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GQueries
Grounded memory and authorized evidence for production AI
GQueries gives AI systems a controlled evidence layer between retrieval and the final answer. It combines persistent memory, authorization, provenance and fail-closed grounding so applications can decide not only what information was found, but whether it is actually allowed and sufficiently supported to be used. Bring your own LLM, keep your existing stack, and send only the evidence needed for the current query.
We built GQueries because retrieval alone is not enough for production AI.
Finding a document, memory, or passage does not automatically mean an AI system should be allowed to use it in an answer.
GQueries adds a controlled layer between retrieval and generation: persistent memory, authorization, provenance, evidence selection, and fail-closed grounding.
The goal is to help applications answer a few important questions before delivering a response:
Is this evidence relevant?
Is it authorized for this requester?
Is the claim actually supported?
Should the system answer, or abstain?
GQueries is designed to work with existing LLMs and retrieval stacks through BYOK, rather than replacing them.
We’re launching it as infrastructure for teams building agents, support systems, enterprise assistants, and other AI applications where unsupported or unauthorized answers are not acceptable.
I’d love to hear how you currently handle grounding and evidence validation in production.
About GQueries on Product Hunt
“Grounded memory and authorized evidence for production AI”
GQueries was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #160 on the daily leaderboard. GQueries gives AI systems a controlled evidence layer between retrieval and the final answer. It combines persistent memory, authorization, provenance and fail-closed grounding so applications can decide not only what information was found, but whether it is actually allowed and sufficiently supported to be used. Bring your own LLM, keep your existing stack, and send only the evidence needed for the current query.
On the analytics side, GQueries competes within API, SaaS and Developer Tools — topics that collectively have 661k followers on Product Hunt. The dashboard above tracks how GQueries performed against the three products that launched closest to it on the same day.
Who hunted GQueries?
GQueries was hunted by Aletheion AGI. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of GQueries including community comment highlights and product details, visit the product overview.
Aletheion AGI