Data Cloud Vector Database will unify all business data, including unstructured data like PDFs, emails, and transcripts, with CRM data to enable grounding of AI prompts and Einstein Copilot, eliminating the need for costly and complex fine-tuning of LLM models
Data Cloud Vector Database will be built into the Einstein 1 Platform, enabling all business applications to harness the power of unstructured data through workflows, analytics, and automation
Einstein Copilot Search will provide AI search capabilities to deliver precise answers from Data Cloud instantly in a conversational AI experience — boosting productivity for all business users
Salesforce (NYSE: CRM) today announced significant updates to its Einstein 1 Platform, adding the Data Cloud Vector Database and Einstein Copilot Search.
This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20231214574921/en/
Einstein Copilot Search enables patterns like Retrieval Augmented Generation to make AI more trusted and relevant (Graphic: Business Wire)
Accurate and relevant generative AI prompts require grounding in the most comprehensive set of enterprise data. Until now, this has required expensive and labor-intensive model fine-tuning. Data Cloud Vector Database will solve this challenge by making it quick and easy to bring unified business data into any AI prompt so customers can deploy trusted, relevant generative AI across all Salesforce applications without having to fine-tune an off-the-shelf large language model (LLM).
Data Cloud Vector Database – built into the Einstein 1 Platform – enables AI, automation, and analytics for improved decision-making and customer insights across all Salesforce CRM applications. Data Cloud will also power Einstein Copilot Search — announced today — enhancing Einstein Copilot, Salesforce's generative AI assistant, with AI search capabilities that use all business data to deliver more precise information, conveniently in the flow of work.
New capabilities:
Data Cloud Vector Database
Data Cloud Vector Database will remove the need to fine-tune LLMs by seamlessly using all business data to enrich AI prompts, allowing customers to use a variety of data types across their business applications and workflows. This increases business value and ROI by unifying unstructured data, including PDFs, emails, documents, and transcripts, with structured data, including purchase history, customer support cases, and product inventory, to power AI, automation, and analytics across every Salesforce application.
For example, customer service leaders will enhance efficiency and customer satisfaction by utilizing a platform that proactively presents relevant knowledge articles to service agents the moment a case is created. This allows for quick identification of similar cases and the integration of automation, thereby reducing case resolution time and improving the overall customer experience.
Einstein Copilot Search
Einstein Copilot, available in February, will include enhanced AI search capabilities that interpret and respond to complex queries from users by tapping into diverse data sources, including unstructured data. Einstein Copilot Search will enhance Einstein Copilot, providing sales, customer service, marketing, commerce, and IT teams with an AI assistant capable of solving problems and generating content by accessing real-time unstructured and structured business data. Customers will benefit from an AI assistant that understands and addresses complex queries by accessing insights and knowledge previously unattainable with foundational LLMs due to limitations in their training data. Einstein Copilot Search also provides citations to source material. Salesforce's Einstein Trust Layer builds trust and confidence in AI-generated content while maintaining data governance and security.
For example, in customer service, Einstein Copilot Search will link a customer's concerns from unstructured emails and phone call transcripts to their structured support ticket history. This provides service representatives with a detailed understanding of customer issues and their historical context and AI-generated, data-backed resolution suggestions. And, the new integration of source citations enhance the customer service team's confidence in the AI-generated insights.
Why it matters: Data is crucial for delivering accurate, compelling customer experiences and driving AI innovation. However, 90% of enterprise data exists in unstructured formats like PDFs, emails, social media posts, and audio files, making it largely inaccessible for business applications and AI models. Forrester predicts* that the volume of unstructured data managed by enterprises will double by 2024, highlighting the urgency of this challenge. While 80% of IT leaders acknowledge the transformative potential of generative AI in leveraging data more effectively, 59% still need a unified data strategy to harness this power.
Salesforce perspective:
- "The Data Cloud Vector Database relieves the challenge of costly and complex processes to harness the value of unstructured data. Now, our customers can reason over their full spectrum of their enterprise data to power their business applications more effectively. By integrating both structured and unstructured data, our new Data Cloud Vector Database transforms all business data, from emails to documents to transcripts to social media posts, into valuable insights. This advancement in Data Cloud, coupled with the power of LLMs, is a game-changer, fostering a data-driven ecosystem where AI, CRM, automation, Einstein Copilot, and analytics turn data into actionable intelligence and drive innovation." — Rahul Auradkar, EVP and GM of Unified Data Services & Einstein
Use cases:
- Customers can receive better, more automated customer service. Customers visiting a self-service page can ask the Einstein Copilot-powered chatbot about upgrade eligibility. The chatbot answers by pulling relevant details from multiple knowledge sources, and citing the specific source articles.
- Service leaders can improve productivity and customer experiences by understanding service-related trends. To improve customer experiences, call center leaders can use unstructured data and AI to automatically compare cases and identify those that are similar in their intent, triggering automated Flows that alert case owners if a new case is a potential duplicate. Service leaders can also use analytics tools like Tableau to cluster knowledge articles and spot trends across newly created cases and articles, helping uncover new ways to deliver better customer experiences.
- Marketers can tailor campaigns based on consumer intent and behavior. When building a campaign, a marketer can use Marketing Cloud Intelligence to understand consumer intent by analyzing unstructured survey data and transcripts in Data Cloud, then iterate on email templates and copy them directly from within Einstein Copilot through natural language instructions.
- Commerce teams can generate new product descriptions faster. When creating a product description, a brand manager can ask Einstein Copilot to compare the details of a new product against existing products that are similar. They can use the information contained in product catalogs from closely related products to generate relevant descriptions quickly.
- Sales can increase revenue using AI to surface insights from prior customer interactions. To prepare for a customer meeting, a sales rep can have Einstein Copilot reference specific unstructured data, like a customer’s 10-K or past email interactions, and ask questions about that data. Responses will include relevant information such as the top three initiatives for the customer’s company in the next fiscal year and who the new executive bench includes, setting the sales rep up with valuable insights for their meeting.
- IT can discover problems and anomalies in product telemetry. Unstructured content produced from machine operations, including machine logs, sensor readings, images, or audio recordings, can be ingested into Data Cloud. Tableau can then analyze this data and Einstein can identify and flag unusual data points through semantic similarity that reveal problems with the equipment.
Customer perspective:
- “With Salesforce automation and AI technology, we have reduced response time for our 6 million annual roadside events by 10%. Our reps have access to real-time data, helping them quote field service arrival times more accurately, automate fleet deployment, and personalize service to members in crisis. Additionally, members can use self-service options from anywhere, which has reduced manual service cases by 30%. As we plan for the road ahead, Salesforce AI will help us serve members more efficiently across the company, including our insurance business, by distilling complex insurance policies into swift, customer-centric responses, delivering faster support for clients and increased productivity for agents." — Shohreh Abedi, EVP, Chief Operations Technology Officer, and Member Experience at AAA – The Auto Club Group
Availability:
- Data Cloud Vector Database will be in pilot in February 2024
- Einstein Copilot Search will be in pilot in February 2024
- Einstein Copilot will be generally available in February 2024
Learn More:
- Learn more about the Einstein 1 Platform
- Learn about Salesforce Data Cloud, which now processes over 100 billion customer records daily
- Dive deeper into retrieval augmented generation (RAG)
- Watch World Tour NYC live for more on Salesforce’s latest innovations
*Forrester, Predictions 2024: Data and Analytics by Zeid Khater, 26 October 2023
Any unreleased services or features referenced here are not currently available and may not be delivered on time or at all. Customers should make their purchase decisions based upon features that are currently available.
View source version on businesswire.com: https://www.businesswire.com/news/home/20231214574921/en/
Contacts
Steve Mnich
Smnich@salesforce.com
