STAGE 1

50%

of records resolved

Augmented Deterministic Matching

Rule-based logic enhanced by ML-based scoring that weights high-value identifiers like SSNs, using intelligent statistical confidence thresholds instead of pass/fail rules.

STAGE 2

30%

additional records

Probabilistic Fuzzy Matching

Deploys the Fellegi-Sunter model and EM algorithms to calculate match probabilities without unique identifiers, through unsupervised learning and behavioral correlations.

STAGE 3

20%

complex edge cases

AI-Powered Contextual Record Linkage

Resolves the hardest edge cases using LLMs to analyze Semantic Gravity and behavioral clusters human-level reasoning that bridges gaps in fingerprints and location history.

HOW DARKMATCH WORKS

Three stages, each catching what the last missed

DarkMatch combines three complementary approaches in a cascading pipeline, each layer resolving the records the previous layer couldn't.

How DarkMatch Works - Stage 1: Augmented Deterministic Matching The process begins with rule-based deterministic matching, enhanced by an ML-based scoring mechanism. This mechanism assigns dynamic scores to attributes and derived rules—weighing a match on Social Security Number higher than Phone Number, for example. Intelligent thresholds augment traditional rules with statistical confidence rather than binary pass/fail logic. This stage handles approximately 60% of records where strong identifiers exist. Stage 2: Probabilistic Fuzzy Matching For records that don't trigger deterministic matches, DarkMatch deploys our high-performance probabilistic record linkage library implementing the Fellegi-Sunter model. Using Expectation Maximization (EM) algorithms, it performs unsupervised learning on the dataset to calculate match probabilities even when unique identifiers are missing. This stage resolves approximately 30% of records through statistical inference on name similarity, address proximity, and behavioral correlation. Stage 3: AI-Powered Semantic Matching The final 10% of records—the hardest edge cases—are resolved through DarkMath's proprietary LLMs analyzing Semantic Gravity. Rather than comparing strings, the system analyzes behavioral and semantic clusters. Two records with different names but identical spending patterns, device fingerprints, and location histories are recognized as the same identity. The AI asks contextual questions ("Are '123 Main St, Suite A' and '123 Main St #A' the same location?") to resolve ambiguity with human-level understanding.

THE OUTCOME

Turn fragmented data into unified profiles

DarkMath uses vector embeddings and Semantic Gravity to unify fragmented customer data into a single Golden Record. In head-to-head testing against 15 leading identity resolution providers, DarkMath achieved 86.44% F1 accuracy a 19.82% improvement over the nearest competitor.

40-50%

Higher match rates

Find connections that string-matching systems miss, through semantic understanding.

22%

Audience expansion

Reach qualified prospects with missing data through semantic attribute inference.

$2.7M

Revenue saved

Generated through audience expansion on unified, accurate profiles.

1 in 4

Solve the $1B identity crisis

Nearly 1 in 4 enterprise customer profiles contain critical errors. DarkMath fixes fragmentation at the source.

BENCHMARKS

Competitor comparsion

Competitor Comparison

Performance Metric

DarkMath

Competitor Score

Our Advantage

vs. A National Data Company

F1 Score (Accuracy & Balance)

86.44%

72.14%

+19.82%

Match Rate

78.96%

61.36%

+28.68%

vs. A Prominent Brand CDP

F1 Score (Accuracy & Balance)

83.42%

68.86%

+21.14%

Match Rate

72.22%

54.91%

+31.5%

vs. An International Audience Creation Company

Total Households(Reach)

10,838,389

6,992,837

+55% (+3.8M)

Household Precision(Max Size)

37(Realistic)

994
(False Positives)

Eliminated
Mega-Clusters

Duplicate ID
Detection

26.91%

0%
(Baseline)

Found 2.7M Duplicates

True Customer 360

See complete purchase history, preferences, and interactions across every channel.

Accurate LTV calculation

Understand actual customer lifetime value once fragmented transactions are unified.

Personalization at scale

Deliver relevant experiences based on a complete behavioral history.

THE OUTPUT

The Golden Record

DarkMatch produces a unified "Golden Record" for each resolved identity: a single, authoritative profile that consolidates all touchpoints, with confidence scores, source attribution, and a full audit trail showing which records were merged and why.
This enables:

FAQ

DarkMatch FAQs

What data formats does DarkMatch accept?

DarkMatch accepts CSV, JSON, Parquet, and direct database connections. We support batch file uploads, SFTP transfers, S3 bucket sharing, Snowflake table sharing, and REST API integration. No proprietary formats or custom connectors required.

How does DarkMatch handle the 'John Smith Problem'?

Common names are the Achilles heel of traditional matching. DarkMatch treats each identity as a vector centroid and analyzes the full constellation of attributes: spending habits, device usage, location patterns, and life stage. Two "John Smiths" at the same address, father and son, are separated with 99% confidence based on behavioral divergence. The system detects two distinct gravity wells rather than forcing a false merge.

Detailed Example: Semantic Generational ResolutionConsider two records: both "John Smith" at "456 Oak Lane" with the same last name and address. Traditional systems fail here, they either incorrectly merge (corrupting the profile) or require a human to manually review. DarkMath's semantic attribute engine, trained on billions of records with known demographics, identifies the generational signature of each record. Record A shows: TikTok app engagement, Instagram activity, Venmo transactions, casual text syntax with abbreviations ("u" instead of "you"), high emoji frequency, Spotify streaming, and mobile-first browsing. These signals map to Gen Z behavioral patterns. Record B shows: Facebook-primary social engagement, formal email communication, desktop browsing preference, traditional banking app usage, cable TV indicators, and established brand purchasing patterns. These signals map to Boomer generation patterns. Without any explicit "Junior" or "Senior" or age field, DarkMath separates these identities through semantically-trained generational attributes—turning what would be a false merge into two distinct, accurate Golden Records.

Read Full Case Study

Can DarkMatch integrate with my existing matching system?

Yes. DarkMatch is designed to complement existing systems, not replace them. You can run DarkMatch on records that your current system couldn't resolve, use it as a validation layer, or gradually migrate matching logic. Most customers start with a proof-of-concept on a subset of data before full integration.

How do you train your semantic matching models?

DarkMath uses "The Corruptor"—a proprietary synthetic data engine that generates training data by systematically introducing realistic variations into known ground truth records: typos, transposition errors, nickname substitutions, format inconsistencies, and missing fields. This creates massive, unbiased training sets that are mathematically representative of real-world data chaos but free from privacy concerns and historical biases.