How does PromptQL organize information?

It extracts key points from threaded conversations and links them into a dynamic wiki, creating a searchable knowledge base of skills and insights.

Who can benefit from using PromptQL?

Teams and organizations that want to centralize collective knowledge from chats, meetings, or discussions to improve collaboration and reduce information silos.

Does PromptQL integrate with existing tools?

Yes, it integrates with popular messaging and collaboration platforms to automatically capture and structure conversations.

Is PromptQL secure for sensitive team data?

Yes, it uses enterprise-grade encryption and access controls to ensure team data remains private and secure.

PromptQL - AI Agents tools - Free trial, pricing intro, performance review, official site access and online experience

What is PromptQL?

PromptQL is a multiplayer AI agent that automatically organizes shared context from threaded conversations into an interconnected wiki of skills and knowledge. It functions like Claude or ChatGPT but with shared threads and a shared brain, eliminating the need for users to manually maintain context. The tool learns by being corrected, starting from context already scattered across Slack, docs, tickets, CRM, and warehouse tables. It shows its work on every task—citing sources and assumptions—so corrections become reusable skills, team knowledge, or semantic-model changes.

Application scenarios

Customer success
Maya can ask if a customer like Acme is a churn risk, and PromptQL analyzes usage data, support ticket sentiment, and CRM to flag risks.
Support escalation
Sam can provide context about recurring export lags, which PromptQL learns and applies to future churn assessments.
Revenue reporting
Dana can request Q1 revenue by region for a board deck, and PromptQL pulls data from analytics tables.
Wiki seeding
Maya can seed a wiki for a new account like AcmeCorp, and PromptQL reads Slack, Google Docs, Snowflake, PostHog, and Salesforce CRM to create wiki pages.
Knowledge management
The tool captures ambiguity and conflicts, prevents context rot, and suggests context updates as people work.
Team collaboration
Multiple users can correct the AI once, and that correction becomes shared context for everyone—reusable skills like “exclude test accounts from revenue.”

Core Features

Shared threads and brain
PromptQL operates as a multiplayer AI agent with shared threads and a shared brain, unlike single-user tools like Claude or ChatGPT.
Automatic context bootstrapping
It seeds shared context from existing sources—Slack, Google Docs, Snowflake, PostHog, Salesforce CRM—in under 60 seconds.
Learn by correction
Users correct the AI once, and that correction becomes a reusable skill, team knowledge, or semantic-model change for everyone.
Source transparency
On every task, PromptQL shows its work—the sources it pulled and the assumptions it made.
Wiki creation
The tool automatically creates wiki pages from the context it gathers, with real stats showing contributions across a team of 70.
Context suggestion
It suggests context updates as people work, capturing new context and linking it to prevent rot.
Conflict handling
PromptQL captures ambiguity and conflicts, allowing users to resolve them in the flow of work.
Cross-platform integration
It reads from Slack, Google Docs, Snowflake (consumption data), PostHog (product analytics), and Salesforce CRM.

Target users

PromptQL is designed for teams that need to maintain shared context without manual wiki updates. Ideal roles include customer success managers (e.g., Maya), support agents (e.g., Sam), data analysts, and anyone in operations or revenue reporting. It benefits teams of any size, with real usage stats from a team of 70, but scales to larger organizations.

How to use PromptQL?

Get started: Visit the PromptQL website and click “Get started” or “Book demo.”
Download the app: Install the desktop client for Mac (Apple Silicon) or Windows, or the mobile app from the iOS App Store or Google Play.
Point it at your context: Connect PromptQL to your existing data sources—Slack, docs, tickets, CRM, and warehouse tables.
Correct it once: When the AI makes an assumption, correct it or tag a teammate who knows. The correction becomes shared context.
Use the wiki: PromptQL automatically creates wiki pages from the context it gathers; review and edit contributions before adding them to the wiki.
Work in the flow: Ask questions like “Are they a churn risk?” or “Pull Q1 revenue by region,” and PromptQL shows its work with cited sources.

Effect review

PromptQL delivers on its promise to eliminate the “second job” of maintaining context. The demo shows a realistic workflow where Maya asks about churn risk, PromptQL analyzes multiple data sources, and Sam provides corrective context that becomes a reusable skill. The real wiki-contribution stats from a team of 70 suggest the tool is actively used and effective. For teams drowning in scattered Slack threads and stale wikis, PromptQL offers a practical, automated solution that compounds context from real work rather than decaying in a manual wiki.

PromptQL