxy audience expansion, powered by contextual intelligence.

PRODUCT DEMO

See DarkMath in Action

Watch how DarkMath resolves fragmented identities and uncovers high-value audiences in real time. The demo shows how you can:

Uploading and processing customer data

Contextual record linkage across datasets

Reviewing match results and confidence scores

Watch Demo

DarkMatch

Contextual Record Linkage

Resolve fragmented and ambiguous customer records like shared households and name variations into accurate, usable identities for higher match accuracy, fewer duplicates, and better targeting and attribution.

IDENTITY RESOLUTION →

DarkMirror

Proxy Audience Expansion

Find high-value customers even when key attributes are missing. Uses behavioral and contextual signals to expand your reach with precision and better CPA and ROAS.

AUDIENCE EXPANSION →

DarkWatch

Stochastic Anomaly Detection

Coming Soon

Detect unusual patterns and potential fraud in real time using contextual analysis that traditional rule-based systems miss with fewer false positives.

FRAUD DETECTION →

DarkLabs

Vector Database Sandbox

Coming Soon

Test DarkMath on your own data in a secure, controlled environment. Validate identity resolution and audience performance before full deployment.

TEST YOUR DATA →

OUR ADDED LAYER

Legacy Systems vs. DarkMath

Dimension

Traditional Systems

DarkMath

Matching Mechanism

Static rules and basic fuzzy matching

Contextual record linkage using behavioral and cross-signal patterns

Matching Scope/Capabilities

Breaks on typos, nicknames, format variations, missing fields

Understands context, behavior, and relationships across records

Profile Accuracy

High error rates and inconsistent identity resolution

Higher accuracy and more reliable identity resolution across datasets

Outcome

Fragmented identities, wasted spend, missed opportunities

Clean customer records, better targeting, and more accurate measurement

DARKMATH IN ACTION

Case Studies

Read through real life examples of how DarkMath works.

DARKMATCH

From 5 to 1: Unifying the Fragmented Financial Identity

How a national bank turned 85 million fragmented records into a single source of truth, uncovering hidden risk and slashing storage costs.

Result: $240M in hidden credit exposure identifed

Read Story →

DARKMATCH

The Contextual Record Linkage Firewall: Eliminating "Zombie Data"

Combating churn by using the DarkMath Identity Spine to validate, repair, and enrich customer data in real-time.

Result: 12% reduction in customer churn

Read Story →

DARKMIRROR

Beyond Demographics: Synthesizing GenZ Audiences

How a global brand used behavioral proxy modeling to identify Gen Z consumers without relying on third-party cookies or explicit age data.

Result: 35% lower cost per aquisition (CPA)

Read Story →

View All Case Studies

Resolve Who's Who, Infer Who Matters.

DarkMath improves marketing performance by fixing identity first and scaling the right audiences second. Powered by DecisionLinks data and continuously learning from real-world outcomes.