Catch matches hidden by typos, nicknames, abbreviations, and inconsistent formatting. MatchLogic combines transparent fuzzy matching with offline AI that recommends cleansing and matching rules, while you control thresholds and final logic.
Records matched
Match accuracy
Time to first profile
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MatchLogic lets you see how similarity is calculated and tune how strict each comparison should be. Use fuzzy methods where they make sense, combine them with exact logic, and keep every result reviewable.
Identify records that belong together even when names, addresses, or other values contain typos, abbreviations, nicknames, phonetic variations, or formatting differences.
Tune how strict or flexible matching should be based on the risk of missing a valid match versus accepting a questionable one.
Give reviewers field-level scores and evidence so they can understand borderline matches without reverse-engineering an unexplained result.
Use offline AI to reduce setup work, then extend automation through MCP so an approved LLM can operate cleansing and matching workflows without accessing the underlying records.
Profile, clean, configure, score, review, and export in one workflow, with algorithms, thresholds, and evidence visible throughout.
Combine exact, fuzzy, phonetic, and other comparisons based on the fields and variations in your data.
Get suggested cleansing rules and match definitions based on the structure and content of your data.
Control how similar values need to be before they count as a match, then refine thresholds as needed.
Inspect how individual fields contributed to a match instead of relying on an unexplained verdict.
Send match results downstream through export or API for recurring and operational workflows.
Bring records with typos, abbreviations, spelling variations, nicknames, and formatting differences. See how MatchLogic identifies matches that exact rules miss and inspect the logic behind the results.
1,204 duplicate pairs
96.4% confidence
312 master records
18 fields compared“I pulled our conquest mail list and found a man we were paying to win back. He had bought three trucks from us, and he had a service appointment that same week. We were spending real money chasing a customer who had never once left.”
Director of Marketing and Customer Retention, Ancira Auto Group
MatchLogic can compare values affected by typos, spelling variations, nicknames, abbreviations, phonetic differences, and inconsistent formatting.
Its offline AI analyzes your data and recommends cleansing rules and match definitions. The fuzzy matching logic and thresholds remain transparent and configurable.
Yes. You can adjust thresholds and matching logic to make results stricter or more inclusive based on your data and review requirements.
Yes. The offline AI can run behind your firewall with no cloud calls, keeping sensitive matching data under your control.
Yes. An approved LLM can operate cleansing and matching workflows through MCP without being given access to the underlying records.
Yes. Bring a representative sample with the variations you need to catch, and the walkthrough can focus on how MatchLogic scores and explains those records.
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