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Hire a RAG Developer · New York, NY
A junior associate at a Big Law firm in New York bills $400–500/hour. A significant portion of that time goes to document research: pulling relevant clauses from commercial contracts, locating specific language across a portfolio of SEC filings, or cross-referencing analyst reports with earnings call transcripts.
That is not a people problem. That is a retrieval problem. A RAG system built for financial and legal document libraries returns the relevant passage in under 10 seconds, with a citation to the exact paragraph.
Pricing for this type of engagement depends on document volume and citation requirements, and we scope it after understanding your library.
Describe your document search problem and we will scope a solution.
New York financial firms deal with document volumes that make manual search impossible at scale. A single credit research team at a mid-size asset manager might track 300+ issuers, each with annual 10-Ks, quarterly 10-Qs, 8-K filings, earnings transcripts, analyst reports, and internal credit memos. That is tens of thousands of documents, many with complex table structures and financial footnotes.
The challenge is not just volume. It is specificity. An analyst asking "what covenants apply to the revolving credit facility" needs the precise clause from the credit agreement, not a summary. A lawyer asking "has the client ever agreed to unlimited indemnification in this industry" needs a yes/no with the exact contract language that justifies the answer.
Generic keyword search fails at this precision level. Full-text search does not understand synonyms ("net debt" vs. "total indebtedness"), cannot filter by document type and date simultaneously, and does not return a grounded answer with a citation. A well-built RAG system does all three.
The same problem appears in commercial real estate. A CBRE or JLL analyst reviewing lease abstracts for a portfolio company needs to know "which leases have co-tenancy clauses with Macy's as the anchor tenant." Pulling that answer manually from 200 lease documents takes a full day. A RAG system on the lease abstract library returns it in seconds.
Healthcare is a third vertical. Mount Sinai, NYU Langone, and Northwell Health each have enormous clinical policy libraries, payer contract portfolios, and compliance documentation. A compliance officer needing "does our Aetna contract permit out-of-network billing for this DRG code" is solving the same document retrieval problem as the finance analyst.
A question that takes 45 minutes of manual research returns in under 10 seconds. The answer includes a citation to the specific paragraph so the analyst can verify it immediately.
10-K exhibits, footnote tables, and XBRL-tagged financials require a different extraction path than standard text. We preserve row-column relationships so queries on structured financial data return accurate values.
Filter by document type, filing date, issuer, or jurisdiction before the semantic search runs. 'Show me only Q3 2023 filings' is a metadata pre-filter, not a prompt instruction. It is precise and fast.
Every answer includes the document name, section, page number, and paragraph index. An analyst can click through to the exact source without re-reading the full document.
When a credit memo or contract is updated, the previous version's chunks are marked superseded, not deleted. Historical queries can specify a date range and retrieve the correct version.
When retrieval quality is low (the question is ambiguous or no relevant content exists in the corpus) the system says so. It does not generate a confident-sounding wrong answer.
Five specific components, each designed for the document types common in financial services, legal, and healthcare.
SEC EDGAR integration, earnings transcript parsing (with speaker labels), credit memo extraction, and lease abstract parsing. Table-aware extraction via pdfplumber or unstructured.io preserves financial table structure rather than flattening rows into undifferentiated text.
Commercial contracts are structured: article, section, subsection, clause. Chunking at the clause level while preserving parent-section context means a query on a specific provision returns the clause text plus its governing article, giving the model enough context to answer accurately.
BM25 keyword search combined with dense embeddings (OpenAI text-embedding-3-large or a fine-tuned domain model). Metadata filters on document_type, filing_date, issuer_ticker, and jurisdiction run before the vector search, keeping results precise without reducing recall on the filtered subset.
A Cohere Rerank or BGE Reranker pass on the initial retrieval set raises precision by 10–15% on enterprise documents. This matters most for ambiguous queries where multiple documents contain partial matches, the re-ranker selects the most relevant chunk for the actual question.
Before handoff, we build a test set of 100+ question-answer pairs from your actual documents and run recall@5, answer accuracy, and citation correctness metrics. You get a benchmark number before you deploy to users, not a demo.
Tell us what document types you have, roughly how many, and what questions users need to answer. We reply within one business day with a rough scope and price range.