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Hire an LLM Engineer, New York
Junior analysts at New York investment banks and law firms spend 4 to 6 hours extracting structured data from financial documents: earnings releases, 10-K and 10-Q filings, credit memos. The fully loaded cost of that analyst hour is $500 or more at firms like Goldman Sachs Research or Skadden. An LLM pipeline with schema-validated structured outputs does the same extraction in seconds per document.
The engineering problem is not the LLM call. It is the pre-processing step: financial tables in PDFs require specialized extraction before the model sees them. We build the full pipeline, from PDF parsing through validation to your database.
Pricing is scoped to the engagement, depending on document complexity and extraction schema depth.
Tell us which documents you need to extract from.
A Goldman Sachs Research analyst or a Sullivan and Cromwell associate extracting structured fields from a single earnings release takes 45 to 90 minutes. The document is unstructured: revenue, EBITDA, guidance, and segment data are scattered across prose paragraphs, footnotes, and tables. Copy-paste into a spreadsheet is the current process at most firms.
At 200 documents per month, that is 200 analyst-hours. At $500/hour fully loaded: $100,000 per month spent on manual data entry. The same 200 documents run through an LLM extraction pipeline in under an hour, with a documented accuracy rate and schema-validated output.
The financial document extraction problem has three layers. First: PDF parsing. Standard parsers lose table structure. We use pdfplumber for digital PDFs and Textract for scanned documents, preserving column-row relationships before the LLM sees the data.
Second: field definition. The schema for a 10-K extraction is different from a credit memo. We define typed JSON schemas with required fields and acceptable value ranges, and the LLM output is validated against them before writing to the database.
Third: confidence scoring. Ambiguous extractions are flagged for human review rather than silently written with wrong values. Accuracy on well-formatted documents runs 95% or above. Edge cases get a human queue.
~$0.40
API cost per 50-page document vs. $500+ analyst hour
95%+
Field-level extraction accuracy on well-formatted financial documents
4–6 wks
Typical build time from PDF samples to production pipeline
Every financial document extraction engagement covers these components. Nothing is optional.
pdfplumber for digital documents, AWS Textract for scanned. Table structure is preserved as structured data before any LLM call.
JSON Schema definitions for every document type: 10-K, 10-Q, earnings release, credit memo. Fields are typed with acceptable value ranges.
Structured output prompts using OpenAI function calling or Anthropic tool use. The model returns typed JSON, not prose.
Every extracted value is cross-checked against source position. Anomalies flag to a human review queue rather than writing silently.
Each field has a confidence score. Low-confidence extractions are held back. You see the accuracy rate per document type.
Output writes to your existing database schema. We document the field mappings and build the insert logic to match your data model.
Share a sample of the documents you need to extract from and the fields you need. We will reply with a scope and price range within one business day.