Your methods become workflows that run on a schedule. Your corrections become rules that stay. What one person works out this week, the whole team applies the next.
Every community manager
posts drafted, reviewed by you, published on time
Prepare tomorrow's account review and flag anything that needs a decision.
We are certified neither SOC 2 nor ISO 27001, and we do not claim to be. What we state here is verifiable in our code.
Most tools stop at the answer. What is missing is what the company keeps once the conversation is closed.
Powerful intelligence, but the work stays scattered across private conversations. The colleague next door starts from zero.
Company context becomes reachable. But the system stays reactive: you ask, it answers, and nothing accumulates.
Methods run under control. Corrections become rules. What the team learns stays in the company, even when someone leaves.
Every playbook declares its steps, the tools it may call and its review points. You see what is going to happen before it happens.
One place to chat with AI that knows your company, review playbook results, trigger quick actions, and make decisions together. Not another chat window — the surface where expert methods meet daily work.
You attach a library and a memory to the workspace once. Every later question inherits them, and nobody re-pastes a document into a conversation.
You correct a result in plain language. The sentence is extracted into an imperative rule, filed in the right memory, and re-injected at the top of every later run. Nobody types it twice.
You correct an output in plain language, inside the result itself.
The sentence becomes a typed rule: always, never, prefer, escalate when.
It is filed at company level, not inside the playbook. Deleting the playbook does not erase it.
It applies from the next run on, including for a colleague who joined the week after.
« Correct once. It learns forever. »
A workspace can use a library. A playbook can read a memory. The Brain brings several memories together. Composition is the product.
Trust & safety
Permissions, guardrails and compliance.
Consistency
One version of truth across the organization.
Actionability
From knowledge to execution.
Intelligence
Continuous learning and improvement.
Connected sourcesValue deliveredHover a block or a relationship to see how it connects.
Five objects, four relationships. Everything a run touches is declared, and everything it does is recorded.
Workspace
people + preferences
Manage users, roles, teams and personalization. Define how people work and what matters to them.
Shapes · context & access
The workspace decides who reads what, and with which preferences. It shapes the memory without owning its content.
Library
knowledge corpus
Centralize, organize and enrich all company knowledge from multiple sources in one trusted place.
Feeds · content & facts
The library supplies the raw material. What is extracted from it becomes facts, and those facts are what a run can cite.
+26 moreMemory
facts + rules
The system of record for structured knowledge: facts, relations, rules and policies that stay consistent.
Playbook
declared execution
Turn knowledge into actionable processes. Define, standardize and orchestrate how work gets done.
Uses · rules & facts
A playbook reads the rules before acting. A correction made once is applied on the next run without anyone repeating it.
Brain
higher-level knowledge
Synthesize patterns, learn and reason across memory to generate insights, predictions and recommendations.
Learns from · history & outcomes
The Brain reads what actually happened, not what was planned. Repeated patterns become knowledge proposals.
Trust & safety
Permissions, guardrails and compliance.
Consistency
One version of truth across the organization.
Actionability
From knowledge to execution.
Intelligence
Continuous learning and improvement.
This is not about model quality. It is about who reaches what, what may act, what it costs, and what must be reviewed.
A playbook can only call a tool that was explicitly mounted. An unauthorised name is refused before any execution, and an empty list blocks the call.
The prohibitions declared in the manifest are held by the engine, not merely suggested to the model in an instruction it could work around.
Code produced during a run executes isolated, as an unprivileged user, with memory, processor and duration limits.
A mode shows what would be written outside without writing it. Outgoing actions become prepared tasks waiting for your approval.
Model used, tokens spent, input, output, sources cited, confidence. And every human decision too: who reviewed, when, against which recommendation.
Detection of names, addresses, IBAN and card numbers runs on our servers and calls no external service for that detection.
The answers are written for you as much as for the engines that will answer in our place.
An enterprise AI platform that brings your teams, your knowledge, your memory and your repeatable methods into governed workspaces. It is published by Quaintyx SAS.
A chatbot answers a question then forgets. Mnemetic treats organisational memory and business methods as reusable objects: what your team corrects today applies automatically tomorrow, including for someone who has just arrived.
A workflow declared in advance: defined steps, attached context, an allow-list of tools, an estimated cost and review points. It is neither a prompt nor an autonomous agent deciding on its own what it does.
An object at company level holding facts, preferences and rules drawn from your corrections. It is reusable by several playbooks, shareable read-only, and it survives the deletion of the playbook that fed it.
It runs the declared steps freely, but actions with an external effect sit behind a preview or an approval. A mode lets you see what would be written outside without writing it. The policy is set per use case.
Yes, and per use rather than once for the whole organisation. Conversation, utility tasks and long analysis are resolved separately. You can register your own provider keys, encrypted at rest.
No. What the product calls learning is a mechanism of textual rules extracted from your corrections and re-injected into the instructions. No training and no fine-tuning happens in our code.
Your documents, their search vectors, your file storage, our analytics and our error tracking live on our own infrastructure, with no third-party vendor, and no model is trained on your data. Generating an answer means calling a model provider: you choose which one, you can register your own key, and we publish the list. Where it runs is a deployment option rather than a fixed property of the product: sovereign European hosting is available when you need it, and we discuss your region requirements before you sign. Our data processing agreement is available on request.