No quick matches. Press enter to see full results.
No quick matches. Press enter to see full results.
We find, match and resolve duplicate, near-duplicate and inconsistent records within your large, disorganized data sets, using judgment, not blanket deletion.
The situation
Records conflict, duplicate or carry errors, and there’s no way to tell which version is current. Teams stop trusting the information they’re working from.
Why this is different
Serves organizations with messy digital-native records that never had a scanning need at all.
Not gated behind a scanning engagement. This is DataBank’s first offering built to serve this market directly.
Protects your records from being erased just because they look redundant on the surface.
Deduplication logic accounts for cases where a duplicate is a signal, such as potential fraud, rather than pure noise.
What’s included
The baseline layer. Identical files produce identical hashes, so true duplicates are flagged cheaply and mostly automatically. It’s a baseline capability, but not where our value sits, since cloud storage already does this.
The real work. Finding records that represent the same underlying thing without being identical, like two versions of the same contract where one is signed and one isn’t, or two files describing the same asset under different naming conventions.
A near-duplicate isn’t automatically noise. Sometimes it’s a meaningful variant worth keeping, like the signed version over the draft. Sometimes the duplicate itself is the signal: in insurance, a duplicate submission can indicate fraud rather than redundancy.
Across inconsistent formats, structures and sources.
Correcting, completing and reconciling conflicting records.
AI extraction runs at over 98% accuracy, but anything that doesn’t meet your accuracy or compliance bar doesn’t get accepted on faith. Industry specialists who understand your sector’s compliance standards review it at the same SOC 2 Type II certified facilities as every other DataBank engagement, and that feedback tunes the models for next time.
What you get
Questions we hear
Cloud storage catches identical copies. The harder work is finding records that describe the same thing without being identical, like a signed contract and its unsigned draft, and showing which one is current.
No. A near-duplicate isn’t automatically noise. We apply judgment rather than blanket deletion, since sometimes a duplicate is a meaningful variant worth keeping, or even a signal, like a duplicate insurance submission that can indicate fraud.
No. This works on large, disorganized data sets with inconsistent formats, structures and sources, including content that’s always been digital and never had a scanning need at all.
Both. AI does the first pass across your records, and anything that doesn’t meet your accuracy or compliance requirements goes to subject matter experts for review before it’s considered resolved, not accepted on the model’s word alone.
Where this connects
Once your records are matched and trustworthy, Document Classification & Metadata Enrichment makes them findable and governable. Then Manage is where they live long term.
Start the conversation
Tell us where the conflicts show up, and we’ll scope what resolving them looks like.
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