Generative AI agents for investment research and financial analyst workflows
Hebbia builds AI agents that let investment banks and asset managers search filings, research and internal documents and generate citation-backed analysis. Its stack pairs Go, Rust and C++ with Kafka, Elasticsearch and Spark across AWS, Azure and GCP, a systems-level, multi-cloud profile matching projects like a custom multi-agent framework and universal indexing. Hiring is accelerating and skews heavily toward senior engineers, with cloud cost control a recurring pressure.
Hebbia is a New York-based generative AI company founded in 2020 with 51–200 employees. It sells to financial institutions, including investment banks and large asset managers, automating analyst work such as research, screening, origination and diligence. The product surfaces insights across filings, research and large volumes of internal documents, and generates documents, presentations and spreadsheets with citations. The company is privately held and hires in the United States and United Kingdom.
Hebbia uses Python, Go, Rust, C++ and Java with Kafka, Elasticsearch, PostgreSQL, Redis, Spark, Airflow, dbt and Snowflake, running across AWS, Azure and GCP. Sales tools include Salesforce, Outreach, Gong and Apollo.
Yes. Engineering is Hebbia's largest hiring area, with demand concentrated at senior level, alongside smaller needs in sales, operations, HR and product across the United States and United Kingdom.
Job posts point to a custom multi-agent framework, universal document indexing, a high-scale document build system, scaling the Matrix platform team and AI-powered workflow development.
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Hebbia's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.