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
100% AI-driven processing with no manual cleanup
Dual AI engines to balance speed and deep reasoning
Industry-standard methodologies, not proprietary guesswork
Field-level confidence scoring for full transparency
Designed for repeatable, production-scale use
Its easy to use
Every file is processed using the same workflow and standards, regardless of size or customer.
Built for Enterprise Data Teams
Common use cases include:
- Vendor and customer master data cleanup
- Accounts Payable and Finance analytics
- ERP migrations and consolidations
- Mergers and acquisitions data integration
- Master Data Management (MDM) initiatives
- Audit, compliance, and reporting readiness
A Commitment to Transparency and Standards
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.
Pricing plans that elevate your business
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See How MasterFile AI Works on Your Data
Upload a small sample and review the results before committing to a full file.