MatchLogic Blogs

Entity resolution software identifies and links records that refer to the same real-world entity across different systems, then consolidates them into a single authoritative record even when the data is inconsistent or incomplete.

A practical walkthrough for connecting Claude or any MCP-compatible LLM to an on-premise data quality platform: clients, discovery, the first workflow, approval.

Ten data matching tools compared by deployment, algorithm transparency, and what each one actually replaces, with the criteria that eliminate candidates fastest.

Agent-operated data quality does not require cloud AI. How MCP workflows run in private networks and fully air-gapped environments, and what each posture needs.

Vendors call both "AI-ready," but documentation MCP servers and workflow MCP servers do different jobs. How to tell which one an entity resolution tool ships.

MCP servers are now near-standard across data quality tooling, but they do very different jobs. A category-by-category map of what exists and how to classify it.

How a well-designed MCP server lets an LLM run data quality workflows without reading a single record: the data wall, approval gates, and what to verify.

Agentic data deduplication lets an LLM propose and sequence dedupe runs while a human confirms each one and the data stays on-premise. See how it actually works.

Learn how an MCP server connects any LLM to on-premise data matching and entity resolution workflows while keeping your records out of the model's reach.