PromptQL
A shared AI brain for your whole team — instead of everyone copy-pasting into their own private ChatGPT, PromptQL keeps one team-wide memory that gets smarter every time someone corrects it.
🔗 Visit PromptQL
Description
Most teams end up with AI knowledge scattered everywhere: one person's ChatGPT history knows how to handle a tricky customer case, another's Claude chat knows the real reason a project got delayed, and none of that knowledge is shared. PromptQL's idea is simple — put the AI in a group chat instead of a private one, so when someone corrects it or teaches it something, that correction sticks for the whole team, not just that one conversation.
Built by Hasura (the GraphQL company), PromptQL is a 'multiplayer AI' agent that builds and maintains a shared, Wikipedia-style knowledge wiki for a team, with full revision history and scope-based access control. It pulls context from the tools a team already uses — Slack, Google Docs, Salesforce, Snowflake, PostHog — and can bootstrap a working wiki entry from existing sources in about 60 seconds. Every answer comes with citations back to its sources and assumptions, so users can verify rather than blindly trust it. It's available across Mac, Windows, iOS, and Android, and is aimed particularly at teams in finance, healthcare, retail, and go-to-market functions that need consistent, auditable answers rather than each person getting a slightly different AI response.
💬 Our review
The short version: if your team already fights the problem of 'the AI gave three different people three different answers to the same question,' PromptQL is built specifically to fix that by making the correction process itself shared instead of private.
Compared to just using ChatGPT Enterprise or Claude for Work, PromptQL's real differentiator is the wiki-with-citations layer — it's not just a smarter chatbot, it's building an actual auditable knowledge base as a side effect of people using it, which matters a lot in regulated industries like healthcare and finance where 'why did the AI say that' needs a real answer. Against a more general knowledge tool like Notion AI or Glean, PromptQL leans harder into the 'correct once, fixed for everyone' workflow rather than just search over existing docs. The tradeoff is that PromptQL doesn't publish clear self-serve pricing on its site, which makes it harder to evaluate against competitors without talking to sales — a real friction point for a team that just wants to try it quickly, and the value really shows up at the 70-person-team scale (Hasura cites ~250 wiki contributions/day at that size) rather than for a 3-person startup.
💰 Pricing
📊 Global score
🤖 AI-enriched data
Pas de grille tarifaire publique détaillée au moment de la rédaction — contact commercial requis pour un chiffrage précis
Pros
Corriger l'IA une fois corrige la réponse pour toute l'équipe
Citations systématiques des sources — auditable
Bootstrap d'une base de connaissance en ~60 secondes depuis les outils existants
Clients entreprise sérieux (Cisco, McDonald's, Instacart)
Cons
Tarifs non publiés — nécessite un contact commercial pour évaluer le coût réel
Valeur surtout démontrée à l'échelle (équipes de 70+ personnes), moins évidente pour une petite équipe
Dépend de la qualité des intégrations (Slack, Salesforce, etc.) déjà en place