Introducing the new intelligence hub for modern teams
Explore product updates, research stories, technical deep dives, and practical guides from the team building the future of AI workflows.
The intelligence hub is our answer to a simple problem: teams need one place to orchestrate agents, knowledge, and review so AI work feels operational, not experimental.
This article explains what shipped, why the architecture looks the way it does, and how admins should roll it out inside their organization.
A single place for operational AI
Instead of scattering prompts, tools, and logs across disconnected surfaces, the hub centralizes configuration, permissions, and monitoring.
Centralization reduces drift between teams and makes it easier to enforce retention, access, and audit requirements consistently.
Designed for review at scale
High-trust workflows need queues, assignments, and clear diffs between drafts so reviewers can move quickly without guessing what changed.
We optimized for batch operations and keyboard-first flows because review is often the real bottleneck in production.
What admins should configure first
Start with identity, data scopes, and model routing policies. Then define the workflows that require human approval and the evidence rules for customer-facing answers.
Once those foundations are stable, expand templates and automations in measured waves so you can measure impact and catch regressions early.
Roadmap and feedback
We will keep publishing detailed release notes and migration guides as the hub grows.
If you are an existing customer, send your highest-friction workflow to your success contact. It helps us prioritize the next set of primitives we build.