Every organization keeps knowledge in contracts, scanned pages, attachments and long files spread across folders and systems. A folder can hold the right evidence while an assistant still cannot find it: a table was omitted, a scan was never read, or the relevant passage fell outside an analysis window. Embedding Labs works on this problem at the data foundation: we recover document content and add context before preparing it for retrieval. We first built it for law firms' case files, then applied the same foundation to the documents of an industrial company.
01 / The work
A stored file is only the beginning
A document may combine selectable text with scanned pages. Dates and obligations can appear in tables or marginal notes. The work is to recover the information a later question will need, while keeping its relationship to the original source.
The components work with documents and images: PDF, Word and plain-text files, scanned pages and page images.
02 / The work
Recover the content, then describe its meaning
The team built readers for PDF, Word and plain text, with OCR and image-processing alternatives. A model then produces a summary and entities such as parties, dates and topics; for the law firms, these included jurisdiction and case numbers. Extracted text and contextual metadata have distinct jobs: one preserves the material, the other helps explain what it concerns.
A separate PDF processor reads rendered page images and prepares the resulting text for a vector store, giving retrieval by meaning an indexing component.
03 / The work
Make the missing passage visible
The practical question for a reviewer, a lawyer in the first example, is whether the source meaning survived. A plausible summary is insufficient when a missing clause changes the answer. Whole-document coverage, correct entities and useful search results each require their own check.
The implementation gives the team concrete intermediate artifacts to examine. Each design choice carries a tradeoff: fast direct extraction can miss mixed scans, and bounded model input can leave part of a long record unanalyzed.
04 / The work
Build a foundation that can be evaluated
This work built extraction and enrichment components and a separate document-to-index path. For the law firms, the goal was contextual knowledge that supports legal research and drafting. The same foundation serves any team whose knowledge sits in scattered documents.
The transferable lesson is to check each transition. Recovering text, describing context, processing an index and retrieving the right passage are different steps, and a dependable knowledge workflow makes each one visible.
Our knowledge is spread across documents in many different formats. This system brings those materials into a searchable knowledge base that people and AI assistants can question, making it easier to find relevant information and work with the knowledge we already have.
The lesson
Recover the source meaning before asking retrieval to find it.
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