AlphaSense vs Hebbia (2026)
Choose AlphaSense when the answer lives in outside content a firm does not own, and Hebbia when it lives in the deal room. AlphaSense licenses a large estate of broker and equity research, filings, event transcripts, hundreds of thousands of expert call transcripts and trade journals. It adds a firm's own research under the permissions it already carries, and layers on cited earnings call summaries, document questions, a grid across many documents and workflow agents. Hebbia works on the documents a deal team brings, from data room files and SharePoint to SEC filings and the firm's own FactSet, Capital IQ and PitchBook feeds. Its Matrix lays documents out as rows and questions as columns. Agents read every page in full rather than retrieved excerpts, citing the exact page, paragraph and sentence behind each cell for an analyst to check or overwrite. Both keep customer data out of model training. Hebbia also holds ISO 42001 for AI management and processes data in the United States or the European Union in a dedicated tenant, while AlphaSense is still working toward ISO 42001.
- Research starts outside the firm. AlphaSense's licensed estate covers broker research, filings, event transcripts, expert calls and trade journals in one search.
- Internal and external content should sit side by side. A firm's own research is ingested under the permissions already attached to it.
- Keys or data must stay with the firm. Bring your own key and bring your own storage keep encryption keys and data under the firm's control.
- Scale and staying power count. More than 6,500 organizations use AlphaSense, including 88 percent of the S&P 100, and it has passed $500 million in annual recurring revenue.
- The work is reading a deal room. Matrix asks the same questions of hundreds of documents at once, with documents as rows and questions as columns.
- Every answer must trace to a sentence. Each cell cites the exact page, paragraph and sentence, and analysts can flag or overwrite it.
- AI governance needs a certificate. Hebbia lists ISO 42001 alongside SOC 2 Type 2 and ISO 27001.
- Data must stay in the United States or the EU. Storage and inference can run in either region, in a dedicated tenant.
This comparison is published by AI FinTech Index, an independent research platform that publishes independent ratings of AI vendors for financial services. AlphaSense and Hebbia are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI FinTech Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| AlphaSense | Hebbia | |
|---|---|---|
| Primary category | Capital Markets & Research AI | Capital Markets & Research AI |
| Founded | 2011 | Not published |
| Headquarters | New York, New York, United States | Not published |
| Website | www.alpha-sense.com | www.hebbia.com |
Side by Side
Select any grade to read the note behind it.
| Axis | A AlphaSense |
H Hebbia |
|---|---|---|
| AI Centrality | ||
| Autonomy and Oversight Model | ||
| Model Risk Management and Transparency | ||
| Operational and Outcome Evidence | ||
| AI Safety and Data Stewardship | ||
| GLBA and Data Privacy Posture | ||
| Security Certifications and Trust Center | ||
| Regulatory Status and Licensure | ||
| AI Governance and Bias Disclosure | ||
| AI Liability and Recourse | ||
| Model Supply Chain Disclosure | ||
| Core Systems and Integration Depth | ||
| Deployment Model and Data Residency | ||
| Commercial Transparency | ||
| Institution and Segment Coverage |
The short version of each
AlphaSense
AlphaSense is a market intelligence and search platform for banks, hedge funds, private equity firms, asset managers and corporate strategy teams. Its licensed estate spans broker and equity research, filings, event transcripts, trade journals and hundreds of thousands of expert call transcripts, much of it from the $930 million Tegus deal in 2024. Generative search, cited earnings call summaries, a grid that asks one question of many documents and workflow agents run across that estate and the firm's own research, which keeps the permissions it already carries. Customer data never trains the models, model providers keep nothing, and a firm can hold its own keys or storage. SOC 2 Type II and ISO 27001:2022 are in place, with ISO 42001 still in progress. More than 6,500 organizations use it, including 88 percent of the S&P 100, and it has passed $500 million in annual recurring revenue.
Source: AI FinTech Index, 2026
Hebbia
Hebbia analyzes large sets of private documents for asset managers, private equity firms, investment banks, credit investors and law firms. Its Matrix lays documents out as rows and questions as columns, and agents read every page in full to fill each cell, citing the exact page, paragraph and sentence for an analyst to check or overwrite. The Max assistant works across a deal team's files the same way. Data comes from what the firm connects, including SEC filings, FactSet, S&P Capital IQ, PitchBook, SharePoint, Box and Third Bridge. Documents, prompts and outputs never train any model, each customer sits in a siloed environment, and storage and inference can run in the United States or the European Union in a dedicated tenant. SOC 2 Type 2, ISO 27001:2022 and ISO 42001 are in place. Morgan Stanley, Centerview Partners and MetLife are customers, and pricing follows a demo.
Source: AI FinTech Index, 2026
Common questions
Is Hebbia an alternative to AlphaSense?
Partly. Hebbia is built for analyzing a firm's own and acquired documents in bulk, such as a data room or a credit agreement set, by the AI FinTech Index's reading. AlphaSense is built for searching licensed outside research, transcripts and expert calls alongside internal content. Both offer a grid that asks one question of many documents, and many investment teams would use each for different stages of a deal.
Where does Hebbia's data come from compared with AlphaSense?
AlphaSense licenses its own content estate, from broker research to expert call transcripts. Hebbia reads what the customer connects, including SEC filings, FactSet, S&P Capital IQ, PitchBook, SharePoint, Box and Third Bridge, and does not write results back into those systems. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified October 17, 2026. No vendor pays for placement.
Do AlphaSense and Hebbia train AI on client documents?
No, for both. AlphaSense keeps customer data out of model training and holds its model providers to zero data retention. Hebbia keeps documents, prompts and outputs out of training for itself and its model providers, and holds each customer's data in a siloed environment. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified October 17, 2026. No vendor pays for placement.
Which is more certified, AlphaSense or Hebbia?
Both hold SOC 2 Type 2 and ISO 27001:2022. Hebbia adds ISO 42001 for AI management, while AlphaSense is still working toward ISO 42001. Graded by AI FinTech Index against the same capability axes from each vendor's own published materials, verified October 17, 2026. No vendor pays for placement.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Wealth & Advisory AI page.
Both sell by quote, neither backs its accuracy with a stated test method, and neither accepts the cost when a wrong extraction reaches an investment memo. OpenAI's customer story on Hebbia names o1, o3 mini and GPT 4o and reports 92 percent accuracy against 68 percent for standard retrieval, a figure from the model supplier rather than an independent test. AlphaSense is independent, valued at $4 billion in its latest round.