Case study · BPO · Pune

Mid-size BPO
Deployed AI agents to automate tier-1 ticket triage. Saved 14,000 agent hours/month at 99.4% accuracy.

Built a custom Claude + RAG-powered triage system that auto-resolves tier-1 tickets, escalates edge cases with full context, and learns from agent corrections weekly. Integrated with their existing Freshdesk and Slack.

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14K hrs

Saved monthly

99.4%

AI accuracy

6 wks

To zero backlog

−28 pts

Attrition drop

The challenge

A 2,500-seat BPO faced rising attrition and a tier-1 ticket backlog growing 8% MoM. 73% of tickets were variations of 12 known patterns but were being handled manually.

The solution

Outcomes

14,000 agent hours/month saved
99.4% accuracy on auto-resolved tickets
Backlog shrunk to 0 within 6 weeks
Tier-1 attrition dropped from 47% to 19%

Architecture

Architecture diagramHigh-level system diagram — available on request.

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Straight answers

Mid-size BPO — the details

What challenge did Mid-size BPO face?

A 2,500-seat BPO faced rising attrition and a tier-1 ticket backlog growing 8% MoM. 73% of tickets were variations of 12 known patterns but were being handled manually.

How did Albero Technologies solve it?

What were the measurable results?

14,000 agent hours/month saved 99.4% accuracy on auto-resolved tickets Backlog shrunk to 0 within 6 weeks Tier-1 attrition dropped from 47% to 19%

What technology stack was used?

The solution was built with Claude Sonnet, Pinecone, Python, FastAPI, Freshdesk API, AWS.