Unify your business data with AI-powered
entity matching

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Measurable results that transform your data operations

Cut manual review time by up to 80%

Cut manual review time by up to 80%

Free your data teams from tedious record comparison.

Redirect skilled resources to high-value analysis.

Identify matches with up to 90% greater precision

Identify matches with up to 90% greater precision

Eliminate costly false positives and misidentifications.

Make critical business decisions with confidence.

Process millions of records in hours, not weeks

Process millions of records in hours, not weeks

Scale from thousands to millions of records without performance loss.

Transform weeks of matching work into hours

Detect hidden matches

Detect hidden matches

Capture relationships invisible to rule-based systems.

Connect entities despite missing or conflicting information.

Real problems we solve every day

Matching professionals across multiple platforms

Matching professionals across multiple platforms

The Problem: Identifying the same individuals across professional networks, databases, news mentions, and publications. Traditional identity resolution methods struggle with common names and incomplete biographical data.

Our Solution: Cross-reference biographical details with industry context while analyzing contextual clues to validate identity.

Your Outcome: Single unified view of professionals with accurate attribution of activities and relevant information filtering.

Unifying fragmented company data

Unifying fragmented company data

The Problem: Matching business entities across systems with inconsistent names, missing websites, and incomplete information. Complex master data management challenges arise when consolidating multiple data sources.

Our Solution: Intelligent name pattern detection with contextual verification and smart web search to complete missing data points.

Your Outcome: Consolidated company records with verified digital presence and confidence-scored matching information.

Core capabilities that redefine entity matching

Enterprise-ready architecture that connects to your data sources

Business information providers

Business information providers

Government and legal registries

Government and legal registries

Professional networks

Professional networks

Search engine results

Search engine results

Social media

Social media

News articles

News articles

Corporate websites

Corporate websites

Digital archives

Digital archives

 

 

Seamlessly integrates with your existing tech stack

Databases
Agent frameworks
Cloud platforms
Programming languages

Answers to your most common questions

What is entity matching, and why is it so challenging?
Entity matching connects fragmented records that refer to the same real-world person or company across different systems. The challenge is complex: names vary slightly (“Acme Inc” vs “Acme Corp”), data is often incomplete, and distinguishing between similar entities requires context. Traditional systems rely on exact field matching but struggle when basic information is ambiguous, leading to expensive manual review processes.
How is Forage AI’s entity matching approach different from traditional matching tools?
What specific challenges does Forage AI’s entity matching solution solve that other approaches can’t?
How secure is our data during the matching process?
Can Forage AI’s solutions handle unstructured data effectively?
How quickly can we implement this solution?
Will we still need people to review matches?