Salesforce launches AI Cloud to bring models to the enterprise

Salesforce is launching a new suite of products aimed at bolstering its position in the ultra-competitive AI space.

Called AI Cloud, the suite, which includes tools designed to deliver “enterprise ready” AI, is Salesforce’s latest cross-disciplinary attempt to augment its product portfolio with AI capabilities. In many ways, it’s a continuation of the generative AI initiative that the company launched in March, which aims to incorporate generative AI across the whole of the Salesforce platform.

“It’s really about bringing generative AI in a trusted fashion to the enterprise,” Adam Caplan, SVP of emerging technology at Salesforce, told TechCrunch in a phone interview. “We’re moving incredibly fast to leverage the history we have in AI and build this into into our stack in a trusted fashion.”

AI Cloud hosts and serves AI models — specifically text-generating models — from a range of partners including Amazon Web Services, Anthropic, Cohere and OpenAI on Salesforce’s cloud infrastructure. First-party models are available from Salesforce’s AI research division, powering capabilities like code generation and business process automation. Or customers can bring a custom-trained model to the platform while storing data on their own infrastructure.

The conceit isn’t far off from Amazon’s recently-launched Bedrock, which provides a family of models trained in-house by AWS as well as pretrained models from startup partners.

“What we’re doing is we’re basically taking an ecosystem approach — an open approach — and working with the best model for the best use case,” Caplan said.

The Salesforce-built models in AI Cloud power new capabilities in Salesforce’s flagship products, including Data Cloud, Tableau, Flow and MuleSoft. There’s nine models in total: Sales GPT, Service GPT, Marketing GPT, Commerce GPT,  Slack GPT, Tableau GPT, Flow GPT and Apex GPT.

Sales GPT can quickly auto-craft personalized emails, while Service GPT can create service briefings, case summaries and work orders based on case data and customer history. Marketing GPT and Commerce GPT, meanwhile, can generate audience segments for targeting and tailor product descriptions to each buyer based on their customer data, or provide recommendations like how to increase average order value.

Image Credits: Salesforce

Slack GPT, Tableau GPT, Flow GPT and Apex GPT are a bit more specialized in nature. Slack GPT and Flow GPT let users build no-code workflows that embed AI actions, whether in Slack or Flow. Tableau GPT can generate visualizations based on natural language prompts, and surface data insights. As for Apex GPT, it can scan for code vulnerabilities and suggest inline code for Apex, Salesforce’s proprietary programming language.

Several of the models are live as of today, including Slack GPT, Commerce GPT, Sales GPT and Service GPT. The rest — minus Flow GPT, which lands in October — are scheduled to arrive as early as this month.

Image Credits: Salesforce

One glaring omission in Cloud AI is an image generation model along the lines of DALL-E 2 and Stable Diffusion.  Caplan said that it’s in the works, acknowledging the usefulness for creating marketing campaigns, landing pages emails and more. But he added that there’s a range of barriers — from copyright to toxicity — Salesforce aims to overcome before it releases it.

So what else sets AI Cloud apart? Well, Salesforce is touting Einstein Trust Layer, a new AI moderation and redaction service. Similar to Nvidia’s NeMo Guardrails, Einstein Trust Layer attempts to prevent text-generating models from retaining sensitive data, such as customer purchase orders and phone numbers.

Einstein Trust Layer’s aimed at companies with strict compliance and governance requirements that’d normally preclude them from using generative AI tools, Caplan says. Certainly, it’s timely. A growing list of firms including Amazon, Goldman Sachs, and Verizon have banned or restricted the use of generative AI like ChatGPT, citing privacy risks.

“The number one question from every customer is around trust and security and how we can enable them as enterprise to approach these new technologies — this new world — in a safe fashion,:” Caplan said.

Einstein Trust Layer sits between an app or service and a text-generating model, detecting when a prompt might contain sensitive information and automatically removing it on the backend before it reaches the model. The service can also filter for toxicity (e.g., sexism, racism, and other forms of discrimination), whether in a prompt or in the response from a model.

Users who link models from third-party platforms such as Amazon SageMaker or Google’s Vertex AI to AI Cloud can still tap Einstein Trust Layer, Salesforce says. And for OpenAI customers, Salesforce says that it’s established a “trust partnership” with OpenAI to deliver joint content moderation using OpenAI’s safety tools in conjunction with Einstein Trust Layer.

Moderating models and prompts is tricky business, and Salesforce has plenty in the way of competition. Microsoft, which just last month debuted a new AI service to moderate text and images, including from models, offers model customization options similar to Einstein Trust Layer via its Azure OpenAI Service (albeit only for OpenAI models).

Perhaps that’s why, to further differentiate AI Cloud from the other managed AI service offerings out there, Salesforce is launching an array of prompt “templates” and prompt template building tools. Salesforce says the template’s “optimized” AI prompts use “harmonized” data to ground model-generated outputs in the context of a company’s needs, influencing the quality and relevance of the generated content.

It’s in the interest of reducing the time and cost to adapt a generative AI model in AI Cloud to a particular use case, Caplan said. For example, a customer could create a template that “instructs” a model to word email responses in a way that’s aligned with a house style, or that pulls in specific customer information from a Salesforce database.

“It’s really a fundamental shift in terms of the quality of an email and the difference between more generic copy versus copy grounded in the customer relationship management data,” Caplan said.

Of course, prompt engineering isn’t a new science. And other generative AI platforms, like Writer, Jasper and even Grammarly, provide ways to steer models’ responses toward certain styles. Arguably the real value proposition here — at least the one Salesforce is underlining — is the ease with which Salesforce data can be connected to (and manipulated by) a model.

For customers already entrenched in the Salesforce product ecosystem, it’s probably a compelling sales pitch.

“AI plus data plus customer relationship management is a really powerful combination,” Caplan said. “We can continue to make these prompts smarter and better. And that’s going to be really powerful, as well as training the models and delivering more value to our customers and similar things across the stack.”

AI Cloud will launch sometime this year, Salesforce says, with Einstein Trust Layer set to become generally available later this month.

Salesforce launches AI Cloud to bring models to the enterprise by Kyle Wiggers originally published on TechCrunch

Google Cloud and Salesforce team up to bolster AI offerings

Salesforce and Google Cloud are teaming up to help businesses leverage data and AI via a new strategic partnership.

Announced this morning, the partnership will allow companies to use their data along with custom machine learning models to anticipate their customer's needs, execs from Google and Salesforce said. The tie-up spans products and services including Google's BigQuery tooling, Salesforce's Data Cloud and Vertex AI, Google's fully managed AI platform.

"This really allows us to democratize AI so that customers get choice on which AI they want to use," David Schmaier, Salesforce's chief product officer, told TechCrunch in a phone interview.

It's the second such major partnership between Google and Salesforce in recent years. The first, inked in 2017, was largely analytics- and big data-focused. The companies subsequently expanded that original agreement, deepening their data sharing collaborations and building AI-infused customer service experience and marketing tools.

The move makes sense for Salesforce on its journey to reposition itself as a "data company." Over the past several months, Salesforce has introduced new generative AI tools, including EinsteinGPT, an AI assistant for customer relationship management (CRM) tasks like drafting emails, and SlackGPT, which answers natural language questions about a company's Slack content, servers and channels.

Salesforce is also investing in the broader generative AI ecosystem, no doubt hoping to sniff out the next hit that might bolster its expanding product portfolio. Salesforce Ventures, the company's corporate investment arm, this March launched a $250 million fund aimed at what it calls "responsible" generative AI startups.

As for Google, the renewed partnership with Salesforce puts its big data analytics and AI services front and center -- which isn't insignificant considering Salesforce's customer base of over 150,000 organizations.

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Google Cloud's arguably in a position of strength, having just two months ago reported its first profitable quarter. At the same time, the company remains third in the U.S. cloud infrastructure market, behind Amazon and Microsoft.

"From our point of view, this partnership brings two very large ecosystems of data together and makes it much simpler and easier for customers to get value," Google Cloud CEO Thomas Kurian told TechCrunch in an interview.

In terms of what the new strategic partnership will accomplish concretely, Google and Salesforce plan to launch an integration between Data Cloud -- which is essentially a sophisticated data integration layer -- and BigQuery that'll enable companies to more easily create unified profiles of customers. The integration will provide companies with access across portions of Google and Salesforce's platforms and clouds, akin to having their data housed in a single location.

Elsewhere, new connectors between Data Cloud and Vertex AI will allow companies to bring any AI models they've trained or fine-tuned in Vertex across the Salesforce platform. Companies will be able to train and retrain models on customer data from Salesforce, ostensibly streamlining the model development process.

How might companies actually use the new integrations and connectors? Salesforce provided a few examples in a memo ahead of today's announcement.

A fashion retailer, for example, could connect CRM data like customer purchase history and service interactions with non-CRM data, such as social media sentiment, to deploy a custom AI model that predicts a customer's likelihood to buy certain types of clothing based on the data. Or a financial institution could combine CRM and non-CRM data with an AI model to anticipate a customer's spending habits and investment preferences.

So, will Google and Salesforce customers actually bite? That's the perennial question. But Kurian and Schmaier seemed confident in the robustness of the new cross-platform offerings.

"Our general view is, these AI models are going to get richer and richer and much more sophisticated," Kurian said. "The result that we're trying to enable is productivity and efficiency for people doing work, and then, secondly, integrating all the information to make better decisions."

The Data Cloud and Google Vertex AI integration will be in pilot starting July 2023 and generally available in October 2023, Google and Salesforce say. As for the Data Cloud and BigQuery integration, it'll enter pilot in October 2023 and GA in February 2024.