Integration · AI and Machine Learning
Bring specific OpenAI model capabilities alongside Agentforce
OpenAI offers GPT chat and reasoning models plus an embeddings API that turns text into vectors for semantic search. Agentforce already runs Salesforce's own models through the Einstein Trust Layer, but some teams want a particular GPT capability — a specific model behaviour, or embeddings feeding a retrieval layer — for a defined step. Emerge Digital designs that integration where you want it and where Salesforce supports bring-your-own or connected models, so an agent can call OpenAI for the task it suits while the rest of the flow stays on Agentforce.
- OpenAI
- Prompts
- Embeddings
- Named model
- Evaluation
- Approved output
- Human approval
OpenAI ai workflow into Salesforce Agentforce with a human approval gate.
What the connection unlocks
- A named GPT model available for a specific step — drafting, classification, or a reasoning task — configured where Salesforce supports a connected or bring-your-own model, not as a wholesale replacement for Agentforce's own models.
- OpenAI's embeddings API used to turn your content into vectors, so a retrieval layer can find semantically similar passages rather than matching on exact keywords.
- The model choice is scoped to the work: you decide which steps route to OpenAI and which stay on Salesforce's built-in models, with that boundary set in configuration.
- Prompts, context, and outputs pass through governance you control, so what a GPT-backed step is allowed to see follows from permissions, not from how a prompt is worded.
Where it can make a difference
These scenarios explain possible workflows. They are not claims of delivered client results; licensing, permissions and feasibility are confirmed during discovery.
Embeddings that power semantic recall
Emerge uses OpenAI's embeddings API to vectorise your knowledge, so when an agent needs background it retrieves the passages closest in meaning to the question rather than the ones that happen to share words.
A GPT step for a specific draft
For a task where a particular GPT model fits — say composing a nuanced customer reply — the agent routes that one step to OpenAI, then hands the draft back into the Agentforce flow for review before it is sent.
Connect the workflow to the outcome
OpenAI capabilities tend to attach at the Engage and Convert stages, where drafting and reasoning quality show up directly in a customer conversation. Embeddings also work quietly underneath Discover, powering the retrieval that helps an agent find the right context. The system of record and the orchestration stay on Salesforce; OpenAI is called for the specific steps you choose.
Built around your environment
Emerge Digital connects OpenAI with Salesforce Agentforce as a consulting engagement, not a self-install app. We work within what Salesforce supports for connected or bring-your-own models, configure the steps that call OpenAI, and stand up embeddings-based retrieval where it grounds an agent. We do not claim GPT replaces Agentforce or that it swaps in with a single switch — we design where OpenAI adds value, set the permissions that govern what those steps see, and keep a person in the loop where the stakes warrant judgement.
Explore AI and automation ↗Before we connect
Does using OpenAI replace Agentforce's built-in models?
No. Agentforce keeps running Salesforce's own models through the Einstein Trust Layer. Where Salesforce supports it, Emerge configures OpenAI for specific steps you choose — it is an additional capability alongside Agentforce, not a wholesale swap.
Is this a one-click way to plug GPT into Agentforce?
No. Connecting OpenAI is a services engagement. We design which steps route to OpenAI within what Salesforce allows, build the configuration, and govern access — there is no self-install connector that does this for you.
How is data that goes to OpenAI controlled?
Access is governed by the permissions and configuration Emerge sets, not by prompt wording. We scope what an OpenAI-backed step can see and pass to the model, and design the flow so sensitive outputs are reviewed before they act.
