Kensho

Automatically extracts structured data from unstructured financial documents including SEC filings, prospectuses, earnings reports, and regulatory documents. Uses AI to identify and parse key financial metrics, dates, entities, and relationships with high accuracy.

Enables querying across years of financial data repositories using plain English questions instead of SQL or structured queries. Returns relevant financial documents, metrics, and insights matching the natural language query.

Automatically extracts key takeaways, guidance, and important announcements from earnings call transcripts. Summarizes management commentary and identifies material information.

Generates visual reports and dashboards from extracted financial data. Creates charts, graphs, and formatted reports for presentation to stakeholders and decision-makers.

Automatically transcribes earnings calls and investor events with speaker identification, timestamps, and sentiment analysis. Converts audio to searchable, analyzable text with minimal manual intervention.

Consolidates financial data from multiple sources and document types into a unified, normalized format. Handles inconsistencies in reporting standards, currencies, and data formats across different financial documents.

Analyzes sentiment and tone in financial documents, earnings call transcripts, and management commentary. Identifies positive, negative, and neutral language to gauge management outlook and market sentiment.

Identifies and extracts named entities from financial documents including company names, executives, financial institutions, and regulatory bodies. Links entities across documents to build relationship maps.

Enables side-by-side comparison of financial metrics, performance, and trends across multiple companies or time periods. Automatically identifies similarities, differences, and outliers in financial data.

Monitors financial documents and filings for regulatory compliance requirements and flags potential issues. Tracks regulatory changes and their impact on financial data and disclosures.

Analyzes financial metrics over time to identify trends, patterns, and anomalies. Tracks how specific metrics evolve across quarters or years and detects significant changes.

Reconciles financial data from multiple sources to identify discrepancies, resolve conflicts, and establish a single source of truth. Flags inconsistencies and provides audit trails for data lineage.