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Entity matching agent G2 ★★★★★ 4.8 / 5

Unify your business data with AI-powered entity matching.

Autonomous agents that connect and clean fragmented records across disparate sources. Forage AI builds, runs, and maintains the matching workflow end to end.

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Up to 80% Less manual review
Up to 90% Greater match precision
Single source of truth Unified entity records
Confidence-scored Every match decision
Sample match schema

Every match arrives with the evidence behind it.

Define the entity types, match rules, confidence thresholds, and output fields. Each record can include the decision, supporting signals, conflicting attributes, and review history.

Input record

  • Source record ID
  • Entity type
  • Raw name and aliases
  • Address and location
  • Domain, email, and phone
  • External identifiers

Candidate and evidence

  • Candidate entity IDs
  • Normalized attributes
  • Exact identifier matches
  • Name and domain similarity
  • Location and affiliation overlap
  • Supporting source references

Match decision

  • Canonical entity ID
  • Match, no match, or review
  • Confidence score
  • Decision reason codes
  • Matched attributes
  • Conflicting attributes

Review and lineage

  • Review threshold
  • Reviewer decision
  • Match policy version
  • Model and rule version
  • Decision timestamp
  • Change history

Need to unify your records?

Matching workflows are custom-built around your data.

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Built for resolution

Agents that resolve your records, end to end.

Forage AI agents learn what makes two records the same entity, decide reliably at scale, and deliver unified records your team can trust.

Grounded
Evidence first, matches second.

Candidate records and the evidence around them are collected and structured before any match decision is made.

Role-based
A team, not a monolith.

Separate agents search sources, extract attributes, compare candidates, verify identity, score confidence, and route the outcome.

Governed
Every match inside the rails.

Match rules, output schemas, permissions, confidence thresholds, and stop conditions define how records are compared, merged, and updated.

Judgment kept
Automation with judgment.

Routine, high-confidence matches can move through automatically. Ambiguous, sensitive, or high-impact pairs are sent to the appropriate reviewer with the supporting evidence.

Plugged in
Lands in the systems you own.

Matched records can be fitted into MDM platforms, CRMs, data warehouses, applications, and delivery workflows.

Measured
Production, measured.

Match precision, exception rate, manual-review volume, latency, and cost per matched record can be monitored throughout production.

Why Forage AI

The matching workflow can look beyond the input file.

External evidence built in. When records are incomplete, the workflow can collect additional context from approved company websites, public registries, filings, news, professional sources, and other relevant locations.

Web and documents together. Match structured database records against information contained in websites, PDFs, reports, filings, spreadsheets, APIs, and internal documents.

Custom entity definitions. Define whether a brand, legal entity, branch, subsidiary, parent company, person, product, seller, or supplier should be treated as the same entity or kept separate.

Transparent decisions. Receive the canonical entity, confidence score, matched fields, conflicts, reason codes, and supporting source references for each decision.

Review focused on exceptions. High-confidence records can proceed automatically. Ambiguous records arrive in a review queue with the relevant evidence already assembled.

Managed from pilot through production. Forage AI handles data preparation, match-policy design, workflow configuration, validation, integration, monitoring, recalibration, and ongoing operations.

Use cases

Real problems, solved every day.

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

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.

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Resources

Latest and hottest in agentic AI.

Researching your way through entity matching? Start here.

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Fuzzy matching measures how similar two strings are. Deduplication usually identifies repeated records within one dataset.

Entity matching combines exact identifiers, normalized fields, contextual attributes, relationships, timelines, business rules, and supporting evidence to determine whether records across multiple datasets refer to the same real-world entity.

The output can also preserve a canonical entity ID, match confidence, decision reasons, and source lineage.

The workflow can be configured for companies, people, products, brands, sellers, suppliers, locations, assets, and other project-specific entity types.

Inputs can include databases, CRMs, spreadsheets, APIs, cloud files, websites, PDFs, regulatory filings, registries, news, and other structured or unstructured sources. The exact entity definitions and source coverage are agreed during scoping.

Often, yes, when enough supporting evidence is available.

The workflow can compare names, domains, locations, industries, affiliations, corporate relationships, dates, and other contextual signals. It can also gather additional evidence from approved external sources.

When the available evidence remains insufficient or conflicting, the record is kept unresolved or routed for review rather than being forced into a match.

A representative sample is evaluated before full rollout using metrics such as precision, recall, false-positive rate, match coverage, automatic match rate, and manual-review rate.

Confidence bands determine which records can be approved automatically, rejected, or sent for review. Reviewers receive the candidate records, supporting evidence, conflicts, and decision reasons needed to resolve the case.

Quality thresholds and acceptance criteria can be written into the project SLA.

Yes. Matching outputs can be delivered through batch files, APIs, databases, cloud storage, data warehouses, or existing MDM and CRM workflows.

The deployment design can account for data-access controls, retention requirements, approved external sources, and review permissions. On-premises deployment is also available for workloads that must remain within the client’s environment.

Start foraging

The first call is to understand your records.

Tell us about your data and the records you need to unify.

  • Matching audit on your actual records
  • A tailored matching plan scoped to your entities
  • Pilot run on your data before you commit
Get in touch

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