Industry-Standard Data Cleanup, Powered by AI

MasterFile AI applies globally recognized data standards and enterprise master data management principles to clean, enrich, and validate vendor and customer master data — with full transparency and confidence scoring.

Data philosophy & approach

MasterFile AI is built on the principle that master data quality must be measurable, explainable, and repeatable.

Rather than relying on opaque “black box” AI outputs, our platform applies established industry standards and master data management (MDM) methodologies, augmented by dual AI engines to handle edge cases and ambiguity.

Each standardized or enriched field is evaluated independently and assigned a confidence score, allowing customers to understand exactly how reliable each data point is.

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Standardization Reimagined

We transform fragmented vendor and customer data into a consistent, enterprise-ready canonical format.

01 Legal Entity Normalization
Standardizing suffixes like Inc, LLC, and GmbH without losing business identity.
02 Syntactic Cleanup
Removing irregular casing, punctuation, and extra whitespace automatically.
03 Alias Intelligence
Mapping common abbreviations and nicknames back to their legal master file.
Normalization Engine Active
RAW: starbucks_corp_ltd
Starbucks Corp Ltd.
RAW: APPLE inc.
Apple Inc.
RAW: m-soft-global
Microsoft Global
Goal: Canonical name suitable for reporting, analytics, and system integration — without distorting legal identity.

ADDRESS, PHONE & EMAIL STANDARDS

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Address Standardization

Address data is standardized using international postal and addressing standards, including UPU S42 and ISO 19160.

MasterFile AI:

  • Normalizes street, city, region, postal code,and country
  • Applies country-specific formatting rules
  • Flags incomplete or ambiguous addresses

The result is globally consistent, mail-ready address data suitable for compliance, payments, and analytics

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Phone Number Standardization

Phone numbers are standardized using ITU E.164 formatting..

MasterFile AI:

  • Applies country codes
  • Removes invalid characters
  • Normalizes extensions where present

This ensures consistent phone data across systems and geographies.

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Email Validation

Email addresses are validated and standardized using RFC 5322 rules.

MasterFile AI checks:

  • Structural validity
  • Domain syntax
  • Common formatting issues

Invalid or questionable emails are flagged with lower confidence scores.

DOMAIN, PARENT & NAICS ENRICHMENT

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Domain Identification

MasterFile AI identifies corporate domains using ICANN-compliant domain rules and AI-based entity matching.

If initial confidence thresholds are not met, a second AI engine performs deeper reasoning to validate the most likely domain.

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Parent–Child Relationships

Parent-company relationships are identified using AI-driven entity resolution.

MasterFile AI evaluates:

  • Corporate naming patterns
  • Domain hierarchies
  • Public business signals

Parent relationships are provided with confidence scoring to support reporting and consolidation use cases.

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NAICS Classification

Industry classification is assigned using NAICS 2022 standards.

MasterFile AI analyzes business descriptions, naming signals, and contextual indicators to assign the most appropriate NAICS code with confidence scoring.

Duplicate Detection

Duplicate records are identified using MDM-style clustering and similarity scoring.

01 Standardized names
02 Addresses
03 Domains
04 Contact data

This approach identifies true duplicates while minimizing false positives.

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Confidence Scoring

Every standardized or enriched field is assigned a confidence score from 0 to 100.

01 Understand data reliability
02 Apply internal review thresholds
03 Prioritize manual review when needed

This transparency is central to MasterFile AI’s design philosophy.

See These Standards Applied to Your Data

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