Every Email. Pre-Analyzed. Pre-Answered.
GPTfy adds AI to Salesforce Email-to-Case — classifying intent, detecting sentiment, running root cause analysis, and drafting a RAG-grounded response before any agent opens the case.
Email-to-Case is reliable but manual — agents read, classify, route, and reply to every message by hand. This demo shows how GPTfy makes every incoming email pre-classified, pre-routed, and pre-answered using AI and your connected knowledge base.
Capabilities shown in this demo
Intelligent Email Classification
- Extracts intent, case type, and sentiment from email content within seconds.
- Populates Salesforce case fields automatically to enable routing rules and flow triggers.
Automated Root Cause Analysis
- Determines the underlying issue from the customer's email content.
- Ensures the case reaches the correct team — Security, Compliance, Technical Support — without manual reading.
RAG-Grounded Response Drafting
- Queries Salesforce Knowledge Articles, FAQs, and internal documentation using a RAG model.
- Drafts a complete, policy-consistent response ready for agent review or automatic send.
Configurable Automation Level
- Choose full automation or a supervised model where agents review before sending.
- Routing rules, escalation criteria, and response triggers are configurable without code.
Use this video when
A support team handling high email volume wants cases to arrive pre-classified and pre-answered so agents focus on exceptions
An enterprise with a Security or Compliance team wants privacy-related enquiries routed automatically without manual triage
A service director wants to reduce average first response time by drafting AI responses the moment an email arrives
A team wants to maintain a human-in-the-loop review step while still eliminating the reading, classification, and drafting effort
An ops lead wants to configure routing rules and escalation criteria that fire automatically based on AI-detected case type and sentiment
A knowledge manager wants to ensure every customer-facing email response is grounded in accurate, current documentation
Frequently asked questions
GPTfy intercepts every incoming case email and immediately runs AI analysis — extracting intent, topic, case type classification, sentiment detection, and root cause analysis. It then queries your connected knowledge base to draft a complete, context-aware response, all before a human agent reads the email.
GPTfy's Intelligent Routing reads the email content and extracts a summary of what the customer needs help with, a case type classification (such as Question, Complaint, or Technical Issue), and a sentiment detection. The demo shows these fields being populated on the Salesforce case record within seconds of the email arriving.
After classification, GPTfy queries your connected knowledge base using a RAG model — syncing with Salesforce Knowledge Articles, FAQs, internal documentation, and product guides. It uses these sources to draft a complete, accurate email response that is consistent with your tone and policies, ready for the agent to review and send.
Yes. Enterprises can choose between full automation — where GPTfy sends the response directly — or a supervised model where a support rep reviews and customizes the AI-drafted response before it goes out. Routing rules, response triggers, and escalation criteria are all configurable to match your support processes.
When an agent opens a case, the email is already pre-understood, pre-classified, and often pre-answered. The agent sees the summary, the case classification, the sentiment, the root cause analysis, and a draft response — all generated automatically. They review, approve, and send rather than starting from a blank screen.
GPTfy's RAG model connects to Salesforce Knowledge Articles, internal FAQs, product documentation, and any other documentation you provide. The demo shows responses grounded in accurate, current policy information — ensuring agents never send outdated or incorrect answers.
Ready to see this in your Salesforce org?
Book a 45-minute session and we'll walk through this use case using your own data.
Video transcript
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Last updated: February 2026
