Shared knowledge base
Hybrid retrieval over your team's documents for every agent in the room.
For teams
AI agents for teams that share your context, not just your prompts
Individual AI subscriptions produce individual results. Everyone builds private prompt libraries, nobody's context is shared, and the same research gets redone four times. Nonilion puts AI agents for teams inside one shared workspace: agents hold assigned areas of ownership, retrieve from the same knowledge base, and post results into rooms the whole team can see.
What are AI agents for teams?
AI agents for teams are shared autonomous assistants that operate on a team's collective context rather than one person's chat history. They hold defined responsibilities, access shared knowledge and tools, and deliver output into a common workspace so the whole team benefits from the same work.
The cost of everyone having their own assistant
When AI usage is private, its value stays private. Context does not compound, quality varies with prompting skill, and there is no institutional record of what the AI concluded or why. Shared agents fix the structural problem, not just the tooling one.
- One knowledge base every agent retrieves from.
- Named agents with clear areas of ownership.
- Output that lands in a room, visible and reviewable.
- A record of decisions that survives staff turnover.
Give each agent a job, not a prompt
Instead of re-explaining context every session, you assign an agent a standing responsibility: research this market, triage this inbox, keep this documentation current, prepare this weekly summary. The agent keeps its context between runs and reports on a rhythm your team sets.
Buy a department, build a specialist, or bring your own
The marketplace offers individual agents and multi-agent department packs you can deploy immediately. If your workflow is unusual, build a custom agent with the tools and knowledge it needs. If you already run agents elsewhere, connect them — Nonilion is designed to host what you bring rather than replace it.
Humans stay in charge of consequential steps
Agents draft, research, and prepare. Anything that sends, spends, publishes, or deletes can require explicit approval. The event log means a manager can review exactly what happened without interrogating whoever ran the prompt.
What you get
Assigned ownership
Agents hold standing responsibilities instead of one-off prompts.
Persistent rooms
Context stays with the space, so nobody re-briefs from scratch.
Department packs
Deploy a coordinated group of agents for a whole function.
Approval workflows
Gate any action that touches money, customers, or production.
Audit trail
A reviewable log of what every agent did and when.
Frequently asked questions
How is this different from everyone using ChatGPT at work?
Individual chat subscriptions keep context, prompts, and results private to each person. Shared agents work from one team knowledge base, hold defined responsibilities, and post output into a common room, so the work compounds instead of being repeated. There is also an audit trail, which private chat history does not give a manager.
How many people do we need for this to be worth it?
The shared-context advantage starts as soon as two people would otherwise duplicate the same research or re-brief the same background. Small teams typically start with one room and two or three agents.
Can we keep our existing tools?
Yes. Most teams keep their core systems and use Nonilion as the live layer where decisions and agent work happen, connecting the tools agents need to act on.
Who can see what an agent produced?
Agent output lands in the room it belongs to, so visibility follows room membership. Sensitive workflows can live in their own room with its own access.
What does it cost to start?
There is a free tier for rooms and basic agent use, and paid plans for heavier workloads. Because the platform is bring-your-own-key, your model spend stays on your own provider accounts at your existing rates.
Related capabilities
An agentic AI platform where agents actually finish the work
An agentic AI platform is software that lets AI agents pursue goals autonomously instead of answering one prompt at a time. It gives agents tools, memory, and permission boundaries so they can plan a task, execute multiple steps, recover from errors, and hand back a finished result.
AI employees that hold a role instead of answering a prompt
AI employees, sometimes called AI coworkers or digital workers, are autonomous agents assigned a standing role rather than one-off tasks. Each has defined responsibilities, access to the tools and knowledge its job requires, and a reporting rhythm — closer to a job description than a prompt.
Multi-agent collaboration with shared context and one audit trail
Multi-agent collaboration is an architecture where several specialized AI agents work on one objective, each handling part of the problem and sharing context with the others. It improves reliability by narrowing each agent's scope and allowing agents to check or build on each other's output.
A multimodal AI workspace that hears, sees, and responds in real time
Multimodal AI describes systems that work across more than one kind of input or output — text, speech, images, screens, and video — in a single reasoning process. Instead of transcribing audio and handing text to a separate model, a multimodal system treats voice, visuals, and text as one connected context.
Create your team room free
Start with one room and a few agents. Add your whole team when it clicks.
