Overview
Industry
Cybersecurity
Region
United States (US)
Company Size
5,000+ Employees
Featured Solution
Salesforce Agentforce AI, Amazon Connect, and AWS-Powered Call Summarization Framework
About the Client
The client is a global cybersecurity and cloud data management company. They help enterprises strengthen cyber resilience and protect critical business data against ransomware, cyberattacks, and operational disruptions. Serving thousands of enterprise customers worldwide, the organization delivers secure backup, recovery, and data protection solutions across cloud, SaaS, and hybrid environments while enabling faster business continuity and operational recovery.
When Partial AI Summaries Create Full Operational Blind Spots
The customer relied on Amazon Connect and Salesforce to manage customer support interactions across its global support operations. However, the existing Amazon Connect Gen-AI summarization capability generated summaries using only a limited portion of the call conversation rather than processing the complete transcript.
As a result, their support agents received incomplete summaries that often missed critical customer context, follow-up actions, and resolution details. So, they had to spend additional time manually reviewing transcripts to understand conversations fully, increasing After Contact Work (ACW) time and slowing case handling efficiency. The customer wanted a more intelligent AI-driven summarization framework capable of processing complete call transcripts, generating actionable summaries, and improving agent productivity directly within Salesforce.
Key Challenges: Limited AI Context, Increased Manual Effort, and Lower Support Efficiency
Increased Manual Effort
Agents spent additional time reviewing transcripts and documenting post-call interactions.
Higher After Contact Work (ACW) Time
Additional conversation analysis and follow-up preparation extended post-call workflows.
Slower Case Handling
Limited visibility into complete customer conversations, delayed issue assessment, and resolution workflows.
Inconsistent Follow-Up Actions
The absence of structured next-step recommendations created gaps in follow-up execution.
Reduced Workflow Efficiency
Agents could not access detailed AI-generated insights directly within Salesforce workflows.
Limited Operational Scalability
Disconnected workflows in post-call handling reduced the ability to scale support operations.
Building an AI-Powered Transcript Intelligence Framework Inside Salesforce
To address these challenges, experts at Grazitti Interactive designed and implemented an AI-powered transcript intelligence framework using Salesforce Agentforce AI, Salesforce Voice Cloud, Amazon Connect, AWS services, and custom Salesforce components.
What our execution strategy included:
- Centralized Amazon Connect Conversation Data Inside SalesforceFirst, we implemented a workflow to capture customer call transcripts, metadata, and Amazon-generated summaries stored in Amazon S3 after every support interaction. This data was automatically triggered into Salesforce and stored within custom Salesforce objects for downstream processing.
- Built Custom LWC Components for On-Demand AI ProcessingNext, we developed custom Lightning Web Components (LWC) inside Salesforce that enabled support agents to trigger AI-powered transcript processing whenever required. This allowed agents to selectively initiate AI actions based on active support workflows and case requirements.
- Enabled Full-Transcript AI Processing With Agentforce AIOnce transcript data became available inside Salesforce, we configured Salesforce Agentforce AI to process complete customer conversations instead of relying only on Amazon Connect’s native partial summarization capability. This enabled more context-aware summaries and improved conversation understanding.
- Generated Actionable Support Insights and Workflow ActionsUsing Agentforce AI capabilities, the framework generated:
- Detailed conversation summaries
- MOMs (Minutes of Meeting)
- Recommended next actions
- Pending follow-up actions
- Structured support insights for post-call workflows
The solution also enabled agents to publish public or private case comments and support case creation workflows directly within Salesforce based on customer conversations and support requirements.
From Transcript Overload to Faster Case Handling
By implementing the AI-powered transcript intelligence framework, experts at Grazitti Interactive helped the customer reduce After Contact Work (ACW) time, minimize manual transcript review effort, and improve overall support workflow efficiency. Their support agents gained faster access to complete customer conversation context, enabling more consistent follow-ups, improved case handling, and quicker response preparation across high-volume support operations.
By centralizing conversation intelligence within Salesforce, the customer improved operational visibility, streamlined post-call activities, and reduced the dependency on manual documentation and conversation analysis.
Highlights
45%
Reduction in After Contact Work (ACW) Time
50%
Improvement in Agent Productivity
60%
Reduction in Manual Transcript Review Effort
40%
Improvement in Case Handling Efficiency
Conclusion
Modern support teams need connected workflows, complete conversation intelligence, and faster access to actionable insights within the systems they already use. Without that, support operations become reactive, manual, and difficult to scale.
By combining Salesforce Agentforce AI, Salesforce Voice Cloud, Amazon Connect, and AWS services, businesses can reduce manual support effort, improve agent productivity, and enable more consistent case handling with AI-driven conversation intelligence inside Salesforce. If your organization is looking to modernize support operations with AI-powered workflows, our Salesforce AI experts can help. Just drop us a line at [email protected]
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