CAPABILITIES

Document Intelligence & Enterprise Search

Retrieval systems at real scale: a 30M-document Elasticsearch index at Newsweek, financial OCR for PE at a private-markets data platform, 66GB of retail catalogs at a construction-commerce marketplace, 100k+ clinical codes at a clinical-intelligence company.

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 a private-markets data platform, 66GB of retail catalogs at a construction-commerce marketplace, 100k+ clinical codes at a clinical-intelligence company.

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 processedCONSTRUCTION-COMMERCE MKT
CASE FILE 01 · NEWSWEEK · DOCUMENT INTELLIGENCE AT NEWSROOM SCALE · 30M-DOC SEARCH

SELECTED PROJECTS · CLIENTS ANONYMIZED

Millions of securities, one grounded answerA wealth-management fintechReading private-markets statements at scaleA private-markets data platformRaw scans to a clinical reportA clinical-intelligence companyA week of analysis in under an hourA market-intelligence startupGrading nine thousand research recordsThe global agricultural-research consortium
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 →

FIELD NOTES

Document intake in an accounting practice: what actually eats the hours6 MIN READDocument intelligence for clinic groups: intake without the retyping5 MIN READRetrieval at 30 million documents5 MIN READ

ADJACENT

Asked about this practice

Questions we get.

The same answers we give on a first call about this practice.

  • FAQ-01How large a corpus can you search reliably?

    We have built retrieval at real scale: a 30M-document search index at Newsweek and 66GB of retail catalogs at a construction-commerce marketplace. Scale is the normal starting point for us, not a stretch goal.

  • FAQ-02Our documents are messy scans and PDFs. Can you extract them?

    Yes. We run OCR and multimodal extraction across PDFs, images, and video frames, then normalize the results into a knowledge base. Extraction quality is the foundation: if it is sloppy, no retriever downstream can compensate for it.

  • FAQ-03How do we know the search is actually good?

    We build a retrieval-quality eval harness from the questions your people actually ask and judge answers against it. Search quality is an evaluation problem before it is an infrastructure one, so we measure it rather than assert it.

OPEN A NEW FILE

Tell us the use case. One call is enough to scope whether there is a fit, and what it takes to ship.

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