Investment research has always had a volume problem: the answer to a question is often buried somewhere in a hundred-page filing, a two-hour earnings call transcript, or a database of private company data most people can't search efficiently. AI hasn't changed what analysts need to know — it's changed how fast they can find it, turning a multi-hour document search into a query that takes seconds.
The five tools below all speed up some part of this research process, but they specialize in different data sources and different kinds of research.
In this article
- AlphaSense: search across filings, transcripts, and expert calls in seconds
- PitchBook: the standard for private company and venture capital data
- Daloopa: extracting clean, structured financial data from messy filings
- Rogo: an AI research assistant built specifically for investment banking and finance workflows
- Hebbia: flexible, spreadsheet-style AI research across any document set
- Quick Comparison
- Frequently Asked Questions
- Are these tools meant for individual retail investors?
- How do I know an AI-surfaced answer is actually accurate?
- What's the difference between AlphaSense and Hebbia if they both search documents?
- Do I need both a public-markets tool and a private-markets tool?
- Final Verdict
AlphaSense: search across filings, transcripts, and expert calls in seconds
AlphaSense indexes SEC filings, earnings call transcripts, broker research, and expert call transcripts, letting analysts search across all of it with natural-language queries and get back the specific paragraph or data point that answers their question, with the source cited. Its AI also summarizes long documents and can flag sentiment shifts across a company's disclosures over time.
AlphaSense's value isn't reading documents for you — it's making sure you never have to read the 90% of a filing that isn't relevant to your question.
Where it fits best: equity research analysts, corporate development teams, and institutional investors who need to search across large volumes of public disclosures and expert commentary quickly. It's widely used enough in professional research that it's become something of a category standard.
Where it falls short: it's priced and built for institutional use — the cost and feature depth are overkill for an individual retail investor doing occasional research.
PitchBook: the standard for private company and venture capital data
PitchBook specializes in data that AlphaSense doesn't cover well: private company financials, venture capital and private equity deal data, cap tables, and fund performance. Its AI features help summarize company profiles and surface comparable deals or companies based on a natural-language description rather than manual filtering.
Where it fits best: venture capital, private equity, and corporate development professionals researching private markets — a different research problem than public equity analysis, since there's no SEC filing to search through.
Where it falls short: its coverage of public company financial detail is much thinner than AlphaSense's — it's not a substitute for public markets research, just a complement for private market questions.
Daloopa: extracting clean, structured financial data from messy filings
Daloopa focuses on a specific, tedious problem: pulling structured financial data (line items from income statements, balance sheets, KPIs disclosed in filings or presentations) out of unstructured documents and into a clean, model-ready format, verified against the source. This is the step that used to mean manually copying numbers into a spreadsheet.
Where it fits best: analysts building detailed financial models who need accurate historical data extracted quickly, rather than analysts primarily doing qualitative research or document search.
Where it falls short: it's narrower in scope than AlphaSense or Hebbia — it's excellent at structured data extraction specifically, not a general research or document-search tool.
Rogo: an AI research assistant built specifically for investment banking and finance workflows
Rogo is built around the specific workflows of investment banking and finance teams — company research, comparable company analysis, and drafting research summaries — with a focus on the security and compliance requirements that come with handling sensitive deal information. It connects to internal data sources as well as public filings.
Where it fits best: investment banks and finance teams that need AI research assistance but have strict data-security requirements around confidential deal information — Rogo is built with that constraint in mind rather than as an afterthought.
Where it falls short: its narrower, workflow-specific focus means it's less of a general-purpose research search tool than AlphaSense — it's built around specific banking tasks rather than open-ended research.
Hebbia: flexible, spreadsheet-style AI research across any document set
Hebbia lets analysts build a matrix of questions against a large set of documents — filings, contracts, presentations — and get back a spreadsheet-style grid of answers, each one sourced and citable, rather than a single chat response. This makes it well suited to research questions that need the same set of questions answered across many companies or documents at once.
Where it fits best: analysts running the same research questions across many companies or a large document set at once — due diligence on multiple deals, or a sector-wide comparison, where a chat interface would mean repeating the same question dozens of times.
Where it falls short: its spreadsheet-style, matrix-based interface has a steeper learning curve than a straightforward chat search tool — it's built for repeated, structured research rather than a single one-off question.
Quick Comparison
| Tool | Best for | Public or private markets? | Interface style |
|---|---|---|---|
| AlphaSense | Filings, transcripts, expert calls | Public | Search/chat |
| PitchBook | VC/PE deal and company data | Private | Database/profiles |
| Daloopa | Structured financial data extraction | Public | Data extraction |
| Rogo | Investment banking workflows | Public + internal | Assistant/chat |
| Hebbia | Research across many documents at once | Both | Spreadsheet/matrix |
Frequently Asked Questions
Are these tools meant for individual retail investors?
Not primarily — all five are priced and designed for institutional use: analysts, investment banks, VC and PE firms, and corporate development teams. An individual investor researching a stock to buy would find these tools expensive and more powerful than needed for that scale of research.
How do I know an AI-surfaced answer is actually accurate?
All five tools cite the specific source document and location for any answer they surface, which lets an analyst verify the underlying data rather than trusting a summary blindly. This citation-first approach is standard across the category precisely because getting a number wrong in financial research has real consequences.
What's the difference between AlphaSense and Hebbia if they both search documents?
AlphaSense is built around search-and-summarize for a single question or a smaller set of documents. Hebbia is built for running the same set of questions across a large number of documents simultaneously, returning a grid of answers — a different shape of research task, even though both involve document search.
Do I need both a public-markets tool and a private-markets tool?
It depends entirely on what you research. A public equity analyst has little use for PitchBook's private company data, while a VC associate has little use for AlphaSense's SEC filing search. Many institutional research teams do end up using both, since public and private market research rarely overlap much.
Final Verdict
The right tool here depends almost entirely on what you're researching, not on general quality differences between them. Public company research through filings and calls points to AlphaSense. Private company and deal research points to PitchBook. Need clean historical financial data pulled into a model — Daloopa. Working inside an investment bank with compliance constraints — Rogo. Running the same research questions across many companies at once — Hebbia. See the rest of the finance stack in the full Business & Finance category.