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Hebbia Tech Stack

AI platform for financial analysis and investment workflows

Software Development New York, NY 51–200 employees Founded 2020 Privately Held
hebbia.com LinkedIn

Hebbia builds generative AI agents for finance, deployed across investment banks and major asset managers. The stack reveals infrastructure-heavy engineering (Kafka, Spark, Hadoop, Airflow, Elasticsearch) optimized for high-scale document indexing and retrieval—core to surfacing insights across filings and internal data. Active hiring skews heavily senior (34 of 45 open roles) and engineering-focused (22 roles), while projects center on scaling matrix/indexing platforms and multi-agent frameworks, indicating rapid expansion of core AI capabilities rather than stabilization.

Tech Stack 35 technologies

Core StackAfter Effects Adobe Premiere Pro Figma Adobe Illustrator Snowflake AWS Python Apache Spark Hadoop Apache Airflow dbt Go C++ Rust Java Kafka Elasticsearch PostgreSQL Redis Salesforce Gong Okta Slack Dropbox Outreach Apollo Azure GCP Linear OneLogin+1 more

What Hebbia Is Building

◆Challenges

  • Replacing repetitive manual workflows
  • Manual drain of workflows
  • Manual repetitive gtm workflow
  • Lack of automation in lead enrichment
  • Performance optimization at scale
  • Complex banking workflows
  • Unreliable apis
  • High-scale document indexing
  • Underwriting risk
  • Complex enterprise evaluations

▲Active Projects

  • Scaling matrix platform
  • Deployment platform
  • O(1) universal indexing
  • Custom multi-agent framework
  • Internal tooling
  • High-scale document build system
  • Performance optimization solutions
  • Strategic growth initiatives post-sale
  • Observability framework
  • Co-creating new gtm structures & incentives

Hiring Activity

Accelerating45 roles · 45 in 30d

Department

Engineering
22
Sales
10
Design
3
Ops
3
Product
3
Data
2
HR
2

Seniority

Senior
34
Mid
5
Manager
4
Junior
2
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About Hebbia

Hebbia is a generative AI platform for financial services, enabling investment banks and asset managers to automate analyst workflows around investment research, due diligence, and deal origination. The product surfaces insights across filings, research, and internal documents with citations, and generates documents, presentations, and spreadsheets via AI agents. Built on a distributed data stack (Spark, Hadoop, Airflow, Snowflake) coupled with real-time search (Elasticsearch, Kafka), the platform handles millions of documents at institutional scale. The company is based in New York and operates across the United States and United Kingdom.

HeadquartersNew York, NY
Company Size51–200 employees
Founded2020
Hiring MarketsUnited States, United Kingdom

Frequently Asked Questions

What tech stack does Hebbia use?

Hebbia runs on AWS and Azure with Apache Spark, Hadoop, Kafka, and Elasticsearch for data processing and search. The backend uses Python, Go, C++, Rust, and Java. Data pipelines rely on Apache Airflow and dbt on Snowflake. Frontend uses Figma and Adobe tools. Sales/ops layer includes Salesforce, Outreach, Gong, and Apollo.

What is Hebbia working on?

Current projects include scaling matrix and deployment platforms, building a custom multi-agent framework, o(1) universal indexing, high-scale document systems, and performance optimization solutions. Internal focus includes observability frameworks and new go-to-market structures.

How this profile is built

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.