Layer 1 — Smart intake
We begin with a shared inbox, IVR, or chat widget. LLMs handle classification, entity capture, and summarization; deterministic regex and validation keep the output predictable. Every intake packet includes:
- Requester, account, or asset IDs pulled from CRM and data warehouses.
- Priority labels tied to SLAs.
- Suggested next actions (draft replies, tasks, or tickets).
Layer 2 — Workflow router
Once intake is structured, we hand it to a router built in Zapier, Make, n8n, or a bespoke Node service. The router only cares about what outcome is needed and which system owns it. Common routes:
- Deal support → Salesforce tasks with auto-filled context.
- IT request → Linear or Jira with AI-written repro steps.
- Scheduling request → Calendly + SMS confirmations with human fallback.
Layer 3 — Human confirmation
We never remove humans; we just give them better leverage. Approval steps land in Slack, Teams, or email with one-click buttons. We track human overrides so the router learns when to escalate sooner.
Implementation rhythm
- Map the top 5 intake sources and destinations.
- Document the “happy path” and edge cases per workflow.
- Launch in sandbox with telemetry (success rate, human overrides, SLAs).
- Roll out to production users with training and weekly retros.
The end state feels invisible: customers still email or call, teams still work in their existing tools, and the AI quietly triages everything in between.