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What does Hebbia AI do?
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This blog provides insights for professionals who struggle with extracting information from vast amounts of unstructured data in fields like finance, law, and pharmaceuticals. It explains how Hebbia, an AI platform, utilizes generative AI to help knowledge workers quickly find accurate answers across multiple documents.
Finance, legal, and pharma teams spend hours searching through PDFs, SEC filings, and research to find one clause or answer.
Speed matters, but most tools slow them down.
This blog is for professionals who work with long, unstructured documents and need a faster way to get clear answers. You’ll learn how Hebbia's generative AI for searching large documents helps users find what they need, across files, with greater accuracy.
If time and clarity matter, keep reading.
Hebbia is an AI-powered research platform that handles dense, high-value documents like SEC filings, expert call transcripts, and broker research. George Sivulka, a business-focused co-founder with a background in electrical engineering and neural computation at Stanford, leads the platform.
Hebbia enables users to ingest, read, and query documents of unlimited length using large language models (LLMs). Its ability to analyze vast amounts of data makes it particularly useful for:
Investment banks
Law firms
Pharmaceutical companies
Hedge funds
Asset managers
These users no longer have to open each file manually or maintain complex Boolean search rules. Instead, they can rely on Hebbia’s matrix platform to ask questions in natural language and get a final answer referencing the original documents.
Hebbia's core is an AI platform that combines large language models with a proprietary search engine that works on both structured and unstructured data.
Here’s how the process unfolds:
Users upload multiple documents in formats like PDFs, DOCX, or HTML. These may include:
SEC filings
Due diligence reports
Expert call transcripts
Company websites
Broker research
Other documents
The system parses these using Hebbia’s document understanding engine.
Hebbia constructs a semantic map of the content using embeddings from language models.
Users pose complex questions like:
“What are the key risks disclosed in Q2 earnings calls?”
“How does Company X discuss pricing strategy across filings?”
The platform enables contextual answers directly sourced from key documents.
Results show relevant information with citations so users can verify responses.
In investment banks and hedge funds, speed and accuracy in due diligence are everything. Hebbia handles large datasets, identifies red flags, and extracts clauses from 10,000+ page disclosures, without delay.
Example: A team evaluating a merger can query prior litigation history or pricing agreements directly from regulatory filings, all within one search window.
Legal researchers often work through endless documents to answer specific questions buried in contracts. With Hebbia, a paralegal can find precedent clauses across thousands of cases with a single query.
Use case: Search for “change of control” clauses across acquisition agreements to prepare for a deal review.
Asset managers and financial institutions use Hebbia to assess market trends, analyze asset pricing, and interpret broker research at scale. The ability to conduct sentiment analysis on earnings transcripts provides a competitive edge.
Use case: Automatically flag shifts in tone across quarterly calls to adjust portfolio positioning.
Hebbia has raised a million in Series B rounds led by Andreessen Horowitz, with participation from Index Ventures, Peter Thiel, and Google Ventures. The company continues to attract top-tier interest due to its performance in verticals like:
| Sector | Use Case Example |
|---|---|
| Law Firms | Contract review, precedent clause identification |
| Pharmaceutical Companies | IP litigation, trial protocol analysis |
| Investment Banks | M&A diligence, Q&A on SEC filings |
| Hedge Funds | Earnings call sentiment, deal term extraction |
| Asset Managers | Trend analysis, multi-document reviews |
The business-focused co-continues to scale the full suite of features, from natural language querying to summarization tools, designed for heavy-duty knowledge work.
| Traditional Search | Hebbia Search |
|---|---|
| Keyword-based | Semantic + contextual understanding |
| Document-by-document review | Ingest multiple files simultaneously |
| No understanding of tone | Integrated sentiment analysis |
| Lacks summarization | Native summarization tool |
Most search tools cannot analyze vast amounts of text across multiple documents and generate answers with verified context. That’s where Hebbia leads.
Hebbia is a direct application of generative AI for high-value, domain-specific tasks. Unlike chatbots or assistants, Hebbia is trained for information retrieval across dense, technical content. It bridges the gap between language models and business-focused needs.
Ask open-ended, complex questions
Work across multiple industries
Speed up due diligence
Gain deep insights from previously unsearchable content
The platform enables faster synthesis of relevant information, making Hebbia a strong alternative to generic AI tools.
Hebbia's generative AI for searching large documents helps professionals handle heavy research with speed and precision. It reduces time spent combing through files and brings the right data forward.
This tool fits naturally into asset pricing, legal reviews, and diligence. Users get direct answers faster, helping them move forward with clarity and confidence.
