Multi-Channel Retailer - AI Support Automation
Deployed an AI support chatbot to absorb routine order and shipping questions.
Reviewed by Amit Sharma, Founder & IT Head· Content reviewed Sep 2026
The background
Order-status and shipping questions made up most of the support queue, arriving faster than the team could clear them during peak periods. We built and trained a chatbot on the brand's own policies and order data, with human handoff for anything it couldn't resolve confidently.
As with every engagement, the work started with a specific, checkable problem rather than a general goal - the numbers below are what changed once that problem was actually fixed, not a summary of activity along the way.
Results
62% of incoming tickets deflected without a human reply
First-response time cut from 6 hours to under 2 minutes
Support headcount held flat through a 40% order-volume peak
Client-identifiable details are anonymized under NDA; figures reflect this specific engagement and aren't a projection for future clients. See every measured result.
How we got there
01
Diagnose
We start from the account, catalog or workflow as it actually is, not a generic checklist - identifying the specific, checkable problem before proposing a fix.
02
Execute
The same team that ran the diagnosis does the work, so nothing gets lost translating a plan from one team to another.
03
Report
Progress is reported against the original problem, on a schedule agreed up front, so a result is confirmed rather than assumed.
REVIEWED FOR ACCURACY BY
Amit Sharma Founder & IT Head, eData4You
Figures and process details on this page last verified Sep 2026
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