customer_support · services · workflow
eSky scales AI customer service across 3 brands and 3 channels with Ada, achieving a 17-point automated resolution increase and 200% ROI
eSky's flow-based chatbots failed to satisfy customers, who sought human agents rather than trusting automation to resolve their issues, while contact volumes spiked dramatically during irregular travel operations and the company lacked an operating model to scale service quality across multiple brands, channels, and markets.
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 · Customer contacts via preferred channel
Customers initiate contact via the communication tool they normally use.
Tools used
AdaAda's MCP ServerWhatsAppMessenger
Outcome
eSky achieved a 17-point increase in automated resolution rate in four months, 200% ROI, and went live with AI customer service across three channels and three distinct brands managed by one team.
What failed first
eSky's prior flow-based chatbot approach managed inquiry volume by deflecting tickets rather than resolving them, leaving customers frustrated and seeking human agents instead of trusting the chatbot.
Results
Time savedreduces average handle time
Grounding & classification
Source type: vendor customer story
23 fields verified against source quotes, 5 dropped as unverifiable.
ai agentconversational aitranslationchat transcriptknowledge basefailure mode describedhuman review describednamed customerproduction runtime claimedsource backedtools describedworkflow describedtravelautomation ratecost reductionemployee productivityresolution time reductionvendor customer storycustomer supportticket triageautonomous resolutionescalation workflow