AI
AI Customer Support System
LLM-powered support automation with instant replies and measurable cost savings.
Problem
The company had too many support tickets and slow response times; agents were overloaded and customers waited hours for first replies.
Solution
We built an AI chatbot integrated with their CRM and ticketing stack, using LLM APIs with retrieval over internal docs and escalation paths to humans when confidence is low.
Process
How we moved from intake to production scale.
- 1
Discovery
Mapped ticket taxonomy, CRM fields, and compliance constraints for automated replies.
- 2
Design
Defined RAG boundaries, citation rules, and human escalation thresholds per intent.
- 3
Build
Shipped orchestration API, vector index pipeline, and operator review console.
- 4
Eval & launch
Regression harness on golden questions; staged rollout with shadow mode.
- 5
Scale
Autoscaling inference, cost dashboards, and continuous eval on new intents.
Before vs after
Positioning snapshot—pair with metrics below for diligence.
| Before | After |
|---|---|
| Manual triage & macros | LLM-grounded replies with citations |
| Hours to first response | Sub-second median first reply |
| High agent load | 70% tier-1 automation with safe handoff |
Architecture (flow)
User → Chat UI → API → AI model → CRM / ticketing → response
Support portal → orchestration API → RAG layer (vector store + policy) → LLM provider → webhook updates to ticket state and analytics warehouse.
Tech stack
Results
Support became largely self-serve at peak times while preserving quality on edge cases.
0%
Tier-1 automation
0%
Cost reduction
<1s
Median first reply
- 70% reduction in repetitive ticket workload
- 3× faster median first response time
- 24/7 coverage for tier-1 questions with human handoff
70% ticket automation · 50% cost reduction · sub-second median first reply
Screenshots
Illustrative product and analytics surfaces—swap for client-branded assets when sharing externally.
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