Customer support · Production

Kustomer Launches Standalone Enterprise AI Platform to Modernize Existing Helpdesks

The problem

Enterprises deploying AI in customer experience face a false choice between costly system overhauls, disconnected bolt-on AI tools that fragment data and duplicate tickets, or standing still — while existing tools cannot balance predictive flexibility with the deterministic precision compliance workflows require.

First attempt

Existing bolt-on AI add-ons create ticket duplication and messy data, lack sufficient customer context, and cannot unify predictive and deterministic reasoning within a single automation framework.

Workflow diagram · grounded in source
1
Customer inquiry across channels
trigger
“AI agents resolve routine questions instantly across channels using full customer history and real-time context”
2
AI reasoning engine interprets
ai_action
“Predictive Intelligence: Interprets intent, sentiment, natural language, and cross-channel context.”
3
Adaptive or deterministic mode routing
routing
“Our reasoning engine allows businesses to toggle between adaptive and deterministic modes based on specific use cases, ensuring AI interprets nuance where necessary and adheres to strict logic where required”
4
AI autonomous resolution
output
“Within the first quarter, we saw AI handling up to 65 percent of routine inquiries during peak periods”
5
Agent assist for complex cases
human_review
“Agents receive complete customer history, relevant knowledge, and real-time recommendations, reducing research time in-the-moment and increasing confidence during complex interactions”
6
CX insight and optimization loop
feedback_loop
“AI transforms every interaction into insight, surfacing trends, optimizing workflows, generating knowledge, and delivering real-time visibility into performance and sentiment”
Reported outcome

Early customers Aplazo and Goody report measurable gains in automation efficiency and customer satisfaction while maintaining operational continuity, with Aplazo's VP of Customer Experience reporting AI handling up to 65 percent of routine inquiries during peak periods.

Reported metrics
routine inquiries handled by AI at peak65 percent
automation efficiency gains (Aplazo and Goody)measurable gains in automation efficiency
customer satisfaction gains (Aplazo and Goody)measurable gains in customer satisfaction
Agent research timereducing research time in-the-moment
Reported stack
Kustomer AIZendesk
Source
https://www.kustomer.com/resources/pr/ai-standalone/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Early customers Aplazo and Goody report measurable gains in automation efficiency and customer satisfaction while maintaining operational continuity, with Aplazo's VP of Customer Experience reporting AI handling up to…

What tools did this team use?

Kustomer AI, Zendesk.

What results were reported?

routine inquiries handled by AI at peak: 65 percent; automation efficiency gains (Aplazo and Goody): measurable gains in automation efficiency; customer satisfaction gains (Aplazo and Goody): measurable gains in customer satisfaction; Agent research time: reducing research time in-the-moment (source-reported, not independently verified).

What failed first in this deployment?

Existing bolt-on AI add-ons create ticket duplication and messy data, lack sufficient customer context, and cannot unify predictive and deterministic reasoning within a single automation framework.

How is this customer support AI workflow structured?

Customer inquiry across channels → AI reasoning engine interprets → Adaptive or deterministic mode routing → AI autonomous resolution → Agent assist for complex cases → CX insight and optimization loop.

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