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Rivac AI Labs
Data Intelligence, Document AI & Applied Analytics

Entity Resolution & Data Deduplication Pipelines

Every organization that's grown through multiple systems, acquisitions, or years of manual data entry ends up with the same problem: the same customer, vendor, or product exists as five slightly different records across different databases, and nobody can get a clean count of anything. This service builds a multi-stage entity resolution pipeline, starting with fast deterministic matching, escalating to fuzzy matching for near-misses, then to LLM-adjudicated matching for ambiguous cases, with a human review step for the ones nothing can resolve confidently, that cleans up messy real-world records without a purely automated process making silent, unreviewed merge mistakes. We tune each stage to your specific data and error tolerance, since a wrongly-merged customer record is a different kind of mistake than a missed duplicate. It solves the quiet but expensive problem of decisions, reporting, and outreach all being built on a record count nobody actually trusts.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

  1. 1

    Audit your current record sets and interview the team that deals with duplicates today to learn where matching breaks down and what a wrong merge costs

  2. 2

    Pilot the multi-stage matching pipeline (deterministic, fuzzy, then LLM-adjudicated) against a representative sample, with a human review step for the low-confidence cases

  3. 3

    Review match and non-match decisions with your team to tune thresholds and catch any risky auto-merges before scaling

  4. 4

    Run the pipeline across your full dataset with an audit trail of every merge decision and a human queue for cases the pipeline can't resolve confidently

What You Get

Deliverables from this engagement

  • Cleaned, deduplicated dataset with a documented match confidence per record
  • Multi-stage matching pipeline (deterministic, fuzzy, LLM-adjudicated, human review)
  • Audit trail of every merge decision for compliance and rollback
  • Ongoing deduplication process for new records as they enter your systems

Six Ways We Could Architect This

Different engagement, different build, pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Read all 6 architectures as text

Lean Fast-Start Architecture

Record Intake

  • Single Source Record Export
  • Field Standardization
  • Exact Key Generator

Matching

  • Deterministic Match Rule
  • Duplicate Candidate List

Review

  • Manual Spot Check
  • Approved Merge List

Output

  • Merged Record Set
  • Cleaned CSV Export
  • Delivered to Data Owner
  • Merge Log Recorded
  • Next Batch Scheduled

Cloud-Native / Managed Services Architecture

Managed Source Layer

  • Cloud Data Warehouse Records
  • Managed Data Quality Service
  • Managed Standardization Function

Managed Matching Layer

  • Deterministic Match Service
  • Fuzzy Match Cloud Function
  • Managed Golden Record Database

Review Layer

  • Low Confidence Match Table
  • Managed Review Queue Service

Operations Layer

  • Merged Record Publisher
  • Downstream System Sync
  • Match Rate Monitoring Dashboard
  • Automated Backup Before Merge

Event-Driven & Automation-First Architecture

Triggers

  • New Record Inserted
  • Match Job Message Queue
  • Record Updated

Automation Layer

  • Deterministic Match Stage
  • Fuzzy Match Stage
  • LLM Adjudication Stage

Review Gate

  • Confidence Level
  • Auto Merge Applied
  • Human Review Queue Notification

Publishing

  • Golden Record Updated
  • Reviewer Decision Logged
  • Downstream Systems Notified

Enterprise-Grade & Compliance-Ready Architecture

Governance Layer

  • Data Steward Sign Off Policy
  • Merge Risk Classification
  • Role Based Approval Rules

Secure Pipeline

  • Access Controlled Record Store
  • Redundant Backup Before Merge
  • Multi Stage Matching Engine

Auditable Merge

  • Every Merge Decision Logged
  • Rollback Capable Merge History
  • Compliance Reporting Export

Compliance Output

  • Data Steward Final Sign Off
  • Trusted Golden Record Set
  • Quarterly Audit Review

AI-Augmented, Human-in-the-Loop Architecture

Record Ingestion

  • Customer Vendor and Product Records
  • Deterministic and Fuzzy Pre Match

AI Adjudication Layer

  • LLM Adjudicator for Ambiguous Cases
  • AI Confidence Score
  • Confidence Threshold Met

Human in the Loop

  • Data Steward Review Queue
  • Merge Decision Correct
  • Threshold Tuning Feedback
  • Human Approved Merge

Delivery

  • Merge Applied to Golden Record
  • Audit Trail Entry Created
  • Downstream Systems Updated

Integration-Heavy / Ecosystem-Connected Architecture

Client Systems

  • Salesforce CRM Records
  • ERP Vendor Records
  • Marketing Platform Contacts

Resolution Core

  • Master Data Management Hub
  • Multi Stage Entity Resolution Engine
  • Golden Record Store

Two Way Sync Layer

  • CRM Record Sync Back
  • ERP Record Sync Back
  • Sales Operations Team
  • Finance Operations Team

Shared Governance

  • Joint Data Quality Review
  • Published Master Record Set

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Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.

Talk to us about Entity Resolution & Data Deduplication Pipelines

Most engagements like this start as a $500–$2,500 pilot, see full pricing.

Questions? Book a free call