Hi folks 👋,
Let’s talk about something that’s creating a lot of buzz in both integration and business platforms right now — AI in Salesforce. Everyone’s talking about Einstein, GPT, predictive analytics, and now Einstein 1 platform… but as someone who’s deeply involved with MuleSoft, people often ask me — “Hey, which of these AI features can I actually use or integrate from MuleSoft?”
So in this blog, I’m going to walk you through:
- What AI features Salesforce is offering these days,
- How they’re grouped,
- And most importantly — how you can leverage some of these from your MuleSoft flows.
Let’s dive in.
🤖 What AI Features Does Salesforce Offer?
Salesforce has been embedding AI under the Einstein branding for quite a while. But recently, especially with the explosion of GenAI tools, Salesforce has gone all in.
Here are the major AI capabilities available:
1. Einstein AI (Traditional ML)
This is more about classic machine learning — models trained on your Salesforce data. It includes:
- Lead scoring
- Opportunity insights
- Next-best actions
- Case classification
- Forecasting
These are often pre-built into Salesforce apps like Sales Cloud, Service Cloud, etc.
2. Einstein GPT (Generative AI)
Now this is the hot one 🔥. Einstein GPT brings generative AI into Salesforce apps. Think of it like:
- Auto-generating emails for sales reps
- Summarizing case history for support agents
- Auto-writing knowledge base articles
- Auto-generating Apex/test classes
It connects to OpenAI, Anthropic, Cohere, or even custom LLMs behind the scenes — and yes, it’s part of the Einstein 1 Platform.
3. Einstein Copilot
This is like a smart assistant inside Salesforce — it helps users ask questions, generate content, and take actions inside the Salesforce UI using natural language.
It’s powered by LLMs and supports multi-turn conversations — and is customizable too.
4. Einstein Studio
This is where things get interesting for MuleSoft folks. Einstein Studio allows you to bring your own ML model, expose it via an API, and use it inside Salesforce or any external system.
Salesforce provides MuleSoft connectors and API support to plug into these models.
🔄 So What Can We Use in MuleSoft?
Now let’s get to the key question — how can we use Salesforce’s AI in MuleSoft integrations? Here’s how I explain it to anyone trying to bridge the two worlds.
✅ 1. Consume Einstein Predictions from MuleSoft
If your Salesforce org is using Einstein Prediction Builder, you can expose those predictions via Salesforce APIs. For example, lead scoring or case classification results can be retrieved from MuleSoft when you’re processing those records.
- Use Salesforce connector in MuleSoft
- Call the prediction field or custom API
- Route or enrich data based on predicted outcomes
✅ 2. Call Einstein GPT or GenAI APIs (via MuleSoft)
Right now, Salesforce doesn’t expose Einstein GPT directly as an open API, but if you’ve got access to OpenAI, Azure OpenAI, or custom LLMs, you can:
- Trigger content generation from MuleSoft
- Summarize or enrich incoming data (e.g., summarize case descriptions before routing)
- Generate text, summaries, email content, etc.
✅ 3. Use Einstein Studio with Custom Models
Einstein Studio lets you expose custom ML models trained on external data via APIs.
From MuleSoft, you can:
- Call these APIs using HTTP connectors
- Feed them structured or unstructured input
- Use the AI response to drive downstream logic (e.g., flag risky transactions, categorize complaints)
This is perfect if your enterprise has Data Science models hosted on AWS Sagemaker, Vertex AI, etc. — and you want MuleSoft to tap into that power.
✅ 4. Embed MuleSoft Data into Copilot Experiences
While MuleSoft can’t directly “talk” to Einstein Copilot yet, there’s a roadmap for Copilot Extensions to support external actions via MuleSoft APIs.
That means someday soon, users might say:
“Copilot, fetch me today’s delayed orders from SAP.”
…and Copilot will invoke a MuleSoft API that talks to SAP and returns the answer. That’s the future we’re heading toward.
🔄 Practical MuleSoft Use Cases with Salesforce AI
Here are a few scenarios I’ve either implemented or explored recently:
| Use Case | How AI + MuleSoft Work Together |
|---|---|
| Intelligent Routing | Use Salesforce Einstein prediction score to decide where MuleSoft routes a case |
| AI-powered Data Cleansing | Send raw product feedback to OpenAI via MuleSoft, get clean summary back |
| Auto-respond to Emails | MuleSoft pulls email content → sends to LLM → auto-generates reply draft |
| Risk Scoring | MuleSoft fetches customer info → sends to Einstein Studio model → flags as high/low risk |
🧠 Final Thoughts
To wrap it up, here’s what I’d say if you’re looking to bridge AI and integration using MuleSoft:
- Salesforce has invested heavily in AI across its clouds.
- As MuleSoft developers/architects, we can tap into these AI tools via APIs, connectors, and HTTP calls.
- The real power lies in combining data from external systems (via MuleSoft) with AI inside Salesforce — that’s where smart automation begins.
We’ll probably see more tight coupling between MuleSoft and Einstein 1 in the coming releases, especially with Copilot Extensions and API orchestration.
