PromptQL
PromptQL is a multiplayer AI agent with shared threads and a shared context “brain” that connects to your business data and systems to deliver grounded, traceable analysis and automations with production-grade reliability.
https://promptql.io/?ref=producthunt
Product Information
Updated:Aug 8, 2026
What is PromptQL
PromptQL (built by the team behind Hasura) is an AI platform designed to help teams ask complex business questions and automate high-stakes workflows using natural language, while staying tightly grounded in real enterprise data. Instead of acting like a standalone chatbot, PromptQL is positioned as a shared coworker for teams: conversations happen in open channels and threads, context is shared across teammates, and the system continuously learns your organization’s terminology, definitions, and operating rules. It connects to databases, warehouses, SaaS tools, and APIs where data already lives, aiming to provide trustworthy answers and repeatable workflows without forcing users to write SQL or rebuild pipelines.
Key Features of PromptQL
PromptQL is a multiplayer AI platform built for teams that connects to your existing data sources (databases, SaaS apps, APIs, code agents) and answers high-impact business questions with grounded, traceable reasoning. It captures and maintains shared business context in a Wikipedia-like semantic/wiki layer that improves through in-flow corrections, turning conversations and fixes into reusable team knowledge and skills. PromptQL emphasizes reliability by separating planning from execution, enforcing per-user permissions at the data layer, and providing observability into AI spend and access control—enabling analysis and automation without moving or reshaping data.
Shared AI threads (“multiplayer AI”): Collaborative, Slack-like threads where teams explore questions together; context and conclusions persist so work isn’t lost across handoffs.
Living semantic + wiki-style context graph: Continuously builds interconnected pages for skills, knowledge, and business definitions; corrections become cited, reusable context that prevents “wiki rot.”
Connects to data where it lives (no ETL): Integrates with warehouses/databases (e.g., Postgres, Snowflake, BigQuery, Databricks) and SaaS tools (e.g., Slack, Salesforce, Google Workspace, GitHub) and introspects schemas to form a unified data graph without copying or reshaping data.
Reliable agentic planning and execution: Creates query plans and processes data outside the LLM context to improve accuracy and repeatability beyond typical RAG/Text-to-SQL/tool-calling approaches.
Governance: scopes, permissions, and auditability: Scope-based visibility and edit controls across retrieval and updates, revision history/audit trails, and per-user permission enforcement at the underlying data layer.
Observability and cost controls for AI workloads: Provides visibility into spend across context exploration, query planning, and execution, with recommendations and controls to optimize each layer.
Use Cases of PromptQL
GTM revenue and deal-risk analysis: Connect Salesforce/Clari and internal data to identify at-risk deals, define “risk” rules, score pipeline, and suggest actions—kept current as deals change.
Customer success churn-risk triage: Combine product usage, support tickets, and CRM context to assess churn risk; capture recurring patterns (e.g., known operational issues) as shared rules that improve future scoring.
Retail performance and supply chain insights: Analyze product performance, promotions, and supply chain health across multiple systems to answer operational questions quickly and produce reusable analyses.
Financial services compliance and AML workflows: Query transactions, detect suspicious patterns, and streamline compliance investigations with explainable reasoning and governed access to sensitive data.
Cross-functional “single source of business truth”: Maintain shared definitions (e.g., what counts as revenue, exclusions like test accounts) and automatically suggest wiki updates when teams discover stale or missing context.
Engineering + data operations with secure code delegation: Delegate code investigation, feature development, or browser testing to connected coding agents in a secure environment, while keeping credentials protected and actions auditable.
Pros
Strong team collaboration model: shared threads plus a shared, evolving context/wiki layer reduces re-explaining and preserves institutional knowledge.
High reliability focus: planning/execution separation, grounded sources, and repeatability aim to outperform basic RAG/Text-to-SQL approaches for complex enterprise questions.
Enterprise-ready governance: scopes, audit trails, and per-user data-layer permissions help with sensitive and multi-tenant use cases.
Broad connectivity without data movement: integrates across databases and SaaS tools and builds a unified data graph via schema introspection.
Cons
Requires ongoing human correction to reach peak accuracy: the system “learns by being corrected,” so initial setup and iterative refinement are part of adoption.
Integration and governance complexity: connecting many sources and defining scopes/permissions can add upfront operational work for larger organizations.
Cost management may be non-trivial: multi-step agent workflows and model choice can drive spend, necessitating monitoring and controls.
How to Use PromptQL
1. Choose where you’ll use PromptQL: Decide whether your team will interact with PromptQL in the PromptQL app or as an AI agent inside Slack/Teams. The goal is to put PromptQL in the place where work and questions already happen.
2. Connect your data sources and tools: Connect the systems where your data already lives (e.g., Postgres, Snowflake, BigQuery, Databricks) and SaaS tools (e.g., GitHub, Slack, Salesforce, Google Workspace). PromptQL queries data in-place (no copying/ETL) and enforces per-user permissions at the data layer, including row/column controls.
3. Start a new thread with a real business question: Use “New thread” as your starting point and ask a specific question in natural language (no need to write SQL). Start with something simple but real—like a metric you already track or an analysis you’ve done manually—so PromptQL can learn your expectations.
4. Review the query plan before execution: Before running anything, PromptQL shows a structured query plan describing what it will do, which sources it will query, and why. Read it to confirm assumptions, definitions, and data sources—this is a key reliability feature (no black box).
5. Edit the query plan when needed (teach PromptQL): If the plan is missing context or using the wrong definition/source, click the pencil icon next to the query plan and modify it. This is where you apply domain expertise (e.g., correct what “revenue” means or which table is authoritative). PromptQL remembers these corrections and self-corrects in future threads.
6. Execute the plan and inspect results: Run the plan to query your connected systems. PromptQL pulls the necessary data and uses AI to help interpret it. Validate outputs against expectations; errors are constrained to checkable points (e.g., SQL engine rejects invalid syntax).
7. Use artifacts to capture and reuse outputs: PromptQL typically produces an artifact (structured output like a table, text, or visualization). Artifacts store processed results and can be referenced later in the conversation, exported, or used to start new threads for follow-up analysis.
8. Correct assumptions and add reusable context to the shared wiki: When PromptQL makes an assumption (e.g., what counts as a churn signal) or misses a known edge case, correct it in the flow. PromptQL can propose a “wants to learn” update—review/edit it and add it to the wiki so the knowledge becomes cited, scoped, and reusable for the team.
9. Collaborate in-thread and tag the right people: If you’re unsure about context, loop in a teammate directly in the thread (e.g., someone close to customer tickets). PromptQL captures the back-and-forth and turns it into shared context, reducing meetings and status updates.
10. Govern access with scopes: Configure scopes to control what can be retrieved, created, and updated end-to-end. Use scopes to separate internal playbooks from customer-facing pages, handle confidential/HR/finance use cases, and apply granular view/edit controls (including bulk operations).
11. (Optional) Use the Program API to embed PromptQL into workflows: For automation, call PromptQL via its Program API from webhooks or serverless functions (e.g., classify issues by label, generate summaries, analyze trends, send notifications). Implement tools outside PromptQL (search, read/write data, external APIs) and use AI primitives (classify, summarize, extract) to operationalize LLM capabilities.
12. (Optional) Integrate the Natural Language API in your app: From PromptQL Settings, copy your Natural Language Endpoint (e.g., https://api.promptql.pro.hasura.io/query) and generate an API key. Use these in your client/server to send natural-language requests and render streamed chunks (messages, plans, artifact updates, code outputs).
13. Iterate: treat every task as a chance to improve reliability: PromptQL improves through back-and-forth. Don’t expect one-shot perfection early; instead, restate key details when continuing a line of inquiry (or start from an artifact). Each correction compounds into durable team context rather than decaying in a separate wiki.
PromptQL FAQs
PromptQL is a multiplayer AI agent with shared threads and shared context (“shared brain”) designed to work from the context your team already has across tools like Slack, docs, tickets, CRM, and warehouse tables.
PromptQL Video
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