Let AI Answer From Your KB. 0 Hallucinations.
Vector search retrieves Knowledge articles before AI responds. Real records, not training data. Near-zero hallucinations.
91%of CRM data is incomplete, meaning AI models trained on it produce unreliable outputs (Salesforce)
Your AI chatbot makes things up
Without retrieval-augmented generation, your AI responds from training data - not your actual knowledge base. Customers get plausible-sounding answers that are factually wrong.
Of AI chatbot answers contain hallucinations
Without RAG, AI models generate responses from general training data. Your Knowledge articles, case histories, and PDFs sit unused while customers get wrong answers.
“It enables Salesforce professionals like me to leverage AI of my choice in a declarative manner. Case summarization, classification, routing, personalized email response generation.”
- Sury Ramamurthy, Technical Architect, Innolake Corporation
Build prompts with Prompt BuilderReferenced in standard AI responses
Your team has built hundreds of Knowledge articles, but standard AI ignores them entirely. Every answer is a guess instead of a citation from your approved content.
“GPTfy accurately understands user input and generates high-quality content in the right format.”
- Ankita Dhamgaya, Director and Founder, AlgoCirrus
Secure this with Security LayerVector store support with your AI provider
Index your Salesforce content into vector stores on AWS, Azure, or GCP using your existing cloud contracts. Hybrid semantic + keyword search finds the right content every time.
“I was looking for a declarative way to integrate OpenAI with Salesforce. GPTfy is easy to configure, free and the support from Rahul and team is awesome. Prompt builder is super cool.”
- Sourav Halder, Salesforce Professional
Power your bot with Einstein ChatbotRAG Retrieves Records Before AI Responds
Vector Search Finds Relevant Knowledge via RAG Pipeline
Vector search retrieves Knowledge articles, case histories, and PDFs before AI responds. Watch the RAG explainer demo.
Responses Grounded in Salesforce Data Through Retrieved Content
AI answers from your actual records, not training data, reducing hallucinations to near zero. See RAG in action.
Search Across Knowledge Semantically
Hybrid Vector and Keyword Retrieval Through Semantic Search
Hybrid vector + keyword search surfaces relevant articles even without exact phrase matches. Configure in the prompt management demo.
AI Generates From Retrieved Content via Prompt Builder
The Prompt Builder injects retrieved content chunks so AI sees actual articles and case notes before responding.
Multi-Cloud Vector Stores via AI Connections
Connect to GCP, AWS, or Azure via AI Connections
Connect to Pinecone, Vertex AI, Azure Search, or any REST vector store via AI Connections with Named Credentials.
Salesforce-Native RAG Pipeline Execution
The RAG pipeline runs inside your org. PII is masked by the Security Layer before any external API call.
Why Choose Retrieval-Augmented Generation
Retrieve Real Records, Not Training Data
GPTfy queries your vector store before generating any response. See the RAG explainer video for a full walkthrough.
Index Any Salesforce Content Declaratively
Ingest Knowledge articles, custom object records, attached files (PDFs, Word docs), external objects, and website content into your vector store through GPTfy's point-and-click configuration.
PII Masking Before Embedding
GPTfy's security layer masks sensitive data before content is vectorized. Field-level security is enforced, and every retrieval is logged in the AI audit trail.
Powerful Capabilities
Einstein Chatbot With Knowledge Retrieval
GPTfy injects retrieved Knowledge article chunks into Einstein Chatbot prompts, so bot responses cite your actual articles instead of generating generic answers.
Case Resolution From Similar Past Cases
When an agent opens a case, GPTfy retrieves resolution notes from semantically similar past cases and presents them as recommended actions - no manual search required.
Document-Grounded Executive Summaries
Upload PDFs or Word docs to Salesforce, index them through GPTfy's RAG config, then generate executive summaries grounded in the actual document content via File Analysis.
Cross-Object Knowledge Synthesis
Combine retrieved content from Knowledge articles, case notes, and account records into a single AI response that synthesizes information across data sources.
Key Takeaways
- Vector search retrieves Knowledge articles, case histories, and PDFs before the AI generates a response, reducing hallucinations.
- Hybrid semantic plus keyword retrieval surfaces relevant articles even without exact phrase matches.
- Connect to Pinecone, Vertex AI, Azure Cognitive Search, or any REST vector store via AI Connections with Named Credentials.
- Security Layer masks PII before content is vectorized. Only sanitized embeddings leave your Salesforce org.
- Index Knowledge articles, custom objects, attached files, external objects, and website content declaratively.
Frequently Asked Questions
GPTfy's RAG system maintains production-ready security by orchestrating the RAG pipeline inside your Salesforce org, with PII masked before any data reaches your AI provider or vector store. It applies PII-masking rules before content embedding, enforces field-level access via Salesforce permissions, and maintains an immutable audit trail of all retrievals and AI calls. This ensures compliance with data residency requirements and security protocols.
The RAG system supports multiple data sources including Salesforce Knowledge articles, custom objects, attached files (PDFs, documents), external objects, and website content. All integrations are managed through a declarative interface, making it easy to set up and maintain without requiring complex coding or external infrastructure.
Hybrid semantic retrieval combines vector search with keyword matching to achieve optimal balance between recall and precision. This approach reduces AI hallucinations by ensuring the AI sees your actual Knowledge articles, case notes, and documents before generating responses. GPTfy retrieves the most relevant content chunks based on user queries or record context and injects them into the AI prompt, grounding responses in real data instead of generic training data.
GPTfy's AI Connections support multiple vector store providers including Google Vertex AI Matching Engine, Pinecone, Azure Cognitive Search, and any vector store with a REST API. Configuration is done through GPTfy's declarative interface using Named Credentials for authentication. This multi-cloud approach lets you use your existing cloud contracts and infrastructure without being locked into a single vendor.
GPTfy can index Salesforce Knowledge articles, custom object records, attached files (PDFs, Word documents), external objects, and website content into your vector store. Ingestion is configured through GPTfy's point-and-click interface, where you select the objects and fields to embed. The Security Layer applies PII masking before content is vectorized, so only sanitized embeddings leave your Salesforce org.
GPTfy's Security Layer masks PII and sensitive fields before any content is sent to the embedding model or vector store. Only sanitized vector embeddings leave your Salesforce org, and raw PII never reaches external services. Every retrieval and AI call is logged in AI_Response__c records with full audit trails showing which content chunks were retrieved, which prompt was used, and which user initiated the request.
See RAG-Powered Responses Using Your Salesforce Data
We will connect GPTfy to your Knowledge base and case history, then show you AI responses grounded in your actual records - not hallucinated training data. 30-minute demo with your org.
Explore More Features
Demo: What is RAG?
Watch how RAG retrieves and grounds AI responses
Demo: Snowflake RAG
See RAG with external Snowflake data sources
Security Layer
PII masking before content embedding and retrieval
Prompt Builder
No-code AI prompt creation with context mapping
Einstein Chatbot AI
RAG-powered knowledge retrieval for chatbot responses
Experience Cloud AI
Deploy RAG-powered AI search in customer portals
File Analysis
Analyze PDFs and documents with AI