Built to Fix the Hardest Master Data Problems
MasterFile AI was created to solve a persistent challenge faced by finance, data, and operations teams: inconsistent, unreliable vendor and customer master data across systems.
Why MasterFile AI Exists
Organizations rely on vendor and customer master data for payments, reporting, analytics, compliance, and integration. Yet this data is often fragmented, inconsistent, and difficult to trust.
Traditional cleanup methods rely on manual rules, static reference files, or one-off consulting projects that are expensive, slow, and hard to repeat.
MasterFile AI was built to provide a faster, more consistent, and transparent alternative — using AI to standardize, enrich, and validate master data at scale while adhering to recognized industry standards.
What Makes MasterFile AI Different
1 100% AI-driven processing with no manual cleanup
2 Industry-standard methodologies, not proprietary guesswork
3Designed for repeatable, production-scale use
4 Dual AI engines to balance speed and deep reasoning
5 Field-level confidence scoring for full transparency
6 Its easy to use
Every file is processed using the same workflow and standards, regardless of size or customer.
Built for Enterprise Data Teams
MasterFile AI is designed for teams that depend on accurate master data to support critical business processes.
Vendor and customer master data cleanup
Accounts Payable and Finance analytics
ERP migrations and consolidations
Mergers and acquisitions data integration
Mergers and acquisitions data integration
Audit, compliance, and reporting readiness
A Commitment to Transparency and Standards
MasterFile AI follows a structured, repeatable process designed to clean, enrich, and validate vendor and customer master data with accuracy, transparency, and confidence at every step.
MasterFile AI does not operate as a “black box.”
Standardization and enrichment are performed using recognized industry standards, and every output includes confidence scores so customers can evaluate data quality and make informed decisions.
This approach allows teams to validate results, apply internal thresholds, and integrate cleaned data with confidence into downstream systems.
See How MasterFile AI Works on Your Data
Upload a small sample and review the results before committing to a full file.