Reducing Salesforce Support Cost With AI Automation

A leading US food and retail delivery platform faced expensive, inconsistent Salesforce support with frequent SLA breaches. Myridius delivered an AI-driven support transformation combining GenAI ticket intelligence, workforce optimization, and autonomous bots, cutting support cost by 30 percent and improving resolution speed by 47 percent.

Key Outcomes

• A 47% improvement in resolution time.
• A 30% reduction in support cost.
• 24x7 optimized coverage with 95% SLA compliance.

Overview

A leading US food and retail delivery platform ran expensive, inconsistent Salesforce support under an incumbent vendor model. Ticket resolution times varied, service levels were repeatedly breached, and a fourteen-member team was not aligned to ticket complexity. Leadership needed lower support cost, stabilized service levels, right-skilled coverage, and data-driven insight into ticket patterns. Myridius delivered an AI-driven transformation that combined GenAI ticket intelligence, intelligent team configuration, autonomous bots, and AI-accelerated development. As a result, resolution time improved by forty-seven percent, support cost fell by thirty percent, and the platform achieved continuous coverage with ninety-five percent service-level compliance on a streamlined team.

Client Context

The client is a leading US food and retail delivery platform whose Salesforce environment underpins critical support operations. Reliable, cost-effective support directly affects both the businesses on the platform and internal teams that depend on Salesforce.

Cost and consistency mattered here because the incumbent model was expensive and unpredictable, with service levels breached and a team structure that did not match the work. What was at stake operationally was the ability to deliver dependable support at a sustainable cost while gaining visibility into the patterns driving ticket volume, all of which influence the platform’s efficiency and reliability.

The Challenge

Support operations were expensive and inconsistent under an incumbent vendor model. Ticket resolution times varied widely, service-level agreements were repeatedly breached, and a fourteen-member team, two onshore and twelve offshore, was not optimally aligned to ticket complexity.

Consider a routine week of Salesforce admin tickets. Without intelligence about which issues were recurring or where complexity truly sat, staffing and routing were guesswork, simple tickets consumed senior capacity, and complex issues waited. Leadership needed lower total support cost, stabilized service levels, right-skilled around-the-clock coverage, and data-driven insight into ticket patterns, which created clear urgency for a smarter operating model.

Transformation Goals

The engagement pursued three north stars that connected cost reduction to service quality and operational control across the support function.

• Cost and Quality for Cost Reduction: Reduce total cost of support while improving service quality and consistency across all ticket types.

• Stability for Operational Control: Stabilize service levels and accelerate time to resolution with a streamlined operating model.

• Coverage and Insight for AI Readiness: Deliver continuous optimized coverage and analytics-driven visibility into ticket patterns.

The Solution

Myridius delivered an AI-driven Salesforce support transformation by combining GenAI ticket intelligence, workforce optimization, autonomous bots, and AI-accelerated development. Rather than simply staffing a support desk, the team orchestrated an intelligence-led operating model, embedded automation into routine work, and reimagined how the support organization aligned people to demand. The progression moved from deploying ticket intelligence, to embedding automation and AI development velocity, to reimagining the coverage model.

• Orchestrated the foundation: Applied GenAI to more than forty-five thousand historical tickets to categorize issues, detect trends, and surface root-cause insight that informed the entire operating model.

• Embedded intelligence into the workflow: Used AI to align staffing, shifts, and skills to ticket complexity with improved routing and coverage, and deployed bots to monitor and self-heal routine admin tickets.

• Reimagined the operating model: Adopted GitHub Copilot, Einstein Code Builder, and AI-powered QA tools to accelerate fixes, streamlining the team to one onshore and eight offshore resources while sustaining quality.

Governance and Trust

Because AI drove both ticket analysis and automated remediation, governance and human oversight were essential. Autonomous bots handled monitoring and self-healing only for well-understood routine admin tickets, while human agents retained ownership of complex and ambiguous issues, keeping AI framed as an accelerator rather than an unsupervised decision-maker.

GenAI insights from historical tickets were used to inform staffing and routing decisions that humans validated, and AI development tools such as GitHub Copilot and Einstein Code Builder accelerated engineers within normal review and quality assurance processes. This disciplined approach maintained service quality and accountability even as the team streamlined, ensuring that cost savings did not come at the expense of trust or control.

Results

The transformation made support faster, cheaper, and more consistent while sustaining quality. It shifted the function from a reactive, manually staffed desk toward an intelligence-led operating model.

The result:

• A 47% improvement in resolution time through AI-accelerated analysis and remediation workflows.

• A 30% reduction in support cost through team optimization and automation, with the model streamlined to one onshore and eight offshore resources.

• Continuous optimized coverage with stabilized operations and 95% service-level compliance.

Technology Stack

In high-volume operations, support that is expensive and inconsistent quietly drains budget and erodes trust. This case shows how AI-driven intelligence, applied to real ticket data, can cut cost while improving service. This was not a vendor swap. It was a shift to an intelligence-led support operating model.