Compare · Hebbia
Hebbia alternative: permission-aware search across every work app
Hebbia is one of the most capable AI tools in finance. Its Matrix product points teams at hundreds or thousands of long documents, credit agreements, filings, data rooms and premium financial datasets, and returns structured, fully traceable analysis in a spreadsheet-style grid. For investment, credit and legal diligence at scale, it is genuinely strong, and it is built, priced and sold as an enterprise, finance-desk tool.
One search · cited answers · permission-aware
Ask your company anything
InSearch searches across Drive, Slack, Notion, Confluence, Gmail, Jira and Salesforce in one query and returns a written answer with citations, only ever from sources you're allowed to see.
Searching connected apps
WorkingAnswer
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Live, interactive · cited answers
Every answer cited to its source documents · permission-aware · never trains on your data
InSearch does a different job. Instead of deep analysis on one defined corpus, it is one permission-aware search across the everyday apps your whole company already works in: Drive, Slack, Notion, Confluence, Gmail, Jira and Salesforce. You ask a question in plain English and get a written answer with inline citations, scoped to exactly what each person is allowed to see, and never used to train a model. If most of your team simply needs to find the answer that is buried somewhere across your tools, that is the job InSearch is built for.
Hebbia is a deep document-analysis engine for finance and legal teams. InSearch is company-wide, permission-aware search that finds cited answers across the apps everyone already uses. They solve adjacent problems, so choose by whether you need heavyweight diligence on a corpus or fast answers across the whole workplace.
Side by side
Hebbia vs InSearch, honestly
A fair look at what each does well. Both are capable tools. Here is where they differ.
| What matters | InSearch | Hebbia |
|---|---|---|
| Primary job | One permission-aware search across every work app, for the whole company | Deep multi-document analysis for finance and legal teams |
| What it searches | Drive, Slack, Notion, Confluence, Gmail, Jira and Salesforce in one query | Uploaded and synced documents, deal data rooms and premium financial datasets such as PitchBook, FactSet, S&P Capital IQ and ICE |
| Answer format | A written answer with inline citations to the exact source | Structured, fully traceable answers laid out in a spreadsheet-style grid |
| Depth vs breadth | Breadth: fast answers across everyday knowledge, for everyone | Depth: parallel agents reasoning across hundreds of long documents (a genuine strength) |
| Permission-aware | Item-level source permissions enforced at query time, per person | Enterprise access controls, built for defined project teams rather than org-wide self-serve |
| Pricing | Transparent per seat, published on the site | No published pricing; enterprise-only, custom, contact sales |
| Best suited for | Any team that needs to find answers across its tools | Investment, credit, private equity and legal teams doing heavy document diligence |
Comparison reflects general, publicly understood positioning. Capabilities change, so check each product for the latest.
Why teams pick InSearch
One cited, permission-aware search across every app
Built for the whole company
Every employee can find cited answers across the apps they already use, not just a finance or legal desk running diligence on a document set.
Permission-aware by design
Item-level access controls inherited from each source and enforced at query time, so a company-wide rollout stays safe.
Never trains on your data
Your content answers your questions and is never used to train models, encrypted in transit and at rest under SOC 2 practices.
Good questions
Hebbia vs InSearch, answered
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Find any answer at work with InSearch
One search across all your company apps, a clear cited answer, scoped to exactly what each person can see, and never trained on your data.
One search across every app · cited answers · permission-aware · never trains on your data