M
About
PromptQL, built by Hasura, is a multiplayer AI platform designed for data-first teams who need more than a chatbot. Think of it as an AI-native Slack: team members interact in shared threads, and PromptQL acts as a trusted teammate that knows who to ask, what context exists, and how to get things done across people and connectors — all in one place. At its core, PromptQL features three pillars. First, Multiplayer AI: when no single person has the full picture, PromptQL identifies the right stakeholders, surfaces relevant context, and orchestrates multi-step workflows across the team. Second, a living Wiki: as conversations happen, PromptQL learns and captures key facts, decisions, and procedures into a shared knowledge base — keeping it accurate even in fast-moving environments. Third, Connectors & Integrations: PromptQL connects natively to databases, HTTP APIs, and incoming webhooks, querying data in place without moving or indexing it. PromptQL is particularly powerful for operations, engineering, and data teams in finance, healthcare, and retail. It enables teams to automate complex cross-functional workflows (like vendor onboarding or DNS configuration) by combining human judgment with AI execution. With citations, edit history, and approval flows, it ensures accountability while accelerating work. Currently available free during preview.
Key Features
- Multiplayer AI Threads: Team members collaborate in shared threads where PromptQL identifies the right people to involve, pulls relevant context, and coordinates work across the entire team without losing the thread.
- Living Wiki with Auto-Capture: PromptQL automatically extracts knowledge from conversations — facts, decisions, and procedures — and proposes wiki updates for human review, keeping documentation accurate and current.
- Data Connectors & Integrations: Connect databases, HTTP APIs, and webhook-based event sources. PromptQL queries data in place without moving or indexing it, enforcing access controls throughout.
- Autonomous Action Execution: Beyond answering questions, PromptQL can execute approved actions — like creating DNS records, updating configs, or calling APIs — with human approval gates built in.
- Shared Context & Citations: All AI responses are grounded in sourced, citable information from the wiki and connected data sources, so teams can trust and verify every output.
Use Cases
- Operations teams coordinating vendor onboarding across finance, engineering, and legal without endless Slack threads
- Data and analytics teams querying connected databases and APIs through natural language in a shared, auditable workspace
- Engineering teams managing infrastructure tasks like DNS changes or API configurations with AI-assisted execution and human approval
- Healthcare and fintech companies building AI-assisted data workflows with governance, citations, and access controls built in
- Fast-moving startups capturing institutional knowledge automatically as the team works, preventing knowledge loss as the organization scales
Pros
- Reduces Cross-Team Coordination Overhead: PromptQL knows who to involve and can pull context from the wiki, dramatically cutting down the back-and-forth typically needed to get answers across departments.
- Knowledge Capture Happens Automatically: Rather than relying on team members to manually update docs, PromptQL surfaces wiki edits as a natural byproduct of conversations, keeping institutional knowledge fresh.
- Executes, Not Just Advises: Unlike pure chatbots, PromptQL can take approved actions against connected systems — making it a true operational tool, not just a Q&A assistant.
- Built-in Accountability: Approval flows, edit histories, and citations ensure that AI-driven actions are auditable and that humans remain in control of critical decisions.
Cons
- Currently in Preview: PromptQL is still in early access, which means features may change, stability could vary, and enterprise-grade SLAs may not yet be available.
- Requires Data Integration Setup: To unlock its full value, teams need to connect their databases and APIs, which involves an initial setup investment and may require technical expertise.
- Best Suited for Collaborative Teams: Solo users or very small teams may not see the full multiplayer and wiki benefits, as the platform's strengths emerge at team scale with diverse data sources.
Frequently Asked Questions
What makes PromptQL different from a regular AI chatbot?
PromptQL is designed for teams, not individuals. It combines multiplayer collaboration, a self-updating wiki, and the ability to actually execute actions against connected systems — not just generate text responses.
How does the wiki feature work?
As your team interacts in PromptQL threads, the AI identifies key facts and decisions and proposes wiki updates. Team members can review and approve these additions before they're saved, keeping the knowledge base accurate without extra manual work.
What data sources can PromptQL connect to?
PromptQL supports databases, HTTP APIs, and incoming webhook events. It queries data directly without moving or indexing it, and enforces access controls from your existing data layer.
Is PromptQL free to use?
Yes, PromptQL is currently free to try during its preview period. Future pricing has not yet been fully announced.
Who is PromptQL built by?
PromptQL is built by Hasura, the company behind Hasura Cloud and Hasura DDN. It is part of Hasura's broader data platform ecosystem.
