customer_support · travel · workflow
RailYatri modernizes travel platform with Google Cloud, achieving 60% faster provisioning and AI-powered customer service
After the pandemic, RailYatri experienced 15-20% monthly growth that its existing cloud provider could not handle, resulting in unplanned downtime and disproportionate database costs, while its legacy on-prem infrastructure was also too rigid to scale with demand.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Compute Engine infrastructure migration
RailYatri hosts its services, including MySQL and MongoDB, on Compute Engine VMs to establish a stable and scalable backend ensuring 24/7 availability.
Tools used
Compute EngineBigQueryLooker StudioGoogle Speech APIsSpeech-to-Text AIText-to-Speech AIGoogle MapsMySQLMongoDB
Outcome
After migrating to Google Cloud, RailYatri achieved 60% faster server and container provisioning, a 10% increase in holiday bookings via the Advanced Resource Period feature, and significantly improved customer service through Speech-to-Text and Text-to-Speech AI.
What failed first
RailYatri's previous cloud provider was unable to scale with rapid post-pandemic growth, causing unplanned downtime and inflated database costs.
Results
Time saved10%
Volume60% faster
Grounding & classification
Source type: vendor customer story
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sentiment analysisspeech to textvoice aicall recordingmetric backednamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedtravelconversion increasecustomer satisfactioncycle time reductionvendor customer storyback office opscustomer supportdata sync enrichmentmonitor detect alert