Document Processing · Enterprise Knowledge Search
Squary AI needed to eliminate slow, outage-prone legal search infrastructure that combined MongoDB with OpenSearch and hurt document review speed, cost, and trust.
Reported impact
Squary AI now handles 10x more data for less cost, returns search and generation results in single-digit seconds, has seen zero outages since migration, and cut document review time by up to 70%.
Squary AI consolidated its legal-workflow platform onto MongoDB Atlas by moving text search and vector search off a separate OpenSearch deployment and into MongoDB Search and MongoDB Vector Search on Atlas. The platform stores case documents in Atlas, indexes existing collections directly without schema rewrites, and used a short dual-write period to validate the migration before cutover. MongoDB Search provides full-text relevance while MongoDB Vector Search supplies embedding-based retrieval for hybrid search. Those retrieval layers feed retrieval-augmented generation workflows that use Claude or Amazon Bedrock depending on client sensitivity. The platform also attaches citations to every generated claim so attorneys can verify outputs against page-level evidence inside very large document sets. By unifying JSON data, search, vectors, and observability on one platform, Squary AI replaced a fragile multi-system stack with a simpler architecture that scales proactively and keeps legal teams working.
Legal documents live in MongoDB Atlas, where MongoDB Search and MongoDB Vector Search on Atlas index the same collections for hybrid retrieval; relevant passages are sent to Claude or Amazon Bedrock-backed generation, and the application returns source-linked answers with page-level citations attorneys can verify.
“Search latency improved just as dramatically. Where OpenSearch query times had stretched up to 15 seconds, MongoDB now returns search and generation results consistently under 10 seconds.”
“Squary AI can attach a citation to every claim in a generated report.”
“The migration was surprisingly fast; we adapted our code in a matter of days.”
“With MongoDB, we now handle 10 times our previous data volume for less money.”
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Pittsburgh, Seattle & Raleigh | 412.606.1219 | info@squary.ai
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