Normalized claim
Time: 60% decrease
Vernost reported a 60% reduction in holiday package booking time.
Vernost cuts travel booking time by 60% and speeds support with generative AI
Use case typeCustomer service automationUpdated Jun 13, 2026
Vernost Tech Ventures Pvt. Ltd. built an intelligent customer engagement suite to make travel and holiday booking experiences faster and more personalized for its banking and aviation clients. The company used AWS-hosted generative AI and machine learning services to support customer Q&A, pre-booking recommendations, booking assistance, and post-booking automation across the travel journey.
- Organization
- Vernost Tech Ventures Pvt. Ltd.
- Industry
- Tech & Comms
- Location
- India
- Published
- May 2026
Reported outcomes
−60%
timeTime & speed
15 minutestime+25%time+50%time
Strategic outcomes
New product / capabilityBuilt AI travel booking and support suiteNew product / capabilityAdded automated pre- and post-booking agentsCustomer experience & trustEnabled faster self-service bookingsSpeed & agilityReduced booking and support friction
Catalog median for time & speed deployments: −50% across 312 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Time: 15 minutes increase
Sales conversion time fell to under 15 minutes, contact-center productivity improved by 25%, and customer-service interaction time fell by 50%.
Normalized claim
Time: 25% increase
Sales conversion time fell to under 15 minutes, contact-center productivity improved by 25%, and customer-service interaction time fell by 50%.
Normalized claim
Time: 50% increase
Sales conversion time fell to under 15 minutes, contact-center productivity improved by 25%, and customer-service interaction time fell by 50%.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Vernost Tech Ventures Pvt. Ltd.
- Provider
- AWS
- Maturity
- Unknown
- Linked source
- AWS Customer Stories
No explicit deployment-stage evidence found.
Customer identity supportedSource describes one deploymentMaturity evidence evaluated
Primary read
Use case focus
Showing 3 of 4
- 1Customer Service
- 2Personalization
- 3Workflow Automation
- Vernost wanted to deliver real-time, personalized and automated travel booking and support experiences.
- The company aimed to reduce dependence on human agents while improving booking speed and service quality for enterprise customers.
- Vernost used Amazon Bedrock to build LLM-powered Q&A chatbots for external customers and internal staff.
- It used Amazon SageMaker for machine-learning workloads and Amazon S3 to store chat histories, FAQs, and customer profiles.
- The solution includes pre-booking AI agents for recommendations, booking assistants that apply loyalty rewards and verify booking details, and post-booking agents that handle ticket downloads, changes, and cancellations.
Technologies
- Vernost reported a 60% reduction in holiday package booking time.
- Sales conversion time fell to under 15 minutes, contact-center productivity improved by 25%, and customer-service interaction time fell by 50%.
- Customers can complete bookings faster without needing a human agent.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Primary source available
- Quantified outcome available
- Technical implementation details available
Type: Customer StoryPublished: May 13, 2026Publisher: AWS Customer StoriesEvidence: PrimaryConfidence: High
AI-generated summary. Verify important details with the linked sources before relying on this case.
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