Custom AI operations platform
One AI dashboard for service, support and production
A mid-size manufacturer ran production, field service and customer support on three disconnected systems. We built a single AI dashboard that reads from all three, flags issues before customers call, and drafts the responses the team used to write by hand.
- Client
- Precision components manufacturer
- Region
- United States
- Scale
- 350 to 500 employees
- Timeline
- 8 weeks to production, 2 weeks of tuning
What the client got
0%
Less time per support ticket
Agents approve AI drafts instead of researching and writing each reply.
0x
Faster issue detection
Production delays reach customers as a notice, not a complaint.
0 view
Replaced 3 reporting spreadsheets
Management reviews run from one live dashboard with an AI narrative.
The bottlenecks
Bottleneck 1
Three systems, no shared truth
Production ran on an ERP, service on a ticketing tool, support on shared inboxes. A delayed batch would surface as angry emails two days later because nobody connected the dots.
Bottleneck 2
Support answered the same questions every day
Order status, lead times and spec confirmations made up most tickets. Each one meant a support agent opening the ERP, copying numbers, and writing an email.
Bottleneck 3
Managers reported by spreadsheet
Weekly reviews depended on exports stitched together on Friday afternoons. By Monday the numbers were stale and nobody trusted them.
What we built
Step 1
Unified data layer
We connected the ERP, service desk and mailboxes through API orchestrations on n8n, normalising orders, tickets and machine events into one store refreshed every few minutes.
Step 2
AI triage and drafting
Every inbound email and ticket is classified by an LLM, matched to the right order or asset, and answered with a draft that pulls live status from the data layer. Agents approve or edit; they no longer start from a blank page.
Step 3
Proactive alerts
When a production delay touches an open order, the dashboard raises it and drafts the customer notice before the customer notices. Service bookings are suggested automatically for assets due maintenance.
Step 4
Live management view
A single screen shows throughput, open tickets by cause, service backlog and at-risk orders, with an AI-written weekly narrative replacing the Friday spreadsheet.
The automation workflow
Service and support automation flow
From inbound event to approved customer response
Questions clients ask about this engagement
Does the AI send emails to customers on its own?
No. Every customer-facing message is drafted by AI and approved by a person. Internal alerts and dashboard updates are fully automatic.
What happens when the ERP changes?
The data layer sits between the dashboard and the source systems, so a new ERP or ticket tool means updating one connector rather than rebuilding the platform.
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Have a similar bottleneck?
Tell us how the work happens today. We will show you the workflow we would build and prototype it on your own process.