Building an AI platform is hard. We did it for you.
Open source, self-hosted, tested with Fortune 50s.
Everyone gets an AI coworker — not just engineers
Sales, support, accounting, HR: the whole company works in one chat, with agents that reach real corporate systems. SSO once a day, and every action runs under that person’s own identity.
“AI is a democratic tool here. Everyone uses it. If you’re an HR, you should also be able to connect to it and use it.”
The chat window becomes the UI
People package prompts and tools into reusable skills, and agents render live dashboards and mini-apps right inside the conversation. The good ones get reviewed and promoted org-wide.
“Sorry, I just built my first MCP app… it actually renders metrics in the UI.”
Shared context, not sixty private chats
Projects keep the files, agents, and conversations of one piece of work together — including shared chats where a whole team collaborates with the same agent.
“Could we have one chat with the same team members that is also interacting with an agent — bringing in multiple people from that team?”
Someone built a great agent. Now ship it to 1,000 people.
Agents run server-side in sandboxes — scheduled, long-running, safe for many users to execute. Per-user execution and cost limits keep one bad loop from becoming an outage.
“Another team took down Jira for two days because their agent is just beating the crap out of it.”
Hundreds of connectors. One catalog. No sprawl.
Host MCP servers as containers in your own cluster and publish them to a central catalog with an approval flow — instead of everyone wiring up their own connectors with tokens in env vars.
“When any kind of user can start adding MCP, it goes to review, to security, they approve it, and then automatically it’s added for your usage or for team usage.”
Your knowledge answers — permissions included
Connect Confluence, Jira, drives, and internal docs. Source-system permissions survive into the AI layer, so people can only retrieve what they could already see.
“We don’t want to just give people blanket access to ERP data.”
One gateway. Zero key distribution.
One governed endpoint for every model and every MCP server. OAuth in front, virtual keys behind, and every token and tool call attributed to the actual person.
“People are saying, hey, we need to somehow distribute this singular API key to 1,000 people so they can just use this endpoint.”
Zero trust, even for agents
Taint tracking follows sensitive data through the conversation and disables dangerous tools before exfiltration can happen — policies enforced at the proxy, not in a PDF.
“I’m already sweating right now. That’s a dangerous combo, right?”
Every token, tool call, and dollar — attributed
Full traces per user, team, and agent, exported over OpenTelemetry into the Grafana you already run. Budgets and per-user limits catch runaway spend before the invoice does.
“I really liked that thing that shows costs per request. That’s just top-tier.”
Building an AI platform is hard. We did it for you.
Open source, self-hosted, tested with Fortune 50s.
Everyone gets an AI coworker — not just engineers
Sales, support, accounting, HR: the whole company works in one chat, with agents that reach real corporate systems. SSO once a day, and every action runs under that person’s own identity.
“AI is a democratic tool here. Everyone uses it. If you’re an HR, you should also be able to connect to it and use it.”
The chat window becomes the UI
People package prompts and tools into reusable skills, and agents render live dashboards and mini-apps right inside the conversation. The good ones get reviewed and promoted org-wide.
“Sorry, I just built my first MCP app… it actually renders metrics in the UI.”
Shared context, not sixty private chats
Projects keep the files, agents, and conversations of one piece of work together — including shared chats where a whole team collaborates with the same agent.
“Could we have one chat with the same team members that is also interacting with an agent — bringing in multiple people from that team?”
Someone built a great agent. Now ship it to 1,000 people.
Agents run server-side in sandboxes — scheduled, long-running, safe for many users to execute. Per-user execution and cost limits keep one bad loop from becoming an outage.
“Another team took down Jira for two days because their agent is just beating the crap out of it.”
Hundreds of connectors. One catalog. No sprawl.
Host MCP servers as containers in your own cluster and publish them to a central catalog with an approval flow — instead of everyone wiring up their own connectors with tokens in env vars.
“When any kind of user can start adding MCP, it goes to review, to security, they approve it, and then automatically it’s added for your usage or for team usage.”
Your knowledge answers — permissions included
Connect Confluence, Jira, drives, and internal docs. Source-system permissions survive into the AI layer, so people can only retrieve what they could already see.
“We don’t want to just give people blanket access to ERP data.”
One gateway. Zero key distribution.
One governed endpoint for every model and every MCP server. OAuth in front, virtual keys behind, and every token and tool call attributed to the actual person.
“People are saying, hey, we need to somehow distribute this singular API key to 1,000 people so they can just use this endpoint.”
Zero trust, even for agents
Taint tracking follows sensitive data through the conversation and disables dangerous tools before exfiltration can happen — policies enforced at the proxy, not in a PDF.
“I’m already sweating right now. That’s a dangerous combo, right?”
Every token, tool call, and dollar — attributed
Full traces per user, team, and agent, exported over OpenTelemetry into the Grafana you already run. Budgets and per-user limits catch runaway spend before the invoice does.
“I really liked that thing that shows costs per request. That’s just top-tier.”
“We came across Archestra while searching for an infrastructure layer to scale and secure our internal agents. From our very first interactions, the energy, depth of knowledge, and speed of the Archestra team made the potential obvious. Archestra stood out for the team's security-first mindset, its open-source nature, an intuitive UI, and a deployment experience that just works.”
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