Sources stay authoritative
Atlas makes them discoverable without creating a second truth.
n3tz Atlas · Pilot programme
Make your ChatGPT fit for your company.
Atlas connects knowledge, decisions and roles into one operating memory. ChatGPT, Claude and specialised agents use it. New insight returns as a reviewable proposal instead of disappearing into the next chat.
No self-service promise: we start with one clearly bounded knowledge or role workflow.
Status before answers
Approved, proposed, outdated or restricted remains visible.
Decisions become steering
Approved insight improves the next agent run.
The problem is not another chat
Your AI is smart. It still does not know your company.
Knowledge lives in documents, tickets, people and past decisions. Individual assistants receive fragments. What is missing is one shared, steerable company line.
Prompt islands
Everyone builds an assistant. Roles, rules and voice quietly drift apart.
Results without authority
Finding a source does not tell you whether it is current, approved or responsible.
Learning without memory
A good decision stays in chat and is missing from tomorrow’s person or agent.
The difference
Do not only know what is true. Know what applies.
Search finds information. Agents execute tasks. Atlas holds the company line between them: source, validity, role, decision and learning loop.
Traditional knowledge base
Collect documents
A good place to write and find. Responsibility for currency and use often stays implicit.
Enterprise search & agent builder
Connect information
Searches across systems and builds agents. Cross-role company steering is often configured per platform.
n3tz Atlas
Govern knowledge
Records which source owns a fact, what was approved, who may apply it and how decisions become precedents.
An open memory instead of platform lock-in.
The company line remains a readable, versioned knowledge model. Atlas delivers it through one shared interface to the AI tools your team already uses.
Readable and versioned
Knowledge, relationships and changes remain traceable.
Client-neutral
ChatGPT, Claude and other MCP-capable agents use the same company line.
Proposal, not silent mutation
Agents propose knowledge. Approval turns it into company steering.
How Atlas works
One control loop for people, knowledge and agents.
- 01
Map sources
We clarify which system remains responsible for rules, work, files and decisions.
- 02
Model the line
Knowledge gains relationships, owners, status, roles and visible boundaries.
- 03
Connect agents
Employees ask in their familiar AI tool or use specialised role agents.
- 04
Learn together
New decisions return as proposals, are reviewed, then become available to every authorised agent.
Many agents. One constitution.
Not a free-running swarm, but a guided AI team.
Atlas gives every agent a mission, permitted sources, tools, hand-offs and stops. A coordinator routes work. People retain approvals and sensitive decisions.
Executive office agent
Condenses, prioritises and surfaces only real decisions.
Compliance agent
Checks rules and stops on conflict or missing evidence.
Domain agent
Works with the knowledge, tools and limits of its role.
Coordinator
Hands off clearly instead of losing context across parallel chats.
From executive office to reusable system
The key lesson from a real executive-office prototype.
An executive office needs more than a good answer. It needs roles, visibility boundaries, decision briefs, open loops and a personal line that becomes a rule only after approval. Atlas turns those parts into reusable primitives.
The current prototype uses synthetic demo data only. Production model, document and permission integration belongs to the pilot path and is not presented as already complete.
01
Personal line
A leader’s decisions remain distinct from general company knowledge.
02
Roles, not one chat
Employees, assistants, domain staff and leadership see different work and boundaries.
03
Decision becomes precedent
Approved decisions ground the next comparable case.
Where Atlas starts
Start where knowledge currently depends on individual people.
Leadership & executive office
Keep briefings, approvals, ownership and open decisions together.
Product, operations & advisory
Make decisions, standards and project history useful across teams and agents.
Regulated functions
Model sources, status, visibility and human approval from the beginning.
Optional for Griffi · on request
When Griffi needs deeper company knowledge, Atlas extends its knowledge layer.
Atlas is natively available as an external knowledge module for Griffi on request. During the pilot, we define sources, roles, permissions and the exact knowledge flow together — with no self-service and no public package price.
- Griffi’s existing knowledge base remains the starting point
- Only explicitly approved knowledge reaches Griffi
- Setup and scope are defined together during the pilot
Frequently asked questions
What Atlas is and deliberately is not.
Can Atlas extend Griffi’s knowledge base?
Yes. During the pilot, Atlas is available on request as an optional external knowledge module for Griffi. There is no self-service, automatic activation or publicly listed price yet.
Is Atlas just another knowledge base?
No. Existing systems remain responsible. Atlas models their relationships, validity and roles, then provides governed context to people and agents.
How is it different from Glean, Dust or Copilot Studio?
Those are strong platforms for search, agent building or orchestration. Atlas focuses on the open, versioned company line between sources and any AI client: what applies, who may use it and how a decision becomes a reviewed precedent.
Does everyone need to learn a new chat tool?
That is not the goal. Atlas can connect to existing AI clients through MCP. Dedicated workspaces can still make sense for specialised roles.
Do agents talk to each other autonomously?
Not without control. Tasks, sources, tools, hand-offs and stops are defined explicitly. Critical approvals and sensitive decisions stay with people.
Where does company data live?
Responsible sources remain in place. The pilot defines exactly what Atlas reads, which metadata it stores, and which models participate in a concrete data-flow and access architecture.
What does Atlas cost?
There is no public standard price yet. We first test a model combining implementation, platform operation and actual agent use instead of publishing an arbitrary per-seat number.
Your agents need more than data. They need one shared line.
Bring one real knowledge or decision workflow. We will assess whether Atlas can carry it meaningfully.
Request an Atlas pilot