Most financial data sits in documents. 10-Ks, 10-Qs, annual reports, filings, and credit agreements carry the figures analysts need, and each one arrives as unstructured text. Reading a full annual report takes hours, and extracting the data points into a usable form takes longer still. By the time the numbers are in a spreadsheet, the market has moved.
AI document intelligence closes that gap. An AI system reads each document, extracts the key figures, metrics, and risk disclosures, and files them as structured data with the source location attached to every field. Analysts compare periods and companies in one view, with the source page behind every figure. Building a system like this traditionally takes extensive development time and expertise. Shakudo deploys it within days, so the team starts working from extracted data on day one of the project.
Shakudo deploys an AI pipeline that turns complex financial documents into structured, searchable data. The system reads 10-Ks, 10-Qs, and annual reports, extracts the key metrics and performance indicators, and categorizes them for analysis. Every extracted figure carries the source document and location, so an analyst can trace any number back to the page it came from. The team compares financial data across multiple periods and companies for trend analysis, sets alerts on significant changes and anomalies in reporting, and finds opportunities and risks faster than manual reading allows. The result is better investment decisions, tighter financial performance monitoring, and a competitive edge in a fast-paced market.
The AI runs in the firm's own environment, so the filings, the research, and the extracted data never leave it. That matters for a firm where proprietary analysis and client work cannot be sent to a public cloud AI vendor. The pipeline ingests each new document, runs the AI extraction, and loads the results into the warehouse the team already queries, on a schedule the firm sets. New document types plug in as the coverage expands, and the AI stays fully owned and controlled from model to memory.
The pipeline runs on a stack the data teams already know. LangChain processes and understands the nuances of financial language in each document. Qdrant is the vector database that powers semantic search and retrieval across the document corpus, so any figure is findable in plain language. DBT transforms the extracted fields into clean, structured financial data, and Snowflake stores that data for analysis at scale. Rill builds fast, AI-powered visualizations of financial trends and key metrics, and N8n automates the entire workflow from document ingestion to insight distribution.
Financial analysts, research teams, and risk teams at asset managers, banks, and corporate finance groups that work from filings, annual reports, and other unstructured financial documents and need the data points as structured, comparable fields. The system fits a desk that reads every filing in its coverage and compares the numbers across periods and companies, and it gives each analyst a data table with the source page behind every row, ready to feed the models and workbooks the team already runs.
10-Ks, 10-Qs, annual reports, filings, credit agreements, and similar unstructured financial documents. The AI reads the full text, extracts the key metrics and disclosures, and files each data point with the source document and location attached, so any figure traces back to its page.
LangChain handles the financial language, and Qdrant makes the whole corpus searchable in plain language. The AI pulls each figure into a structured field, DBT transforms the fields into a clean schema, and Snowflake stores the result. The analyst queries the data like a table, and the source document sits behind every row.
Building a comprehensive financial analysis system traditionally takes extensive development time and expertise. With Shakudo, the pipeline is deployed within days, so the team starts extracting insights from day one of the project.
For financial research, that means a quarter of filings that used to take a week of reading now arrives as structured data with sources attached. Book a demo and see AI document extraction on the filings the firm already reads.