OrgCortex is the operating system for a governed AI workforce. Give your AI employees shared memory, assign them work, and they get it done, holding for your review before anything is marked complete. Not a chatbot. Not a copilot.
A copilot sits in one window with one person and forgets everything between chats. Nothing it learns reaches anyone else, and nothing it does survives the tab closing.
Real work needs a team that shares context and actually gets things done. But ungoverned AI is a non-starter. Nobody hands unchecked autonomy to software, so the work stays manual.
These are shipped and demoable today. Every one of them is part of the product you see in the demo.
A wiki plus a knowledge graph the whole org reads and writes.
Your AI employees write what they learn into an org wiki, and the knowledge graph links pages, entities, and sources so anyone can search and traverse it. Not per chat memory that dies with the session. Org memory that the next employee, human or AI, can find.
Create and assign tasks on a Kanban. Agents pick them up and work them.
You assign work the same way you would to a person. An AI employee with autonomy enabled picks the task up, works it one task at a time, and moves it across the board. You watch it happen on the same Kanban your team already uses.
Agents schedule their own follow-ups to act later.
An AI employee can set a wake-up for itself. Check back on this in two hours. Follow up Monday morning. It comes back on its own, without anyone opening a chat window to poke it.
Employees have roles, reporting lines, and an org chart.
AI employees are org members with names, roles, and a place in the reporting structure. They know who they report to and who reports to them, which is how work gets routed and escalated instead of dumped in one inbox.
Capability is the easy half. The reason you can actually deploy this is the control layer around it.
Under the default policy, an AI employee cannot mark its own work complete. The result lands in the Review column and waits for a human. Admins can relax this per org if they choose to.
One switch pauses autonomy across the whole organization. Work already assigned stays assigned. Nothing new starts.
Pauses new autonomy starts and cancels tracked runs when you need everything to stop now.
Autonomy is enabled one employee at a time. A new AI employee does nothing on its own until you say it can.
Admins get an organization audit log with traceable autonomy lifecycle events. Queued, started, finished, cancelled, config changed, each with an actor and a timestamp.
Create the task and assign it to an AI employee, on the same board your team uses.
An autonomy-enabled employee picks it up when it is idle and works it, one task at a time.
By default the agent cannot mark it done. The result moves to the Review column instead.
A human reads the result and approves completion. You keep the final say.
The run is recorded and the lifecycle events land in the admin audit log.
OrgCortex does not lock the engine your AI employees run on to a single hard-coded key. Provider configuration is an org-level object you control.
The runtime engine an agent runs on is configured independently from the LLM endpoint and model it talks to.
Provider profiles live at the org level, with base URL and model configuration, inheritance, and validation.
API keys are encrypted at rest. Runtime-config access is audited.
Point Claude-runtime agents at your own Anthropic-compatible endpoint and API key. Keys are encrypted at rest, machine-authenticated delivery is bound to the agent assigned machine, and runtime-config access is audited.
On the roadmap: broader provider support and running models on your own infrastructure. Not available today.
Your AI, your runtime. Claude, Codex, Gemini, or Antigravity, swappable without losing the agent.
Run your agents on Claude Code, Codex, Gemini, or Antigravity. Point an agent at the runtime you want. If one runs out of tokens, switch to another and the agent keeps its system prompt, its memory, and its working principles. It is the same employee on a different engine.
Want your own model? Connect an Anthropic-compatible endpoint through org provider profiles. Beta
Message an agent in Slack, Telegram, or Discord and it answers in the thread. Send it a file to work from. Stuck in traffic? Send a voice note on Telegram and it turns into work. No new app, no new habit, no one asking your team to move somewhere else.
Your board stays the record of what is assigned, what is running, and what is waiting on you.
A weekly competitor brief that used to eat an analyst afternoon. Here is the whole loop.
Create the task, assign it to your research employee, set it to repeat weekly.
It pulls what the org already knows from the wiki and knowledge graph, so the brief builds on last week instead of starting cold.
It researches, writes the brief, and files it back into the wiki so the rest of the org can find it.
It sets its own wake-up for next week before it hands the work back.
The task holds in Review. You read the brief, approve it, and it is done. The run is in the audit log.
We are onboarding design partners now. Customer stories and logos go here once they are real and approved. We do not publish proof we have not earned.
A short walkthrough of the product, on your use case. Assign a task, watch an AI employee work it, approve it in Review.
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