Shared threads and shared context
Teams work in shared AI threads instead of isolated chats. PromptQL keeps the context available to everyone in the workspace so the next person does not start from scratch.
Shared-context AI for team work
PromptQL is a multiplayer AI workspace for teams that want shared context instead of isolated chat histories. The product is presented as a team brain that helps people work in shared AI threads, show their work with sources and assumptions, and turn useful context into wiki updates that others can reuse.
The site positions PromptQL for situations where context is spread across multiple systems and people keep having to re-explain the same things. It starts from existing information in tools such as Slack, docs, tickets, CRM, and warehouse tables, then turns corrections made during work into cited knowledge, reusable skills, and semantic-model changes.
Core capabilities
Context that compounds
PromptQL learns from corrections made in the flow of work. The homepage says corrections stick for everyone and become reusable skills, team knowledge, or semantic-model changes.
Cited answers and reviewable edits
On each task, PromptQL shows the sources it used and the assumptions it made. The homepage also describes cited, scoped knowledge with revision history and editorial controls.
Cross-source context gathering
PromptQL starts from context scattered across Slack, docs, tickets, CRM, and warehouse tables. It can surface ambiguity, prompt teammates for missing context, and turn the result into wiki updates.
Usage-based billing in OLUs
Pricing is usage-based through Operational Language Units. The pricing page says OLUs normalize token types and models, include infrastructure and sandbox hosting, and expose per-step usage breakdowns.
Model choice and enterprise controls
The platform supports model choice per thread and mentions open-weight and proprietary models. Enterprise features listed on the pricing page include SSO, private networking, dedicated VPC/BYOC, audit trails, and fine-grained permissions.
Practical ways teams use PromptQL
Account and churn analysis
Customer success or account teams can ask whether an account is at risk, pull together usage and ticket signals, and preserve the reason behind recurring patterns so the team does not keep relearning the same issue.
Metric definition and source-of-truth maintenance
Revenue, operations, or finance teams can reconcile where a metric should come from, capture the correct source of truth, and mark stale assumptions so later threads use the updated semantic model.
Shared troubleshooting and handoffs
Support, product, and engineering teams can resolve a missing-context question by pulling in the teammate who knows, then converting the answer into a cited wiki update for future sessions.
Recurring request handling
Marketing and other knowledge-heavy teams can use PromptQL as a starting point for recurring requests, so work begins in a thread instead of in a separate queue of tickets and follow-ups.
Pros and Cons
Pros
- Shared threads help teams work from one context instead of fragmented chats.
- Corrections made once can become reusable knowledge for the whole team.
- The product shows sources and assumptions on tasks, which supports review and trust.
- The pricing page supports model selection per thread and includes enterprise controls such as SSO, private networking, and audit trails.
- Usage is measured in OLUs, with per-step breakdowns and usage dashboard visibility.
Cons
- The public pages provided do not give full integration documentation or a complete list of supported systems.
- Setup, deployment, and team-size limits are not clearly documented on the sources provided.
- Some details about how wiki edits, skills, and semantic-layer changes are applied are described at a high level rather than in technical depth.
FAQ
What is PromptQL?
PromptQL is a multiplayer AI workspace that lets teams work in shared threads and then turn the context they create into suggested wiki edits. The site describes it as a shared brain for team AI work.
How does PromptQL pricing work?
The pricing page says PromptQL measures usage in Operational Language Units (OLUs), a normalized unit that rolls up different token types and models into one billable measure.
Can I choose different AI models for different tasks?
Yes. The pricing page says you can switch models per thread, and even mid-thread. It also notes that PromptQL gives access to every model available in its lineup, which expands over time.
What kinds of data or context does PromptQL connect to?
The homepage says PromptQL starts from context already scattered across Slack, docs, tickets, CRM, and warehouse tables, and the demo page highlights challenges like semantic layers and tool schemas.
Is PromptQL meant for individuals or teams?
PromptQL is presented as a team product for shared AI threads, wiki updates, and scoped access control. The source does not spell out limits on team size or deployment setup on the public pages provided.
Quick Facts
- Category
- Multiplayer AI workspace
- Primary use
- Shared team threads and context management
- Pricing model
- Usage-based, measured in Operational Language Units (OLUs)
- Source domain
- promptql.io
- Public workflow
- Work in shared AI threads, review suggested wiki edits, then accept or edit them
- Enterprise options
- SSO, private networking, dedicated VPC/BYOC, audit trails
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