AI-Powered Identity Resolution and Semantic Custom Audiences

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.

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.

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.

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.

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.

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.