PromptQL Company Profile

Background

Overview

PromptQL is an AI platform developed by the team behind Hasura, the creators of the Hasura GraphQL Engine. Established to address the challenges of integrating AI with enterprise data, PromptQL focuses on delivering reliable, natural language-based analysis and automation on internal business data. The platform is designed to understand an organization's unique context, including tacit knowledge, business rules, and internal lexicon, to create a self-improving knowledge layer.

Mission and Vision

PromptQL's mission is to build the de facto data access layer for AI, enabling enterprises to leverage AI with accuracy and trust. The vision is to provide AI systems that act with the precision and reliability of a trusted expert across decision-making and automation use cases.

Industry Significance

In the rapidly evolving AI landscape, PromptQL addresses a critical gap by ensuring that AI systems can accurately access and reason over business data. This capability is essential for enterprises seeking to deploy AI solutions that are both effective and trustworthy, thereby enhancing operational efficiency and decision-making processes.

Key Strategic Focus

Core Objectives

Enterprise-Grade Reliability: Develop AI systems that meet the stringent reliability standards required by large organizations.

Natural Language Processing: Enable natural language analysis and automation on internal business data.

Continuous Learning: Create AI agents that continuously learn and improve, adapting to evolving business contexts.

Areas of Specialization

Data Access Layer for AI: Building a robust data access layer that facilitates seamless AI integration with enterprise data systems.

AI Automation: Automating complex decision-making processes using AI, reducing manual intervention and increasing efficiency.

Key Technologies Utilized

Large Language Models (LLMs): Employing advanced LLMs to process and understand complex data inputs.

Domain-Specific Languages: Utilizing specialized languages to plan and execute AI tasks with high accuracy.

Distributed Query Engines: Implementing query engines that can access data across systems without requiring centralization.

Primary Markets Targeted

Enterprise Organizations: Focusing on large-scale enterprises across various industries seeking reliable AI solutions.

Data-Intensive Sectors: Targeting sectors such as telecom, healthcare, finance, retail, and anti-money laundering, where data complexity is high.

Financials and Funding

Funding History

As of December 2025, PromptQL has raised a total of $152 million in funding. The most recent funding round was a Series D, completed in October 2025, raising $50 million. Notable investors include Lightspeed Ventures, Greenoaks, Vertex Ventures, and Nexus Venture Partners.

Utilization of Capital

The capital raised is intended to:

Product Development: Enhance and expand the capabilities of the PromptQL platform.

Market Expansion: Increase market penetration and establish a stronger presence in target industries.

Research and Development: Invest in R&D to drive innovation and maintain a competitive edge.

Pipeline Development

Key Pipeline Candidates

AI Analyst Deployment: Deploying AI analysts capable of handling natural language Q&A, analysis, and deep research across proprietary data.

GenAI Assessment Test (GAT): Offering a vendor-agnostic assessment to identify high-impact AI use cases and create realistic roadmaps.

Stages of Development

AI Analyst Deployment: Currently in the rollout phase, with initial deployments expected within 30 days of engagement.

GAT: Conducted over a 5-day period to map AI workloads and define evaluation metrics.

Target Conditions

Operational Efficiency: Improving decision-making processes and reducing manual reporting burdens.

Data Complexity Management: Addressing challenges associated with messy, siloed data in enterprise environments.

Anticipated Milestones

30-Day AI Analyst Deployment: Achieving full deployment and operationalization within 30 days.

GAT Completion: Providing a comprehensive assessment and roadmap within 5 days.

Technological Platform and Innovation

Proprietary Technologies

Agentic Semantic Layer: Captures evolving business context, including tacit knowledge and business rules.

Domain-Specific Language: Separates query planning from execution to avoid AI hallucinations.

Distributed Query Engine: Accesses data across systems without requiring centralization.

Significant Scientific Methods

Intent-Driven Routing: Optimizes different types of questions, achieving faster response times for simple queries while maintaining higher accuracy for complex analyses.

Continuous Learning Framework: Ensures AI systems adapt and improve over time, maintaining relevance and accuracy.

Leadership Team

Executive Profiles

Tanmai Gopal: Co-founder and CEO. Previously co-founder and CEO of Hasura, leading the company to achieve unicorn status.

Rajoshi Ghosh: Co-founder and CTO. Formerly CTO at Hasura, with a background in engineering and product development.

Mike Walsh: Vice President of Sales. Experienced in enterprise sales and business development, with a focus on AI solutions.

Key Contributions

Tanmai Gopal: Led the strategic direction and growth of Hasura, transitioning to focus on AI integration with PromptQL.

Rajoshi Ghosh: Oversaw product development and engineering teams, ensuring technical excellence and innovation.

Mike Walsh: Joined PromptQL as Vice President of Sales in August 2025, bringing extensive experience in enterprise sales and AI solutions. He has driven sales strategies and partnerships, expanding PromptQL's reach in the enterprise market.

Market Insights and Competitor Analysis

The enterprise AI market is experiencing rapid growth, with organizations increasingly seeking reliable AI solutions to enhance operational efficiency and decision-making. However, a significant challenge remains, as 95% of enterprise AI pilots fail to deliver measurable ROI.

Competitors

Apollo GraphQL: Founded in 2016, Apollo GraphQL offers a platform for building and managing GraphQL APIs, focusing on data integration and management.

Hygraph: Established in 2017, Hygraph provides a headless content management system, enabling flexible content delivery across various platforms.

Apigee: A subsidiary of Google, Apigee offers an API management platform, focusing on API analytics, security, and scalability.

Strategic Collaborations and Partnerships

In June 2025, PromptQL partnered with the University of California, Berkeley, to develop a comprehensive data agent benchmark aimed at enhancing the reliability of enterprise AI agents.

PromptQL differentiates itself by emphasizing enterprise-grade reliability and continuous learning, directly addressing the frequent issue of AI systems producing confident yet incorrect outputs. This strategic focus positions PromptQL as a leader in trustworthy AI solutions designed for complex business environments.

Strategic Opportunities and Future Directions

PromptQL is positioned to leverage the growing demand for reliable AI solutions in the enterprise sector. Its collaboration with UC Berkeley strengthens its research capabilities and market credibility. Expansion into AI consulting services introduces additional revenue streams and broader market access. Sustained investment in product innovation and strategic partnerships will be critical to maintaining growth and fostering continued innovation.