> case study
AI Workflow Automation
62%
manual triage reduction
41%
faster task cycle time
29%
fewer missed handoffs
Problem: Operations teams were manually triaging incoming requests, copying data between tools, and chasing approvals over chat. Work frequently stalled between handoffs.
Solution: Built an orchestration flow that auto-categorized incoming requests, enriched records with AI-generated context, routed tasks to the right owners, and posted approval summaries where teams already worked.
Result: The team shipped work faster with fewer coordinator bottlenecks. The process became predictable enough to handle growth without adding headcount for triage.
Before vs After
Before
- Manual ticket triage and reassignment across multiple channels
- No consistent quality checks before handoff to execution teams
- Frequent delays caused by missing context in requests
After
- Automated routing with confidence scoring and fallback rules
- AI-generated summaries attached to each request
- Clear ownership and audit trail for every handoff
Technical Approach
The core challenge was handling inconsistent upstream data formats without requiring constant manual intervention. Classification was done with a structured prompt chain using function calling — output was constrained to a typed schema so downstream routing logic never had to guess intent.
Routing rules were stored as config rather than code, so ops could adjust thresholds without a deploy. The n8n layer handled orchestration across Supabase, OpenAI, and Slack — each node was scoped to one responsibility so failure modes were easy to isolate and retry.
Idempotent execution was enforced at the trigger layer so duplicate webhook deliveries produced no side effects.
Stack Used
Metrics are anonymized from a recent operational workflow engagement.