Gregor Wynne Arney (GWA) PLLC faced a $35M healthcare fraud and anti-kickback investigation with over one million documents to process. With DecoverAI's embedded ingestion pipeline and custom corpus architecture, the entire document set was searchable and queryable in days—not weeks.
Gregor Wynne Arney (GWA) PLLC is a white collar crime practice whose clients face existential stakes. When GWA was retained on a $35M healthcare fraud and anti-kickback investigation, the document set that came with it was staggering: over one million documents spanning billing records, clinical notes, communications, contracts, and regulatory filings.
Standard e-discovery platforms aren't built for this. The ingestion time alone would consume weeks of the matter timeline, and the resulting corpus would be difficult to query with the precision a healthcare fraud case demands. GWA needed something purpose-built.
DecoverAI's embedded team built a custom ingestion pipeline designed around the specific document types, metadata structures, and query patterns the investigation required. The result: a fully searchable 1M+ document corpus that GWA's attorneys could interrogate directly—with a single question—from the earliest stages of the matter.
GWA evaluated their options against four non-negotiable criteria. Standard platforms failed on all four.
DecoverAI embedded with GWA to build a custom ingestion pipeline designed around the specific document types, metadata structures, and query patterns the case required. The embedded team didn't hand GWA a manual and a login—they built the system alongside GWA's attorneys and then remained available throughout the matter to refine it as the case evolved.
The result was a corpus that GWA's attorneys could interrogate directly. A question that previously required hours of review across thousands of documents on multiple e-discovery platforms could now be answered with a single precise query.
That's not a marginal efficiency improvement. It changes how the firm works: attorneys spend their time on legal strategy, not document retrieval. And they arrive at key facts earlier in the matter—when it matters most.
The pipeline was built around the specific document types in this matter: billing records, clinical notes, communications, contracts, and regulatory filings—each classified and indexed for the queries GWA needed to run.
The system was trained to parse medical billing codes, procedure classifications, and clinical language—making searches for fraudulent billing patterns precise rather than approximate.
A single natural-language question surfaces the relevant documents across the full 1M+ corpus. No keyword iteration, no platform-hopping, no manual page-turning.
GWA didn't operate alone. The DecoverAI team remained embedded throughout the matter, tuning the system as the case developed and new document sets arrived.
Obtaining the correct information on queries that used to take several hours of reviewing and analyzing 1,000s of documents on various e-discovery platforms is now possible in a single question.
DecoverAI embedded alongside the trial team for a five-week jury trial, surfacing contractual provisions and deposition inconsistencies.
Medical chronologies, record reviews, settlement demands, and interrogatory responses—all streamlined with a custom engine.
End-to-end production with privilege logs, Bates stamps, and redactions—saving $147K and 25 days.
Full production remediation with corrected Bates numbering, privilege logs, and metadata under federal scrutiny.
See how DecoverAI handles the most demanding white collar investigations.
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