Deduplicate millions of records in minutes with AI-recommended match rules. Catch misspellings, nicknames, and format variants that exact matching misses. Review every duplicate group before anything is removed.
Start Finding Duplicates
Find duplicates that exact matching misses. Misspellings, nicknames, and format variants group together across one file or every system you connect.
MatchLogic reads your data and proposes which fields to match on. Every suggestion carries a reason and a confidence rating. Edit anything before you run it.
Every duplicate group shows its match score and field evidence. Approve, override, or skip. Nothing is deleted without confirmation.
“As part of the journey we’ve gone through with Matchlogic, we’re becoming more data-first, moving from assumption to assurance around data quality.”
Score names, addresses, emails, and phone numbers with the algorithm each field needs. Set weights per field and adjust thresholds without code. Every match score breaks down to the fields that produced it.
The Match Definition Recommender reads your columns and proposes which fields identify a record, which tolerate variation, and which add noise. Every suggestion is checked against what the platform can actually execute, so the AI cannot propose a configuration that will not run. Edit anything, approve what works, and run the AI in the cloud, fully offline, or switched off entirely.
Comparing a million records against each other means 500 billion pairs. MatchLogic blocks records into candidate groups first using phonetic keys, ZIP prefixes, or any field you choose. Widen the keys for more recall, tighten them for speed.
Source data stays intact through every run. Deduplication outputs a unique file, a duplicate file, and a decision log. Change a threshold and rerun instead of restoring a backup.
See every duplicate in seconds. MatchLogic scans all sources at once, scores each record pair, and clusters matches into duplicate groups. Sort by score to review the ambiguous ones first.
Every group gets an ID, a record count, and a top match score. Groups spanning multiple systems are flagged automatically.
See which fields drove each match and by how much. A 91 percent score breaks down to name, address, and phone contributions.
Set the score ranges that auto-resolve, route to review, or stay untouched. Adjust a threshold and records shift bands.
Matching, conflicting, and empty fields are color-coded across every pair. Accept, reject, or defer a whole band at once.
Configure deduplication without writing a rule. MatchLogic reads your data, proposes the cleansing steps and the match definition, and explains every choice. Nothing runs until you approve it.
MatchLogic identifies what a column actually contains, whether it is a full name, a mailing address, or a tax ID. Header labels like cust_ref_2 or f_nm do not matter.
The Match Definition Recommender picks strong identifiers for direct matching and configures name matching that tolerates nicknames and typos. It also flags the fields that would add noise.
Every suggestion states why it was made and how confident it is. Accept, edit, or skip each one individually.
Run the AI in the cloud for the strongest suggestions, fully offline inside your environment, or switched off with non-AI suggestions still available.
Export everything the run produced. MatchLogic generates a deduplicated file, a duplicate file, and a full decision log. Write results back to CSV, Excel, SQL databases, or CRM systems.
Export deduplicated records and removed records separately. Nothing is discarded, so any decision stays reversible.
Every exported record carries its duplicate group ID. Rejoin records downstream without rerunning the match.
Export the match definition, thresholds, scores, reviewers, and outcomes for every run.
Schedule deduplication to run hourly, daily, or triggered by new data loads. MatchLogic applies your saved match definition without manual intervention. Get alerts when duplicate rates spike.
Call deduplication from ETL pipelines or applications at record creation. Catch duplicates at point of entry, not at month end.
Run deduplication nightly, weekly, or on data load through the Workflow Scheduler.
No more conflicting record counts. Upload your data and see the recommended match rules, duplicate groups, and field evidence instantly.
Start Finding Duplicates
MatchLogic identifies records that refer to the same customer, vendor, or product across your data. It scores every field, groups matching records, and logs every decision for review. This is record-level deduplication for CRM and database records, not storage block deduplication.
Yes. MatchLogic runs entirely on your servers and no record leaves your environment to be matched. This meets HIPAA, SOX, GDPR, and DORA data residency requirements. Private cloud and containerized deployments are available, and the AI features include a fully offline mode.
AI handles setup, not matching. The Match Definition Recommender reads your columns and proposes which fields to match on, with a reason and a confidence rating on every suggestion. You edit and approve it. The matching that follows is rule-based, transparent, and repeatable.
Deduplication finds and groups duplicate records. Merge purge decides which values survive when a group consolidates into a single record. Entity resolution, handled by MatchSense, links records to real-world entities across systems over time. Most projects run deduplication first, then merge purge.
Yes. Match on name, address, email, phone, date of birth, or any combination of fields. Fuzzy and phonetic algorithms catch variations that exact matching misses. Records with no shared identifier still group correctly across systems.
Only if you choose the cloud mode. MatchLogic offers three options: cloud for the strongest recommendations, fully private and offline for air-gapped or sensitive environments, or AI switched off entirely with non-AI suggestions still available. The deduplication itself always runs on your servers.
.svg)
