Intelligent Extraction

AI-powered extraction of critical data from complex financial documents, transforming unstructured information into structured, actionable insights.

Extract What Matters Most

Financial documents contain vast amounts of unstructured data buried within thousands of pages. Our intelligent extraction technology uses advanced natural language processing and machine learning to automatically identify, extract, and structure the most critical information from SEC filings, contracts, and regulatory documents.

Traditional manual extraction is time-consuming, error-prone, and doesn't scale. Our AI-powered approach processes documents in seconds, maintaining accuracy while dramatically reducing research time.

Filing Sections & Exhibits – Automatically identify and extract specific sections like Risk Factors, MD&A, Financial Statements, and all material exhibits from 10-K, 10-Q, 8-K, and proxy filings.
Agreement Clauses – Parse complex legal agreements to extract key terms, conditions, payment structures, termination clauses, and material obligations from merger agreements, credit facilities, and commercial contracts.
Wealth & Compensation Data – Extract executive compensation details, stock ownership, insider holdings, beneficial ownership tables, and wealth concentration metrics from proxy statements and Form 4 filings.
Financial Metrics – Pull revenue segments, geographic breakdowns, subsidiary information, debt covenants, and KPIs directly from financial statements and footnotes.
Risk Disclosures – Identify and categorize risk factors, legal proceedings, regulatory investigations, and material uncertainties across all filings.

99.2%

Extraction Accuracy

50x

Faster Than Manual

100+

Data Points Extracted

Smart Summarization

Transform lengthy documents into concise, actionable summaries that capture the essential insights without missing critical details.

From Pages to Paragraphs

The average 10-K filing exceeds 200 pages of dense legal and financial text. Reading every filing for every company you track is simply not feasible. Our smart summarization engine uses large language models trained specifically on financial documents to generate concise, accurate summaries that preserve the meaning and context of the original text.

Unlike generic summarization tools, our AI understands financial terminology, regulatory requirements, and the structure of SEC filings, ensuring that summaries maintain accuracy while reducing reading time by up to 90%.

SEC Filing Summaries – Generate executive summaries of 10-K and 10-Q filings that highlight business changes, financial performance, key risks, and material events in a fraction of the time.
Contract Highlights – Distill merger agreements, credit agreements, and material contracts into bullet-point summaries covering parties involved, transaction value, key terms, conditions precedent, and material obligations.
ADV Form Insights – Summarize Form ADV filings from investment advisers, extracting AUM, fee structures, disciplinary history, conflicts of interest, and investment strategies.
Earnings Call Summaries – Condense hour-long earnings calls into digestible summaries highlighting management commentary, guidance updates, and analyst concerns.
Comparative Analysis – Generate side-by-side summaries comparing multiple filings, contracts, or companies to identify differences and trends.
Custom Depth Control – Adjust summary length and detail level based on your needs, from tweet-length overviews to detailed section-by-section summaries.

90%

Reading Time Reduction

10M+

Documents Summarized

15 sec

Average Summary Time

Vector Search

Lightning-fast semantic search across millions of documents using cutting-edge vector embeddings and neural search technology.

Search by Meaning, Not Just Keywords

Traditional keyword search falls short when researching financial documents. Searching for "acquisition" won't find documents discussing "strategic transactions," "business combinations," or "takeover bids" unless you manually search for every synonym. Vector search solves this by understanding the semantic meaning of your query and finding conceptually similar content, even when exact keywords don't match.

Our vector search engine transforms every document into high-dimensional mathematical representations (embeddings) that capture semantic meaning. When you search, we convert your query into the same vector space and find documents based on conceptual similarity, not just word matching.

BETA

Early Access Program

Vector Search is currently in beta and not yet publicly available. Contact us to get early access and be among the first to experience semantic search across financial documents.

<100ms

Search Response Time

50M+

Documents Indexed

Better Recall vs Keyword

How It Works

The technology powering our AI-driven research platform

Large Language Models

We leverage state-of-the-art transformer-based language models fine-tuned specifically on financial and regulatory documents. Our models understand the nuanced language of SEC filings, contracts, and financial disclosures.

Custom-trained on 20+ years of SEC filings
Financial domain-specific vocabulary and context
Continuous learning from new filings
Multi-task architecture for extraction and summarization

Named Entity Recognition

Our NER models identify and classify key entities within documents including companies, people, locations, dates, financial figures, and legal terms with industry-leading accuracy.

Recognition of 50+ entity types
Relationship extraction between entities
Co-reference resolution across documents
Temporal reasoning for event sequencing

Vector Embeddings

Documents and queries are transformed into dense vector representations in high-dimensional space, where semantic similarity is measured by mathematical distance between vectors.

1024-dimensional embedding space
Cosine similarity for relevance ranking
Approximate nearest neighbor search (ANN)
Real-time index updates for new documents

High-Performance Infrastructure

Our platform is built on distributed computing infrastructure designed for speed and scale, processing millions of documents while maintaining sub-second response times.

GPU-accelerated inference for LLM tasks
Vector database optimized for similarity search
Horizontal scaling for concurrent requests
99.9% uptime SLA with global CDN

Real-World Applications

How AI-powered research transforms workflows across industries

Investment Research

Analysts use vector search to identify comparable companies, extract financial metrics at scale, and monitor portfolio companies for material changes flagged by smart summarization.

Legal Due Diligence

Law firms leverage intelligent extraction to pull key contract terms from merger agreements, identify litigation risks across filings, and compare disclosure language.

M&A Advisory

Advisors track deal activity, extract valuation multiples from precedent transactions, and generate summaries of comparable acquisitions for client presentations.

Credit Analysis

Credit analysts extract debt covenants, financial ratios, and risk factors to assess creditworthiness and monitor compliance across borrower portfolios.

Financial Journalism

Reporters use smart summarization to quickly understand breaking filings, identify newsworthy disclosures, and find similar stories across company histories.

Academic Research

Researchers query millions of filings to test hypotheses, extract large-scale datasets for empirical studies, and identify disclosure patterns over time.

Download Our Vector Search Whitepaper

Learn how vector embeddings and semantic search are transforming financial research. Our comprehensive whitepaper covers the technical foundations of vector search, implementation best practices, and real-world performance benchmarks.

Topics covered: Neural embedding models • Approximate nearest neighbor algorithms • Hybrid search architectures • Production deployment strategies • Performance optimization techniques

Download Whitepaper Request Demo

Transform Data Into Actionable Intelligence