E-Commerce
Support ticket deflection rate achieved
CSAT score maintained (out of 5), unchanged from pre-launch baseline
About
Noon's ticket volume was rising 40% year over year, and scripted responses varied wildly by agent while support OPEX climbed toward ₹8 crore. We built a retrieval-augmented AI agent over Noon's full support knowledge base that triages sentiment, resolves routine queries end to end, and hands off complex cases to a human agent with full context already attached.
Industry
E-Commerce
Company size
5,000 – 10,000 employees
Founded
2017
The Company
An e-commerce marketplace scaling faster than its support desk
Noon runs one of the region's largest e-commerce marketplaces, spanning retail, grocery, and quick commerce across a fast-growing customer base. Support ticket volume rose roughly 40% year over year as the platform scaled, driven by order tracking, returns, and payment queries across a widening product and seller base.
Support was staffed and scripted the traditional way — tiered agent teams working from FAQ scripts — which meant answer quality and tone varied by whoever picked up the ticket, and OPEX scaled roughly linearly with ticket volume rather than sub-linearly.
The challenge
Scripted answers could not keep pace with rising volume
With ticket volume outpacing headcount growth, average resolution times were creeping upward and first-response quality was inconsistent — the same question could get a materially different answer depending on which agent, and which script version, handled it.
Complex or high-sentiment tickets — an angry customer over a failed refund, a seller dispute — needed to be identified and escalated quickly, but the triage process itself relied on agents self-flagging rather than any systematic sentiment detection, so escalations were frequently late.
The Solution
A RAG pipeline over 60,000 knowledge-base documents
We built a retrieval-augmented generation pipeline over Noon's full support knowledge base — more than 60,000 documents spanning policy, product, and seller-operations content — powering a GPT-4 agent that answers routine queries directly, cites the underlying policy it drew on, and asks a clarifying follow-up rather than guessing when a query is ambiguous.
A sentiment-triage layer runs on every incoming ticket, scoring urgency and emotional tone so high-risk cases are escalated to a human agent immediately, with the full conversation context and a suggested resolution path already attached rather than starting the handoff from scratch. The whole pipeline is instrumented with Prometheus for real-time monitoring of deflection rate, response latency, and escalation accuracy.
The Results
38% ticket deflection with CSAT held steady
Ticket deflection reached 38% — more than a third of all incoming support volume now resolves without a human agent touching it — while CSAT held at 4.4, meaning the deflection did not come at the cost of customer satisfaction. The combination unlocked an estimated ₹16 crore in five-year NPV through reduced support headcount growth relative to ticket volume growth.
Sentiment-based triage meant high-risk tickets reached a human agent with full context and urgency already flagged, cutting escalation lag and giving agents a materially better starting point on the cases that most needed a human touch.
The agent didn't just cut our costs — it made our escalations better, because the tickets that do reach a human now arrive with the full picture already attached.
Head of Customer Experience, Noon
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