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.
Vendor and customer names are standardized using enterprise MDM principles designed to reduce variation while preserving legal and business identity.
MasterFile AI normalizes:
- Legal entity suffixes (Inc, LLC, Ltd, GmbH, etc.)
- Punctuation, casing, and spacing
- Common abbreviations and aliases
The goal is to produce a consistent, canonical name suitable for reporting, analytics, duplicate detection, and system integration — without over-normalizing or distorting legal identity.
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.
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.
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.
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.
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.
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 records are identified using MDM-style clustering and similarity scoring.
MasterFile AI evaluates combinations of:
- Standardized names
- Addresses
- Domains
- Contact data
This approach identifies true duplicates while minimizing false positives.
Every standardized or enriched field is assigned a confidence score from 0 to 100.
Confidence scores allow customers to:
- Understand data reliability
- Apply internal review thresholds
- Prioritize manual review when needed
This transparency is central to MasterFile AI’s design philosophy.
