Live transcription writing Salesforce fields during the call. Coaching delivered to Slack the moment the moment happens. Agentforce orchestrating multi-step plays. None of it works reliably without a process foundation underneath. The Pulse Workflow Engine is that foundation: a three-layer engine on top of Salesforce that turns conversations into work.
Pulse listens to the call, hears MEDDPICC fields fill in, and writes them to Salesforce in about 10 seconds. That only works because every spoken signal maps to a defined Workflow Action that knows which Parent Object to update, which fields are valid, and what status the record should be in next. The engine is what makes the live writes reliable instead of risky.
Coaching DMs, deal channel summaries, transcripts, approvals. Every Pulse output lands in Slack because that's where revenue teams already work. The engine decides what to deliver, when, and to whom.
Spoken signals during the call trigger Workflow Actions. Pulse extracts the MEDDPICC fields, writes them through the engine, posts the transcript excerpt to the deal channel for audit. About 10 seconds end-to-end.
Without it, AI is guessing which fields to update on which records. With it, AI executes against a defined process: Parent Objects, typed Workflow Actions, conditional status transitions. Reliable, not hopeful.
Every workflow in Pulse runs on the same relational state machine. The same three primitives describe sales discovery, patient intake, loan origination, donor stewardship, and support escalation. That's why the 500+ library spans industries, not just sales.
The Salesforce entity the workflow runs against. Opportunity, account, patient intake, application, ticket, donor record. Standard or custom. The engine reads against your real records, in place.
The discrete, typed steps in the process. Each one a defined node with required parameters, dependencies, and traversal logic. Not a checkbox. An operation the engine knows how to run, hand to a human, or let an AI execute.
The state of each action at any point. Drives branching, conditional progression, and re-entry without re-running completed work. The engine always knows where every record is and which steps are valid next.
LLMs are probabilistic. They will hallucinate fields, skip required steps, and invent process states if you let them loose on raw Salesforce data. The Pulse Workflow Engine is deterministic. Combine the two (the LLM as the worker, the engine as the rails) and you get AI in Salesforce that's actually reliable.
Your team defines which Workflow Actions correspond to which conversational signals, which fields populate from which spoken cues, and which status transitions require human approval. Not a Pulse implementation consultant. Your team. The AI learns your process.
The AI doesn't guess what's possible. It picks from a constrained, parameterized set of Workflow Actions you've defined. No hallucinated capabilities. No "agent decided to update the wrong field."
The AI can't jump from "lead created" to "contract signed" without satisfying the actions in between. The engine refuses invalid moves. That's the safety rail that turns probabilistic AI into reliable execution.
Connect an MCP to raw Salesforce, you've connected it to a data lake. Connect an MCP to the Pulse Workflow Engine, you've connected it to a process foundation it can actually execute against. The same one your live transcription, Slack delivery, and Agentforce flows already run on.
The market uses "workflow" loosely. Here's what's actually under the hood.
| Capability | Salesforce Tasks | Salesforce Action Plans | Momentum (Salesforce-owned) | Pulse |
|---|---|---|---|---|
| Sequence and dependency between steps | None. Flat list. | Predefined dump, no dependency | Per-workflow builder | Native to engine |
| Conditional branching | None | None | Per-workflow logic | Native via L3 status transitions |
| Re-entry without re-running completed work | Manual | Manual | Per-workflow logic | Native via state machine |
| AI executes against typed actions, not free-form prompts | No | No | No. AI runs on prompts. | Yes. Every Action is typed. |
| End-user training surface | n/a | n/a | No | Yes |
| Pre-built library | n/a | FSC / Public Sector templates | Build it yourself | 500+ across industries |
| Industries supported | Generic | Financial Services, Public Sector | B2B SaaS sales | Sales, healthcare, financial services, non-profit, support |
500+ pre-built workflows ship with Pulse, spanning sales discovery, patient intake, loan origination, donor stewardship, claims handling, and support escalation. Each one is a full L1/L2/L3 definition you can use as-is or fork. If your process doesn't match anything in the library, define it from scratch using the same three primitives.
The training surface lets your end-users tell the engine which Workflow Actions correspond to which conversational signals, which fields to populate from which spoken cues, and which status transitions require human approval. The AI learns your process, including the parts that aren't in any playbook.
During the meeting, Pulse transcribes live, picks Workflow Actions from your defined set, populates the required parameters from what's being said, transitions L3 statuses according to your rules, and posts the right outputs to the right Slack channels. About 10 seconds from spoken word to Salesforce write. Agentforce or any MCP-connected agent can plug into the same engine to take additional actions.
Every Workflow Action is configurable to require human review, run autonomously, or escalate on a condition. Reps and managers see proposed status transitions and field writes in their Slack DMs and approve or correct in one click.
Book 15 minutes. We'll show you a real call where live transcription writes Salesforce fields through the engine, posts the deal summary to Slack, and lets the AI take the next step. All running on the same three-layer foundation.