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GPTfy - Salesforce Native AI Platform

Read the Room Before the Call

GPTfy analyzes case comments and generates a sentiment summary inside Salesforce Service Cloud, so agents enter every call with full context.

Service agents waste minutes reading through case histories before each call. This demo shows how GPTfy generates a sentiment-aware case summary in seconds — so reps focus on resolution, not research.

Capabilities shown in this demo

Case Summary Generation

  • Reads all case comments and interaction history to produce a concise situation overview.
  • Surfaces key facts like claim status and unresolved issues so agents can open the call informed.

Sentiment Detection

  • Classifies the customer's overall emotional tone across the case timeline.
  • Lets agents calibrate their approach before the conversation starts.

Data Privacy Built In

  • PII is masked before case data leaves your Salesforce org.
  • AI processes anonymized content, and results are returned securely to the case record.

One-Click Workflow

  • Agents select the summary and sentiment prompt and run it with a single click.
  • No Apex, no custom code — configured entirely through GPTfy's Prompt Builder.

Use this video when

A health insurance call center agent picks up a case about a disputed claim and needs context before calling the policyholder

A high-volume support team wants to reduce the time agents spend reading case history before each customer interaction

A service manager wants to give reps sentiment signals so they can adjust their tone before escalated calls

A team handling after-hours callbacks uses AI summaries so the returning rep is immediately up to speed

A quality assurance team monitors sentiment trends across cases to identify recurring customer frustration points

An admin wants to add pre-call context to the case layout without requiring custom development

Frequently asked questions

GPTfy reads through case comments and email threads, then generates a concise summary of the customer's situation along with an overall sentiment rating. This gives service reps the context they need before picking up the phone, without reading through every interaction manually.

When a rep opens a case before calling a customer, they can run the summary and sentiment prompt in seconds. They immediately know whether the customer is frustrated, neutral, or satisfied — and can tailor their tone accordingly. This reduces the time spent reading case history during the call itself.

Yes. GPTfy anonymizes personally identifiable information from case records before sending data to the AI provider. The raw data stays inside your Salesforce org and only masked content reaches the model, maintaining enterprise-grade data privacy.

The demo shows this exact scenario — a health insurance call center handling a coverage dispute. GPTfy is designed to work within regulated environments, with data masking and audit trails built into every AI interaction to support compliance requirements.

No — agents can still read through the full case history if they choose. The AI summary provides a faster starting point so reps can enter conversations informed rather than cold. The original case comments remain fully accessible.

The demo focuses on Service Cloud cases and case comments, but GPTfy's prompt framework can apply the same sentiment and summary pattern to any Salesforce object that contains text-based interaction data, including custom objects.

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Video transcript
Next we're gonna look at the service cloud side of using GPTfy, particularly in cases. Now when you're running a call center in health insurance, you're gonna be getting a lot of calls about claims. And in this scenario, Andrew Garfield, a policy holder, recently received a medical bill that he thought was gonna be covered and is confused as to why it's not. Now oftentimes a customer when going through a call center has to answer a lot of the same questions that these customer service reps ask, and it can be frustrating for the customer. And customer service reps don't wanna deal with frustrated customers, and they can save time with these interactions by going through these case comments just to get an idea of where this uninsured claim is at, as well as where Andrew is at mentally with all of this. With this kind of context, the customer service rep can have a more efficient conversation with Andrew, but the downside of all of this is that it takes time for the customer service reps to sift through all of these emails just to get a better idea of Andrew's situation. So what we can do with AI is do a summary and sentiment analysis and it's gonna provide a brief summary of just everything that has gone on with this uninsured bill as well as a sentiment. And now here we can see that Andrew has had a pretty positive experience with customer service, and so the customer service rep can use this information to have a more efficient call with Andrew and can provide a better experience for Andrew without needing to sift through a bunch of information on their own when AI can provide all that context for them. Now, of course, they can still go through all of this if they want to, but they can go into it with a better understanding of the situation and not have to go in blind, which can take more time than it needs to.

Explore More

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Last updated: February 2026