CAPABILITIES

Document Intelligence & Enterprise Search

Retrieval systems at real scale: a 30M-document Elasticsearch index at Newsweek, financial OCR for PE at Cepres, 66GB of retail catalogs at BuildClub, 100k+ LOINC codes at N1Health.

PRACTICE NO.
04
PRINCIPALS
NB
CASES ON FILE
01
FIELD
NLP & DOCUMENT AI · MEDIA & PUBLISHING · PRIVATE EQUITY ANALYTICS · LEGAL TECH · HEALTHCARE OPERATIONS
Document Intelligence
FIG. 01 · NEIGHBORHOOD MAP · SOURCE: TEAM EXPERTISE DATASET

PROBLEM SPACE

Search, extraction, and understanding across millions of documents.

Retrieval systems at real scale: a 30M-document Elasticsearch index at Newsweek, financial OCR for PE at Cepres, 66GB of retail catalogs at BuildClub, 100k+ LOINC codes at N1Health.

ROUTINE CLEARS AUTOMATICALLYEDGE CASES ROUTE TO A REVIEWER

WHAT WE DELIVER

  • 2.1RAG pipelines
  • 2.2vector + graph search
  • 2.3OCR & multimodal extraction (PDF, image, video frames)
  • 2.4knowledge bases
  • 2.5retrieval-quality eval harnesses
30M DOCUMENTS INDEXED

HOW IT SHIPS

Systems in this practice run as a loop, not a launch. Inputs are retrieved, routine outcomes execute automatically, and anything consequential holds at a review gate where a named person clears it with context attached. Every decision (human or automatic) lands in the audit trail.

INPUTRETRIEVEGATEEXECUTEAUDITHUMAN REVIEW · CONSEQUENTIAL ONLY
THE ENGAGEMENT MODEL BEHIND THIS →

PROOF

30M
documents in one search indexNEWSWEEK
66GB
retail catalogs processedBUILDCLUB
CASE FILE 01 · NEWSWEEK · DOCUMENT INTELLIGENCE AT NEWSROOM SCALE · 30M-DOC SEARCH
EVERY FIGURE TRACES TO SHIPPED WORK

WHO BUILDS IT

Nikhil BeryPrincipal · RAG & Document AIRAG, document intelligence, multi-LLM pipelines, vector + graph search at scale. 30M-doc Elasticsearch indexes.PROFILE →

ADJACENT

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