THE SITUATION
The client operated a large Salesforce ecosystem supporting billing and CPQ processes across thousands of tenants. As transaction volumes increased and tenant expansion continued, production support demands began to rise significantly.
This growing complexity started to impact core operations. Incident volumes increased, resolution times were affected, and billing discrepancies began to influence revenue reliability.
The system was processing approximately $24.5M in monthly invoicing across more than 7,800 tenants and around 20,000 invoices per release cycle. At this scale, small inefficiencies translated directly into billing and operational issues.
THE SOLUTION
GrowthArc partnered with the client to stabilize production support and improve operational efficiency.
The approach focused on reducing recurring incidents through pattern identification and root cause analysis, while strengthening overall support processes. AI-powered tools, including Rovo and in-house accelerators, were used to enable faster triaging, improve resolution consistency, and reduce manual effort.
WHAT WE BUILT
A structured, AI-enabled production support model designed to improve stability at scale.
This included capabilities to identify recurring incident patterns and automate root cause analysis, enabling faster and more consistent triaging. Monitoring scripts and automated validation checks were implemented to detect anomalies early and reduce downstream impact.
The support model was refined with clearer prioritization and stronger collaboration with engineering teams. Delivered approximately 85 short-term fixes and 140 long-term fixes to address recurring and systemic issues.
A governance layer was established through real-time dashboards tracking SLA adherence, ticket trends, and operational performance, enabling continuous improvement and better decision-making.
THE OUTCOME
The production environment saw a clear downward trend in incident inflow, along with improvements in mean time to resolution. Billing accuracy improved to 99.80%, with duplicate billing accuracy reaching 99.99%. Missed billing instances were significantly reduced, contributing to more reliable billing operations. On-time invoicing improved from 79% to 99%, supporting consistent processing of high-volume transactions across the ecosystem. Overall, the result was a more stable and efficient production support model, better aligned to the scale and complexity of the client’s operations.
99%
On-time invoicing
Improved from 79% across a high-volume billing system
99.80%
Billing accuracy
With overall 99.99% duplicate billing accuracy
$24.5M
Monthly invoicing processed
Across 7,800+ tenants and ~20,000 invoices per cycle
FUTURE OUTLOOK
The improvements establish a more stable foundation for ongoing operations at scale.
The structured support model and governance mechanisms provide a basis for sustaining performance as transaction volumes continue to grow, while maintaining consistency across billing and production support processes.
