Unify your business data with AI-powered entity matching

Leading financial institutions that developed data matching systems in the early 2000s and 2010s have faced persistent limitations. Despite combining fuzzy logic and algorithmic approaches, these systems remain fundamentally brittle, requiring substantial manual review teams to adjudicate matches that are instantly recognizable to humans but unresolvable by conventional programming. Our agentic approach transforms entity matching through intelligent graph networks that comprehend the multiple representations of the same entity across disparate systems. Where traditional solutions fail at the first point of ambiguity, our AI agents excel by interpreting contextual information, proactively seeking verification data when needed, and continuously refining their capabilities through systematic feedback.

Our system excels in real-world scenarios where traditional approaches fail. For companies, we intelligently navigate corporate complexity: distinguishing between legal entities and “doing business as” names, tracking relationships through mergers and acquisitions, and identifying new companies missing from legacy databases. For people matching, our approach shines with common names like “John Smith” appearing across news articles and professional databases. Rather than simply comparing basic fields, our identity resolution agent evaluates rich contextual evidence: Is the person’s age consistent with their career stage? Could they realistically commute between the reported home and work addresses? Does their immigration timeline align with their professional history?

Data security is a top priority. Unlike cloud-only vendors, we can build data matching solutions on your premises, keeping your sensitive data completely within your own four walls. This on-premises approach integrates seamlessly with your existing security frameworks while giving you full control over data access and processing.

Absolutely. Our expertise in data automation and AI systems—particularly web data collection and Intelligent Document Processing (IDP)—allows us to extract, process, and standardize unstructured data from websites and documents. We develop AI agentic workflows that transform unstructured information into high-quality, actionable datasets tailored for master data management and comprehensive entity resolution.

Most clients see their first matched datasets within days, not months. Our implementation team handles configuration, integration, and validation to get you up and running quickly.

You’ll be shocked at how much of a reduction in manual effort there is with our agentic approaches. We’ve seen deployments reduce efforts by 70–90%, and have unlocked many more matches than are otherwise possible given the deep web research automation capabilities now built into our systems.