AI Agent

Customer Data Management AI Agent

Maintain golden customer records without manual deduplication

"Which customer record is correct?"; the question data teams will stop asking. Continuous deduplication and golden record management.

Benefits

End Duplicate Customer Records Forever

Duplicate and inconsistent customer records cause failed deliveries, missed revenue, and poor customer experience. Manual data cleansing is reactive and never-ending. This agent maintains golden records continuously.

30%

Data quality improvement

99%

Duplicate detection rate

Real-time

Data cleansing

Agentic Flows

Complete MDM Automation

Automated workflow: continuous duplicate detection across systems, match scoring with confidence levels, merge proposals with business rule application, golden record creation and maintenance, data enrichment from external sources, quality monitoring.

How it works

AI-Powered Master Data Management

The agent detects duplicates using fuzzy matching and ML algorithms, merges records with intelligent survivorship rules, enriches data from external sources automatically, maintains golden records with continuous quality monitoring.

Self-learning agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart model switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Self-learning agents

The agent improves with every task, adapting to outcomes, applying feedback, and self-correcting using Constitutional AI.

Upto 98% accuracy

As a result of constant feedback loops, Beam AI Agents refine their approach with every cycle, leading to 98% accuracy across flows.

Smart model switching

We call it ModelMesh. Each agent selects the right model for the task, balancing speed, accuracy, and cost in real time.

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Extract order number, seller information, delivery information, and payment details from the uploaded order forms.

Completed

ID-0E48

Integrations

Unifies Data Across All Systems

Integrates with Salesforce, HubSpot, Microsoft Dynamics, NetSuite, SAP, and custom CRM systems. Enrichment data from Clearbit, ZoomInfo, D&B. Real-time sync maintains consistency everywhere.

Implementation

Clean Data in 2 Weeks

Connect your CRM and customer systems. Define matching rules and survivorship logic. Most companies achieve 30% data quality improvement within 30 days of deployment.

Step 1

Agent discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 1

Agent discovery

Conduct workshop(s) with relevant stakeholders to identify and prioritize use cases and map the requirements

Step 2

Agent setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 2

Agent setup

Develop and launch your first agent with basic logic and integration (process scoping and recording, test dataset of 30-50 examples, baseline agent running and testing with target output mapping)

Step 3

Agent training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 3

Agent training

Test performance and gather feedback from process users (agent optimization up to 80% against expected output, variable-level accuracy measurement and optimization, integrations setup, and feedback API interfacing)

Step 4

Agent live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Step 4

Agent live

Extend to more workflows and client teams (live and continuous monitoring, human-in-the loop interfacing, node auto-tuning for automated prompt enhancements, >90% accuracy improvement, weekly business logic improvements)

Key Features

Enterprise MDM Capabilities

Fuzzy Matching

Detects duplicates despite typos, abbreviations, and format variations. ML models improve accuracy.

Survivorship Rules

Applies business rules to determine which data survives merges. Maintains audit trail of changes.

Data Enrichment

Fills missing fields from external data sources. Keeps records current with automatic updates.

Demo

See Customer Data Management in Action

Watch the agent analyze 100,000 customer records: identify 15,000 duplicates, merge with survivorship rules, enrich 20,000 records, create golden records for all.