How Capillary Automated Multi-Product Support | Beacon.li

How Does a Global Loyalty Leader Manage Complex, Multi-Product Support?

How Darwinbox Achieved 85% Faster Onboarding with Beacon.li

Capillary, the global leader powering next-generation loyalty programs, has redefined its support experience with Beacon’s Customer360 orchestration agent.

By automating its complete support lifecycle, Customer360 now intelligently handles every stage of ticket management across Loyalty, Engage, and Member Care, transforming hours of manual effort into instant resolution.

95%

SLA compliance maintained consistently

SLA compliance maintained consistently

SLA compliance maintained consistently

47%

47%

47%

faster overall Time to Resolution (TTR)

faster overall Time to Resolution (TTR)

faster overall Time to Resolution (TTR)

Six critical bottlenecks slowing down Capillary's support operations:

Six critical bottlenecks slowing down Capillary's support operations:

Six critical bottlenecks slowing down Capillary's support operations:

High Ticket Volume & Manual Resolution
Thousands of client tickets every month required repetitive analysis, configuration reviews, and multi-team coordination between support, product, and engineering.

Fragmented Knowledge Access
Critical product information, configurations, and playbooks were distributed across disconnected systems, slowing down triage and extending turnaround times.

Escalation Overload
Due to limited automation and contextual visibility, many simple issues were unnecessarily escalated to engineering.

Inconsistent SLA Adherence
Manual triaging and slow root-cause identification made it challenging to maintain SLA targets.

Manual Ticket Investigation
Even routine issues required agents to manually verify tenant setups, data flows, or configuration mismatches.

Dependency on Multiple Teams
Simple issues often depended on other internal teams for inputs, creating delays and inefficiencies.

High Ticket Volume & Manual Resolution
Thousands of client tickets every month required repetitive analysis, configuration reviews, and multi-team coordination between support, product, and engineering.

Fragmented Knowledge Access
Critical product information, configurations, and playbooks were distributed across disconnected systems, slowing down triage and extending turnaround times.

Escalation Overload
Due to limited automation and contextual visibility, many simple issues were unnecessarily escalated to engineering.

Inconsistent SLA Adherence
Manual triaging and slow root-cause identification made it challenging to maintain SLA targets.

Manual Ticket Investigation
Even routine issues required agents to manually verify tenant setups, data flows, or configuration mismatches.

Dependency on Multiple Teams
Simple issues often depended on other internal teams for inputs, creating delays and inefficiencies.

High Ticket Volume & Manual Resolution
Thousands of client tickets every month required repetitive analysis, configuration reviews, and multi-team coordination between support, product, and engineering.

Fragmented Knowledge Access
Critical product information, configurations, and playbooks were distributed across disconnected systems, slowing down triage and extending turnaround times.

Escalation Overload
Due to limited automation and contextual visibility, many simple issues were unnecessarily escalated to engineering.

Inconsistent SLA Adherence
Manual triaging and slow root-cause identification made it challenging to maintain SLA targets.

Manual Ticket Investigation
Even routine issues required agents to manually verify tenant setups, data flows, or configuration mismatches.

Dependency on Multiple Teams
Simple issues often depended on other internal teams for inputs, creating delays and inefficiencies.

A single orchestration agent that sees everything, learns continuously, and acts instantly

A single orchestration agent that sees everything, learns continuously, and acts instantly

A single orchestration agent that sees everything, learns continuously, and acts instantly

Capillary deployed Customer360, an intelligent orchestration agent designed to automate and streamline the entire support process with precision and speed, from ticket ingestion to resolution

AI-Driven Ticket Triage

Automatically classifies, prioritizes, and routes tickets from ServiceNow based on module, severity, and SLA tier.

Enriches every ticket with product, tenant, and integration metadata for instant context.

Autonomous Troubleshooting

Runs AI-driven diagnostics trained on Loyalty, Engage, and Member Care knowledge models.

Detects known errors, configuration gaps, or API discrepancies and applies automated remediation wherever possible.

Automated Engineering Handoff

For tickets beyond automation thresholds, Customer360 creates Jira issues enriched with diagnostic traces, reproduction steps, and next-step suggestions to ensure engineering receives complete context with zero manual intervention.

Continuous Learning

Every resolved case feeds back into the AI model, improving pattern recognition, prediction accuracy, and response confidence over time.

AI-Driven Ticket Triage

Automatically classifies, prioritizes, and routes tickets from ServiceNow based on module, severity, and SLA tier.

Enriches every ticket with product, tenant, and integration metadata for instant context.

Autonomous Troubleshooting

Runs AI-driven diagnostics trained on Loyalty, Engage, and Member Care knowledge models.

Detects known errors, configuration gaps, or API discrepancies and applies automated remediation wherever possible.

Automated Engineering Handoff

For tickets beyond automation thresholds, Customer360 creates Jira issues enriched with diagnostic traces, reproduction steps, and next-step suggestions to ensure engineering receives complete context with zero manual intervention.

Continuous Learning

Every resolved case feeds back into the AI model, improving pattern recognition, prediction accuracy, and response confidence over time.

AI-Driven Ticket Triage

Automatically classifies, prioritizes, and routes tickets from ServiceNow based on module, severity, and SLA tier.

Enriches every ticket with product, tenant, and integration metadata for instant context.

Autonomous Troubleshooting

Runs AI-driven diagnostics trained on Loyalty, Engage, and Member Care knowledge models.

Detects known errors, configuration gaps, or API discrepancies and applies automated remediation wherever possible.

Automated Engineering Handoff

For tickets beyond automation thresholds, Customer360 creates Jira issues enriched with diagnostic traces, reproduction steps, and next-step suggestions to ensure engineering receives complete context with zero manual intervention.

Continuous Learning

Every resolved case feeds back into the AI model, improving pattern recognition, prediction accuracy, and response confidence over time.

Five strategic steps to autonomous support

Mapped End-to-End Support Lifecycle
Analyzed ticket flows across support, product, and engineering to pinpoint automation opportunities.

Trained Domain-Aware Models
Built AI models trained on Capillary’s product logic, configuration hierarchies, and historical resolution data.
Embedded Orchestration Across Systems
Integrated with ServiceNow, Jira, and internal APIs to enable automation across the entire ticket lifecycle.
Set Up Intelligent Confidence Thresholds
Defined when Customer360 should self-resolve versus escalate, ensuring both safety and precision.
Continuous Learning Feedback LoopEnabled a live feedback mechanism so every resolution continuously improves the AI’s diagnostic intelligence.

Impact

Beacon’s orchestration of Customer360 has changed how Capillary’s support operates to act faster, resolve smarter, and remove the everyday bottlenecks that once slowed their global operations.

Metric

Metric

Before Beacon

Before Beacon

With AI Orchestration

With AI Orchestration

Recurring Tickets

Time to Resolution

Engineering Escalations

SLA Adherence

High manual investigation effort

Slower due to repetitive diagnostics

Frequent escalations

Inconsistent across complexity

85% handled autonomously

47% faster TTR

50% fewer escalations

>95% SLA compliance

Metric

Recurring Tickets

Time to Resolution

Engineering Escalations

SLA Adherence

Before Beacon

High manual investigation effort

Slower due to repetitive diagnostics

Frequent escalations

Inconsistent across complexity

With AI Orchestration

85% handled autonomously

47% faster TTR

50% fewer escalations

>95% SLA compliance

Drive faster time-to-value across every client onboarding with Beacon’s AI orchestration

Book a Demo

Drive faster time-to-value across every client onboarding with Beacon’s AI orchestration

Book a Demo

Drive faster time-to-value across every client onboarding with Beacon’s AI orchestration

Book a Demo