xpander.ai

xpander.ai runs on the model and framework you already chose. It works with Claude, GPT, Gemini, Llama, Qwen, Mistral, Deepseek or your own fine-tuned model, and with runtimes like Claude Code, Codex, OpenCode or a harness you wrote yourself. That neutrality keeps the cost of switching models low, which matters when a better option shows up or a vendor changes its terms.

The platform gives operators one place to manage the agent lifecycle, from first launch to fleet operations. An agent registry tracks owners, versions, environments and status, while a connector registry holds approved APIs, tools, MCP connectors and private systems. The core promise is that if a control isn't enforced in the runtime, it isn't treated as a control at all.

Agents run in your AWS, GCP or Azure account, your VPC, on-premises or a fully air-gapped network. Self-hosted deployments egress no operational data, and credentials come from a vault at the moment of use so the model never sees a secret. For teams in regulated fields, this is often the deciding factor.

Omni is an agentic forward-deployed engineer that builds agents from a plain-language description. You tag it like a teammate inside ChatGPT or Claude, ask for the agent you want, and it wires up the tools and hands back something ready to run. It also optimizes, migrates and fixes agents over time, which trims the setup work that usually eats a project's early weeks.

Agents and the people using them share one conversation. A published agent runs self-serve for any authorized team, and work happens where people already are: Slack, Teams, ChatGPT, Claude and the Omni UI. Approvals and handoffs sit in the same thread, so a human can step in without leaving the context.

Every agent has a named identity, and every action is logged with spend attributed per task. Operators set approved models, routing rules, limits and spend caps, and attach human-in-the-loop approvals to sensitive actions. A run trace shows each tool call and what came back, which is what makes an agent reviewable rather than a black box.

A policy engine defines who can build, run, approve, publish and access agents, and it scopes permissions, secrets and data access per agent. The company reports SOC 2 Type II certification and GDPR compliance, with a full audit trail that can be exported to your SIEM. Security teams usually want that in writing, and this platform publishes it rather than describing it.