Description
PromptQL is an AI platform, built by the team behind Hasura, that lets teams discuss, decide, and act on their business data in shared AI threads. It connects to warehouses, databases, SaaS apps, and APIs as they exist today, introspects their schemas to build a unified data graph, and writes and runs code to answer real business questions without moving or reshaping data.
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Instead of asking people to maintain a wiki nobody opens, PromptQL captures context from everyday work. When someone corrects an assumption or explains how the business really works, that correction becomes shared context: a reusable skill, cited team knowledge, or a semantic-model change that the next session can use. Its accuracy compounds as the team works, and every task shows its sources and assumptions.
Because it is multiplayer by design, multiple people and the agent work inside the same thread, @-mentioning the right teammate without restarting the task. The agent can run on any reasoning LLM per thread or task, generate images, run code in a secure cloud sandbox, connect new tools by talking to it, and act proactively on schedules and external events.
PromptQL is designed for regulated environments, with row- and column-level permissions enforced at the data layer, single-tenant or on-premises deployment, no training on customer data, and full audit logging. It is used across financial services and by global enterprises and AI-native leaders.
- CategoryAI Data Analysis
- Pricing ModelContact sales
- DeploymentCloud, On-premises
- PlatformsWeb
PromptQL's Core Features
Shared multiplayer AI threads for teams
Connects to warehouses, databases, SaaS apps, and APIs with no data prep
Self-organizing, Wikipedia-style context graph of skills, knowledge, and semantic layer
Corrections become reusable skills and shared knowledge with citations
Any-model support, including image generation, per thread or task
Code execution in a secure cloud sandbox with tables, charts, and dashboards
Proactive scheduling and reaction to webhooks, emails, and alerts
Permissions enforced at the data layer with audit logs and on-prem or BYOC deployment
How to use PromptQL?
Connect data: Point PromptQL at your warehouses, databases, SaaS apps, and APIs.
Seed context: Import knowledge from Slack, Google Drive, or GitHub, or start from scratch.
Start a thread: Ask business questions in a shared AI thread and review the sources and assumptions.
Teach it: Correct assumptions or pull in a teammate so fixes become reusable shared context.
Automate: Turn recurring analyses into artifacts and let the agent act on schedules and events.
PromptQL's Use Cases
- Team data analysis
- Dashboards and reports
- Financial services analysts
- Shared team knowledge
- Customer-facing and agent products
