Three Methods
Match logic for every dataset.
We pick the right method — or combine them — based on your keys, your tolerance for false positives, and your goal.
Exact Match
Merging datasets using literal matching with one or more match keys.
- Single-key or multi-key joins
- Deterministic, reproducible results
- Fastest method, ideal for clean keys
Proprietary Matching
Our proven proprietary match logic, used in hundreds of matches and billions of records. Validated to be the most inclusive and accurate match technology.
- Validated across billions of records
- Most inclusive and accurate technology
- Hundreds of production matches
Fuzzy Matching
AI-based matching used for incremental (non-literal) records — catches typos, transpositions, and variants.
- Catches typos and abbreviations
- Handles name and address variants
- Confidence scoring on every match
Use cases
Where teams use Data Matching.
Anywhere two datasets need to be reconciled, we replace error-prone manual joins with deterministic, validated matching logic.
Single customer view
Collapse duplicate CRM accounts across multiple sources into one master record.
Cross-channel dedup
Combine direct-mail, email, and SMS lists without overlapping records.
Post-acquisition merge
Reconcile two company databases after an acquisition — customers, vendors, suppliers, properties.
Person matching
Link records across sources (using such fields as name + address), even when fields are inconsistent.
Battle-tested
Billions of records matched, every year.
Our proprietary engine has been validated across enterprise customer files, voter rolls, healthcare networks, and direct-mail campaigns for over a decade.
FAQ
Common questions
How do you decide between Exact, Proprietary and Fuzzy matching?
We consult with you to understand your tolerance for ‘mis-matches’. We create the three tiers and report back — you decide what is best for you.
Do you offer consulting services?
Yes, we have experienced consultants who can work with you to integrate advanced matching into your environment.
What is the typical match rate on a real-world customer file?
Of course, the answer is: ‘it depends’. While there is some level of variability, we see match rates ranging from 60–90% for exact matching to 70–100% for fuzzy matching.
Do you provide a confidence score for each match?
Yes — every matched pair returns a 0–100 confidence score so you can set your own thresholds for auto-merge vs manual review.
Can matching run on an ongoing basis (not just one-time)?
Absolutely — we offer subscription matching for teams that need to deduplicate new records daily, weekly, or in real-time via API.
What about privacy when matching sensitive data?
We support hashed-key matching for PII-sensitive workflows. Names, SSNs, and DOBs never leave your environment in plaintext.