PromptQL

PromptQL: The Multiplayer AI Agent Redefining Shared Context

Maintaining context is often described as the second job nobody wants, yet it is essential for team productivity. This is where PromptQL steps in. PromptQL is a revolutionary multiplayer AI agent—built to function like Claude or ChatGPT but enhanced with shared threads and a shared brain. Instead of individual silos of information, PromptQL creates a collaborative environment where every interaction contributes to the collective intelligence of the organization.

In the modern workplace, knowledge is often scattered across Slack, internal documents, support tickets, CRMs, and warehouse tables. PromptQL points directly at this existing context, learns from it, and ensures that once a correction is made, it sticks for everyone. This eliminates the need for repetitive explanations and ensures that the entire team benefits from individual expertise.

What’s PromptQL?

PromptQL is more than just a chatbot; it is a multiplayer AI agent designed to bootstrap and maintain shared context in real-time. Unlike traditional AI tools that treat every user as an island, PromptQL operates on a shared thread model. When one teammate teaches the AI a specific nuance—such as excluding test accounts from revenue metrics—that knowledge becomes a reusable skill available to the entire team.

At its core, PromptQL serves as a persistent semantic model. It shows its work by citing the sources it pulled from and the assumptions it made, allowing for a transparent and auditable workflow. It transforms the way teams handle data, moving from static wikis that nobody opens to a dynamic system where context compounds from real work.

Key Features of PromptQL

1. Multiplayer Shared Brain

PromptQL features shared threads that allow team members to collaborate within the same AI interaction. This "shared brain" ensures that everyone is looking at the same data, utilizing the same context, and building upon the same set of facts.

2. Learn by Correction

One of the standout features of PromptQL is its ability to learn through human feedback. When the AI makes an assumption or uses a stale data source, a user can correct it once. PromptQL then prompts the user to "Add to wiki," turning that correction into a permanent piece of shared context or a semantic-model change.

3. Rapid Context Bootstrapping

PromptQL can bootstrap shared context in as little as 60 seconds. It integrates seamlessly with a variety of data sources, including:

• Slack: Reading channel history and conversations.
• Google Docs: Accessing call transcripts and documentation.
• Snowflake & PostHog: Analyzing consumption data and product analytics.
• Salesforce & Netsuite: Pulling CRM and financial data.

4. Wikipedia-Like Operating Model

PromptQL utilizes a familiar wiki-style interface that includes:

• Citations to real work: Every claim is backed by source data.
• Revision history and audit trails: Track how knowledge has evolved over time.
• Editorial controls: Technical and non-technical users can manage content easily.
• Notifications: Stay updated on page creations, edits, and deletions.

5. Governance with Scopes

Security is paramount with PromptQL. The platform allows teams to govern information using granular scopes. This ensures that personal, confidential (HR/Finance), internal, and external (customer-facing) data remain strictly separated while still being accessible to the right people.

How to Use PromptQL

Using PromptQL is designed to be intuitive and integrated into your existing flow of work. Here is how you can get started:

1. Seed the Wiki: Begin by pointing PromptQL at your data sources. In just 60 seconds, it can read your Slack threads, Google Docs, and data warehouses to create the initial knowledge base.
2. Start a Thread: Open a new thread to perform a task, such as pulling revenue data or analyzing churn risk. PromptQL will pull from its shared context to provide an answer.
3. Review and Correct: PromptQL shows its work. If it uses a stale source (e.g., pulling revenue from an old analytics table instead of Netsuite), simply provide the correction in the thread.
4. Add to Wiki: Once corrected, PromptQL will suggest an update. Click "Add to wiki" to save this new rule. Now, every future query by any team member will use the updated logic.
5. Tag for Context: If the AI is unsure, you can tag a teammate within the PromptQL thread to provide the missing piece of information, which then becomes part of the permanent shared context.

Use Case Scenarios

Financial Services

In financial services, PromptQL can transform data workflows by ensuring that revenue reporting and ARR health are sourced from the correct, verified tables. It helps analysts move away from manual spreadsheets and toward a unified semantic layer.

Healthcare

PromptQL acts as an AI analyst for healthcare data workflows, managing complex context across various medical documents and patient data systems while maintaining strict security and audit trails.

Retail and GTM

For retail and Go-To-Market teams, PromptQL helps analyze product analytics and customer sentiment. It can identify churn risks by combining usage data from Snowflake with support ticket sentiment from Zendesk or Slack, providing a comprehensive view of account health.

Why Teams Choose PromptQL: The Vibe Shift

Teams using PromptQL report a significant "vibe shift" in how they handle work. By moving away from lists and queues toward active threads, the friction of "work between the work" is eliminated.

"The friction was the work between the work, and that friction constantly broke my flow state. I am always in a flow state now," says Shahidh K Muhammed, Director of Engineering.

Anushrut Gupta, Applied AI Lead, notes: "I don’t keep a TODO list anymore... before PromptQL, work was a queue. Now, the unit of work is a thread already in motion."

FAQ

Q: What platforms is PromptQL available on? A: PromptQL is available for Linux (AppImage and .deb), and can be downloaded from the iOS App Store and Google Play Store.

Q: How does PromptQL handle sensitive data? A: PromptQL uses "Scopes" to handle end-to-end retrieval, creation, and updates. You can set granular controls for personal, confidential, internal, and external access to ensure data security.

Q: Do I need to be a technical user to update the wiki? A: No. PromptQL is designed for both technical and non-technical users. Updating the shared context is as simple as reviewing an AI-suggested edit and clicking "Add to wiki."

Q: How quickly can PromptQL be set up? A: You can bootstrap shared context for your team in as little as 30 to 60 seconds by connecting your primary data and communication tools.

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PromptQL is a product by Hasura, Inc. All rights reserved (2026). Try PromptQL or Book a Demo today to start building your team's shared brain.