Every AI app needs a control record.
Every app needs policies, owners, approvals, evidence, support, and value in one operating record. Framer uses it. Weaver releases with it. Tracer keeps it current.
Automation without context makes the wrong work faster.
Most teams have operating context scattered across drives, wikis, ticket comments, and people's heads. Policies go stale. Owners stay implicit. Support paths live in chat history. AI tools amplify the confusion.
So we start with a knowledge inventory. We keep your systems and link what matters. Framer, Weaver, and Tracer can query the facts.
One record. Four audiences.
Each layer serves a different reader. The same facts appear at the altitude each role needs.
Same fact, four readers. Marketing for the buyer · Customer for the user · Product for the team · Architecture for the engineer.
What week one produces.
We inventory Notion, Confluence, Google Docs, GitHub, internal wikis, and other systems: what exists, where it lives, and how stale it is.
Each fact is mapped to owners, policies, systems, support paths, and the audience layer that needs it.
Operating context gets wired into Framer, Weaver, and Tracer. When an app needs a policy, owner, or support path, it pulls from the right layer.
Launched apps update the record. Stale entries get flagged. Documentation becomes part of operations.
The record stays current as the work changes.
Policy updates, support notes, owner changes, and runbook edits are drafted in context. AI suggests. A human approves. Every artifact lands in the right layer with provenance attached.
when there's an alert, page the on-call
Page the on-call engineer via PagerDuty (not Slack, alerts shouldn't compete with chat traffic). Include: alert title, runbook URL, severity, and the most recent metric snapshot.
if they don't ack in 10 min, escalate
Escalate to the secondary on-call after 10 minutes without acknowledgement. At 20 minutes, escalate to engineering and open the incident channel.
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Engineering extensions.
Tracer data is queryable through REST on Professional and Enterprise. Framer definitions and Weaver handoffs are webhook-driven and Model Context Protocol-compatible.
REST API
JWT endpoints: outputs, owners, policies, telemetry, support paths, value records.
Webhooks
Push notifications on output changes, anomaly detection, and connector health changes. Verified signatures, retry semantics, replay.
Custom Connectors
Build via Model Context Protocol, same standard our AI-native integrations use. Templates, harness, validation included.
Inventory your operating record.
We will inventory your documentation systems and return a layered map: what to keep, what to connect, what to retire, and which AI workflows need control first. Available for qualified design partners.