Built for the hardest data matching problems

Data matching is one of those problems that looks simple on the surface — just compare two lists — but becomes genuinely hard in practice. Names are misspelled. Products have different SKUs in different systems. Addresses are abbreviated inconsistently. Fields are missing.

Traditional approaches rely on rigid rules, SQL joins, or manual review. They break on messy data and don't scale. Match Data Studio takes a different approach: use AI to understand what should match, not just what literally looks the same.

The approach

We built an eight-stage pipeline that combines the best of several matching techniques: vector embeddings for semantic similarity, string algorithms for precision on codes and IDs, numeric matching for dates and prices, and LLM confirmation for ambiguous pairs.

The AI assistant configures this pipeline for your specific dataset. Describe your data and what a match means to you — the assistant translates that into a working configuration you can review and edit.

Self-service by design

Match Data Studio is designed to be used without a data engineer or ML practitioner. Upload two CSVs, have a short conversation with the AI, run a sample to check results, then process your full dataset. The pipeline catches matches that rule-based tools miss — across any dataset size.

Get in touch

Questions about the product, pricing, or a large-scale use case? Contact us and we'll get back to you.