Your team wastes 9 hours per week searching for information across Slack, Jira, Salesforce, and email. AI agents waste even more.
You have a CRM that does not talk to your project manager. A billing system that does not sync with your support tool. Client context lives in 6 different apps, and every time someone needs the full picture, they spend 20 minutes assembling it manually. AI agents hit the same walls. They cannot automate what they cannot access.
What this replaces
This hub uses Make to listen for webhooks across every tool in your stack, normalize the data into a unified schema, and push it into a single Notion database. When Ultron agents need context, they query one place. When events occur, the dispatcher routes them to the correct agent automatically.
The Stack
The single source of truth. Every task, client, and metric lives here in a highly structured, machine readable format.
Listens for webhooks across all your external apps and standardizes the data before pushing it into Notion.
Monitors the central hub. When a new lead appears, it dispatches the sales agent. When a ticket opens, it dispatches the support agent.
System Architecture
The number one reason AI agent deployments fail is not the model. It is the data architecture. If your agent cannot access the client record, the billing history, and the support ticket in a single query, it will hallucinate or stall. Centralized data is the prerequisite to autonomous operations.
You cannot build an AI workforce on top of a fragmented data layer. Fix the foundation first.