Overview
Industry
Computer Vision
Region
United States
Company Size
450-600 Employees
Featured Solution
AI Agent Integration with Agentforce Service
About the Client
The customer is a spatial data leader pioneering 3D digital twin technology. Operating at the intersection of PropTech and SaaS, the company transforms physical spaces into dimensionally accurate, interactive virtual models. Trusted globally across real estate, construction, and insurance, its AI-powered platform optimizes property marketing, remote collaboration, and facilities management.
How Surging Support Volumes Limited Scalable Customer Onboarding
The customer was using Agentforce Service (formerly Salesforce Service Cloud) to manage its global customer service operations. As the company expanded its SaaS and hardware portfolios, the volume of customer support inquiries grew significantly. Managing these inquiries required a large human workforce, resulting in higher operational costs and slower response times.
To maintain service excellence without scaling its support team, the customer sought an AI-powered solution to automate routine customer interactions and improve support efficiency.
Where Support Inefficiencies Began Impacting Cost, Speed, and Customer Satisfaction
Over time, as support volumes grew, high pressure was placed on support velocity, operational efficiency, and resource utilization.
Rising Operational Costs
Supporting a growing volume of customer inquiries required a larger workforce, increasing operational overhead.
Reduced Agent Productivity
Human agents spent significant time addressing routine Tier 1 inquiries, limiting their capacity to focus on more complex customer issues.
Slower Response Times
Higher case volumes made it more difficult to respond to customer inquiries promptly.
SLA Compliance Risks
Without an instant resolution mechanism, the sheer volume of cases threatened the client’s ability to meet service level agreements.
How Grazitti Interactive Built an Automated Support Engine With Fin AI
To enhance Level 1 support with AI, Grazitti’s Salesforce experts implemented a structured, system-led support framework directly within the customer’s existing Agentforce Service ecosystem. The solution followed a structured, step-by-step deployment sequence to establish a consistent and secure foundation for customer support handling:
- Re-engineering Case Status Lifecycle
First, we re-engineered the core Salesforce case status lifecycle to accommodate automated resolution pathways. This ensured the system could transparently track cases as they moved from initial AI triage to active human troubleshooting or successful closure.
- Restructuring SLAs and Entitlement Processes
Next, we updated the Entitlement Process and Service Level Agreements (SLAs) within Salesforce. This step was critical to ensure that response and resolution timelines accurately accounted for the time spent in AI triage before any human handoff occurred.
- Integrating the Third-Party AI Agent
With the underlying support architecture prepared, we integrated the third-party AI agent, ‘Fin’, directly into the Salesforce ecosystem. This established Fin as the primary, automated line of defense for incoming customer cases.
- Activating Cross-Channel Knowledge Assets
Then, we configured Fin to leverage the customer’s existing knowledge base. This enabled the AI Agent to dynamically pull information, generate initial responses, and resolve cases originating from web, email, and chat channels.
- Implementing Apex Triggers and Rule-Based Routing
Next, we developed custom Apex Triggers and Apex Classes to manage real-time data handling between Fin and Salesforce. Alongside this code development, we restructured the Salesforce Case Assignment Rules to instantly route unresolved inquiries to the appropriate human queue or user.
- Launching the Help Center Experience
Lastly, we exposed the integrated chatbot directly on the customer-facing Help Center using Community Builder. This final deployment step provided a clean, user-friendly digital interface that enables end-users to access instant self-service.
Driving Support Efficiency and Operational Scalability
The integration of the Fin AI agent with Agentforce Service enhanced the client's existing support operations by introducing AI-powered Level 1 support. By handling the high volume of support requests, the solution reduced the daily workload on support teams, allowing agents to focus on more complex customer issues. Re-engineered case status workflows, optimized SLAs, and updated case assignment rules further improved support operations while providing leadership with greater visibility into case activity and performance. The result was a more efficient and scalable support model capable of handling future growth in customer support volumes without a proportional increase in headcount.
Highlights
62%
Average Case Resolution Achieved
Instant Level 1 Self-Service Deployed with Fin AI Agent
Re-engineered Salesforce SLAs and Workflows
Exposed Fin Chatbot on Help Center for End Users
Embrace AI-Driven Service Scalability
The greatest impact of AI in customer service comes from how well it integrates into existing service operations, not from automation alone. When AI agents, workflows, SLAs, and routing logic operate as a unified system, organizations can deliver faster support, maintain governance, and scale service delivery without a proportional increase in operational overhead.
As enterprise service models continue to evolve, success will depend on building connected, AI-enabled support ecosystems where autonomous agents handle routine work while human teams focus on complex customer outcomes.
Looking to modernize your service operations with Salesforce and AI? Connect with our experts to design an integration strategy tailored to your business.
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