The leading AI platform for finance, used by the world's leading asset managers, investment banks, law firms and Fortune 500 companies.
Hebbia is perceived well for its ability to facilitate AI research workflows, mainly through features that search, summarize, generate, and cite sources. However, users express concerns about the fragmented experience of using multiple tools within the workflow, indicating a need for more seamless integration. The sentiment around pricing isn't explicitly mentioned in the observations. Overall, Hebbia seems to have a positive reputation but could benefit from improvements in workflow cohesiveness.
Mentions (30d)
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Hebbia is perceived well for its ability to facilitate AI research workflows, mainly through features that search, summarize, generate, and cite sources. However, users express concerns about the fragmented experience of using multiple tools within the workflow, indicating a need for more seamless integration. The sentiment around pricing isn't explicitly mentioned in the observations. Overall, Hebbia seems to have a positive reputation but could benefit from improvements in workflow cohesiveness.
Features
Use Cases
Industry
information technology & services
Employees
140
Funding Stage
Series B
Total Funding
$159.7M
What would an ideal “research workflow” look like if you could design it from scratch?
I’m in this weird in-between moment with AI research workflows. There’s tools that can search/summarise/generate/cite sources, but the workflow still feels fragmented at best. I have to jump between tools, double & triple check outputs, and manually stitch things together, plus keeping a mental note of what can/can’t be trusted. Obviosly things are “evolving”, and i’ve been thinking about what my dream setup would look like, beyond “LLM but better”. Like the FULL workflow including inputs, retrieval, context handling and memory across research threads. Where would you tolerate latency vs accuracy, what do the outputs need to include to be usable, how do you increase trust at output level? FOr me the biggest gap is still around source-aware AI search so I’d like to see proper citations, more like document retrieval with sources so that you can trace a claim back without second-guessing. More structured retrieval. I’ve seen some movement towards the latter instead of just chunk-based RAG over unstructured text using Baselight/Elicit + Hebbia as well as ChatGPT and i think this is where i’d start. Definitely want some fact check automation and being able to quickly verify statistics with sources submitted by /u/CodNo2235 [link] [comments]
View originalHebbia uses a tiered pricing model. Visit their website for current pricing details.
Key features include: Any Task, Any Data, Total Transparency, Enterprise Security, Integrations., Natively multi-modal., Forever improving., Trustworthy by design..
Hebbia is commonly used for: Financial analysis and forecasting, Market research and trend analysis, Risk assessment and management, Investment strategy development, Regulatory compliance monitoring, Customer insights and sentiment analysis.
Hebbia integrates with: Salesforce, Tableau, Microsoft Power BI, Slack, Zapier, Google Sheets, AWS, Azure.
