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Promethium

Category: AI in Analytics & BI

An AI-powered data fabric platform that enables users and AI agents to access, query, and analyze enterprise data using natural language, without requiring data movement. It automates data discovery, preparation, and governance to deliver data products in real-time. Promethium was founded in 2018. The company is led by Prat Moghe. Based in Menlo Park, California, USA. Team size: 25-50. Total funding raised: $34.5M. Latest round: Series A, $26.0M, 2022-02, led by Insight Partners. Key investors include Insight Partners, .406 Ventures, Zetta Venture Partners.

Founded
2018
Headquarters
Menlo Park, California, USA
Team size
25-50
Total funding
$34.5M

Value proposition

Empowers both technical and non-technical users to query complex and disparate datasets using natural language. It accelerates time-to-insight from months to minutes by eliminating dependencies on manual IT and data engineering processes.

Products and solutions

Agentic Data Fabric Platform, Mantra (AI Agent Chatbot for Data), Automated Data Discovery and Profiling, Natural Language to SQL Engine, No-Code Federated Query Pipeline Builder

Unique value

Its 'Instant Data Fabric' provides self-service data access without needing to move, copy, or centralize data. It combines a natural language interface with patented automated data preparation and profiling technology, enabling users to get answers from data in minutes rather than months.

Target customer

Mid-market to large enterprises, specifically targeting data and analytics teams as well as business units seeking to democratize data access and accelerate AI/ML initiatives.

Industries served

Healthcare, Retail, Financial Services, Government, Utilities

Technology advantage

Patented data profiling technology and the use of AI agents (Mantra) to provide direct, query-based data access. This simplifies and accelerates data analysis for non-technical business users, creating a significant time-to-value advantage.

How they differentiate

Promethium's primary differentiation is its 'no-data-movement' data fabric that uses natural language queries and AI agents. This approach aims to eliminate the complexity and delays of traditional data warehousing and ETL processes, making data access truly self-service for non-technical users.

Main competitors

Starburst, Dremio, Denodo, Alteryx, Koverse

Key partnerships

Snowflake

Notable customers

Includes a major US-based utility company; other customer names are not publicly disclosed.

Major milestones

Recognized by Gartner as a Cool Vendor and an early pioneer in the data fabric space., Launched 'Mantra', an agentic chatbot for natural language data queries., Appointed former Hortonworks executive Prat Moghe as CEO to lead the next phase of growth., Secured a U.S. patent for its innovative data profiling technology., Announced a strategic partnership with Snowflake.

Growth metrics

Not publicly disclosed, but the company has secured significant Series A funding and is actively expanding its team and partnerships.

Market positioning

A recognized pioneer in the AI-powered data fabric market, focused on self-service analytics for enterprises that are increasingly adopting AI and looking to democratize data access.

Geographic focus

North America, with expanding global operations.

Patents and IP

Holds a U.S. patent for its proprietary automated data profiling and context determination technology.

About Prat Moghe

Prat Moghe is a seasoned technology executive with 25 years of experience in data and analytics. He is a successful founder and entrepreneur who has scaled multiple startups and public companies. Prior to Promethium, he held executive roles including EVP at Cloudera, Founder & CEO at Cazena, and SVP at IBM Netezza. He is known for launching and scaling category-creating products globally, with expertise spanning data analytics, AI/ML, security, and networking. He is also an advisor and mentor to entrepreneurs and startups. He has served as President of TiE Boston and a Trustee of the TiE Board of Trustees.

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