Rep throughput
4×
Reps send drafts, not write from scratch.
Use case · AI Automations·AI Automations
Tickets triaged before your team logs in.
Inbound tickets categorised, prioritised, pre-drafted with cited KB snippets, and routed to the right person — within seconds of arrival.
- 4×
- Throughput
- 92%
- Tag accuracy
- 15hrs
- Saved / week
Setup in 1–2 weeks · Production-ready · Fully observable
weblink.builds · production
412 tickets today · all systems green
automation · support-triage-v3
live · 412 tickets today
Inputs · sources
Email · support@187
Intercom chat142
Zendesk tickets68
WhatsApp Business15
running
Triage Engine
Claude + classification
Outputs · destinations
Auto-resolve12s
Drafted reply · agent review18s
Slack · escalations22s
CSAT survey · post-resolveafter close
recent activity · last 5 minutes
- 4 sec ago✓ Auto-resolved · password reset · Acme Corp · CSAT pending
- 23 sec ago✓ Drafted reply · billing question · routed to Arjun · 94% confidence
- 1 min ago⚠ Low confidence (38%) · escalated to Priya · refund dispute
- 2 min ago✓ Categorized · feature request · added to product backlog · Linear synced
- 3 min ago✓ Auto-resolved · shipping status · order #ML-4271 · tracking link sent
showing latest 5 of 412 tickets today
The problem
Support queues are drowning every Monday.
Mondays bring 60% of weekly tickets. Most are repeat questions answerable from your knowledge base — but they sit in the queue until a human reads them.
By the time tier-1 reps even open a ticket, the customer has emailed twice, tweeted once, and started writing a review. Speed matters more than completeness.
What manual handling looks like
Form submission
Email to ops@
Spreadsheet check
Slack tag rep
~ 2–4 hours typical · sometimes days
The solution
AI triage that beats your queue.
Inbound tickets are categorised, prioritised, and pre-drafted with KB-cited responses within seconds of arrival. Reps review and send — they don't research and type. Low-confidence cases escalate automatically.
How it works
- 01
Categorise
Classify ticket against your taxonomy — billing, bug, refund, etc.
- 02
Prioritise
Score by SLA, customer tier, and sentiment.
- 03
Draft
Pre-draft a reply with cited KB snippets for the rep to review.
- 04
Route & escalate
Assign to the right tier; escalate low-confidence to humans.
The outcome
What this looks like in production.
Tag accuracy
92%
On a 12-class taxonomy.
Median first-response
<30s
AI drafts in seconds; humans send in seconds.
Hours saved / week
15hrs
For a 3-rep tier-1 team.
What it costs
Transparent pricing. Outcome-tied.
Tier
Support Triage Automation
Engagement
From ₹1.25L
2–3 weeks setup
Optional retainer
₹40k/mo
monitoring & adjustments
Included
- Custom ticket taxonomy + classifier
- KB-grounded reply drafting with citations
- Confidence-based escalation rules
- Integration with Zendesk, Intercom, or Help Scout
- Slack alerts for high-priority tickets
- Dashboards for accuracy and rep adoption
- 30 days of post-launch monitoring
Tech stack
Built on production-grade tools.
Automation Layer
n8nCustom Node
AI Layer
ClaudeGPT-4Embeddings + pgvector
Helpdesk
ZendeskIntercomHelp Scout
Notification
Slack
Stack varies based on your existing tools — we work with what you have.
Variations
Other ways we build this.
Lite
Tagging + priority only. No drafting. Good for small teams.
Standard
Full triage with KB-cited drafts. Most common.
Enterprise
Multi-language, multi-product, custom escalation matrices.
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