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PromptQL

Collaborative AI workspace with shared team context and an automatically maintained knowledge base

Source rating 5.0 (75) 72k views 72k/mo traffic 0 saves Paid
Multiplayer AI Collaborative AI Shared AI threads
More categories (57)
Team AI assistant Shared context AI knowledge base Team wiki Self-organizing wiki Knowledge Management Tribal knowledge capture AI agent AI Analyst Business intelligence Data analysis Enterprise search Connected data sources Slack integration CRM integration SaaS integrations Data warehouse analysis Google Docs integration Salesforce integration Snowflake integration PostHog integration Source citations Transparent AI reasoning Human-in-the-loop AI Collaborative review Reusable AI skills Semantic layer Semantic modeling Context Management Context compounding Knowledge Sharing Revision history Editorial controls Access Control Scoped permissions Multi-user permissions Data governance Audit Trails Enterprise security SSO Private networking Dedicated VPC Bring your own cloud Bring your own LLM OLU pricing Usage-based pricing Prepaid AI billing Per-user quotas Spending controls Open-weight models AI workflow automation Customer churn analysis Revenue analysis Operational analytics Financial Services AI Healthcare AI Retail AI
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From $0.20 per OLU

Verdict

A quick read before you dig into the full page.

Best if
You are aI analysts and need: Analyze customer usage, support tickets, and CRM data to assess churn risk
Skip if
You need a free plan with no credit card
Pick this when
Paid · From $0.20 per OLU

Overview

PromptQL is a multiplayer AI platform for teams that combines shared AI threads, connected business data, collaborative corrections, and a self-organizing team wiki. It works like Claude or ChatGPT with shared context, allowing teammates to review answers, tag experts, preserve reasoning, and turn corrections into reusable skills and knowledge. PromptQL connects sources such as Slack, documents, CRM systems, SaaS applications, data warehouses, coding agents, and product analytics while providing scoped permissions and audit controls.

PromptQL screenshot

Best for

Short scenarios where PromptQL tends to shine.

  • Analyze customer usage, support tickets, and CRM data to assess churn risk

    Best for AI analysts

  • Create shared account knowledge by combining Slack, call transcripts, product analytics, warehouse data, and CRM records

    Best for Data analysts

  • Detect and correct stale data sources used in financial or board reports

    Best for Business intelligence teams

  • Build reusable revenue, customer-success, and operational analysis skills

    Best for Customer success managers

  • Run AI analyst workflows for financial services, healthcare, and retail

    Best for Sales operations teams

  • Research business questions across connected SaaS applications and databases

    Best for Support teams

  • Maintain internal playbooks, semantic models, and customer-facing knowledge with controlled access

    Best for Revenue operations professionals

  • Collaborate on technical investigations involving coding agents, cloud tools, monitoring systems, and project-management platforms

    Best for Financial services teams

Example outputs 1

Sample generations and before/after results for PromptQL.

PromptQL screenshot
PromptQL screenshot

Access & connections

Where you can use PromptQL, and what it plugs into.

Platforms

Web

Integrations

Slack

Capabilities

What this tool accepts, produces, and is strongest at.

Highlights

  • Multiplayer AI threads with shared team context
  • Collaborative answer review, correction, and refinement
  • Automatically suggested wiki and knowledge-base updates
  • Connections to Slack, Google Docs, CRM systems, SaaS applications, data warehouses, and analytics tools
  • Source citations, visible assumptions, and step-by-step work
  • Reusable team skills and semantic-layer knowledge
  • Wikipedia-like pages with revision history and editorial controls
  • Scoped access controls and granular multi-user permissions
Show 6 more
  • Data-access audit trails
  • External, confidential, and personal context separation
  • Per-thread model selection across proprietary and open-weight models
  • OLU-based usage tracking with per-step consumption details
  • Per-user quotas, spending alerts, prepaid billing, and auto-pause controls
  • Enterprise security features including SSO, private networking, dedicated VPC, and BYOC

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FAQ 11

What is PromptQL?
PromptQL is a multiplayer AI platform for teams that combines shared AI threads, connected business data, collaborative corrections, and a self-organizing team wiki. It works like Claude or ChatGPT with shared context, allowing teammates to review answers, tag experts, preserve reasoning, and turn corrections into reusable skills and knowledge. PromptQL connects sources such as Slack, documents, CRM systems, SaaS applications, data warehouses, coding agents, and product analytics while providing scoped permissions and audit controls.
How to use PromptQL?
Create a shared AI thread and ask PromptQL to analyze connected business data, documents, messages, or applications. Review the sources, assumptions, and results it provides. Tag teammates to verify or correct the answer when needed. PromptQL can then suggest the correction as a wiki, skill, or semantic-layer update; review and approve it so the shared context is available to the team in future threads. Choose models per thread, monitor OLU usage, and configure permissions, quotas, and spending limits as needed.
What is PromptQL?
PromptQL is a collaborative AI platform with shared threads, connected business data, and a team knowledge base that learns from reviewed corrections.
How does PromptQL capture team knowledge?
When a teammate corrects an answer or adds context, PromptQL suggests turning that information into a wiki page, reusable skill, or semantic-layer update for future sessions.
What data sources can PromptQL connect to?
PromptQL can connect to sources including Slack, Google Docs, call transcripts, Snowflake, PostHog, Salesforce CRM, SaaS applications, coding agents, and business events.
What is an OLU?
An Operational Language Unit is a normalized unit of AI work that combines token usage across different token types and models into a consistent billing unit.
How much does PromptQL cost?
The Starter plan costs $0.20 per OLU, with an introductory at-cost rate of $0.14 per OLU. There are no minimums or per-seat fees. Enterprise pricing is customized.
Is there a free plan or free trial?
Yes. New projects receive up to $100 in free credits, plus up to $70 for each teammate who joins. No credit card is required to start.
Can I use my own OpenAI Codex plan?
Yes. After connecting an OpenAI Codex subscription through the bring-your-own-LLM option, PromptQL charges $0 for OLUs consumed on that model.
Can PromptQL control access to sensitive information?
Yes. Scopes provide granular retrieval, creation, update, viewing, and editing permissions for external, internal, personal, and confidential contexts.
What happens when free credits or prepaid balance run out?
You can add a payment method and use prepaid billing. Work automatically pauses when the balance reaches zero, and users can configure spending limits, quotas, and auto-top-up settings.
PromptQL From $0.20 per OLU
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