How does NeuroGTM pricing work?

Every engagement is scoped to your specific stack and goals. We walk through what you need on a 30-minute discovery call and give you a clear proposal — no black-box pricing, no surprises.

Does NeuroGTM work with Salesforce or just HubSpot?

Both. We also integrate with Slack, Gmail, Outlook, Calendly, Gong, Fireflies, Apollo, Clearbit, ZoomInfo, n8n, Make, Zapier, and more. If it has an API, we can connect it.

What happens after NeuroGTM builds the system? Who maintains it?

You own everything — no vendor lock-in. The system runs autonomously. If something in your stack changes or you want to add new workflows, you can bring us back on a Sprint or move to Ongoing Ops for continuous optimization.

How is NeuroGTM different from hiring a RevOps contractor?

A RevOps contractor configures your existing tools. We build AI systems that run on top of them — scoring, routing, enrichment, alerts, and forecasting that execute autonomously. It's the difference between setting up a workflow and building an intelligence layer.

How long does a NeuroGTM implementation take?

Sprints ship in 3–5 days. Full System Builds take 1–3 weeks. No committees, no lengthy SOWs. We go heads-down and build the actual system.

Do I own what NeuroGTM builds?

Yes. Infrastructure, workflows, intelligence layer — all yours. No recurring platform fees, no proprietary lock-in. If you want to part ways, everything keeps running.

Can I start small with NeuroGTM and expand later?

That's what Sprints are for. Pick your biggest bottleneck — lead scoring, deal alerts, CRM enrichment — and we build a production system for that one problem. Most clients come back for more once they see it running.

AI for Multi-Step Sales Processes

Most sales automation is one trigger, one action. New lead enters CRM - send a welcome email. Deal changes stage - update a Slack channel. Contact fills out a form - create a task. Simple cause and effect. Useful but limited.

Real sales processes aren’t single-step. Qualifying a lead requires enrichment, scoring, routing, and notification - in sequence, with decisions at each step. Following up after a call requires reading the transcript, extracting action items, creating tasks, updating CRM fields, and alerting the manager if something’s at risk. These are multi-step workflows where each step depends on the output of the previous one and some steps require judgment, not just logic.

AI agents handle multi-step processes the way a human would - reading context, making decisions, and adapting the next step based on what they find.

Why can’t traditional automation handle multi-step sales workflows?

Traditional automation tools - HubSpot workflows, Zapier, even basic Make/n8n setups - work on a branching logic model. If X, then Y. If Y and Z, then W. Each branch is predefined. Every possible path has to be anticipated and built in advance.

This works when the process is predictable. It breaks when it’s not.

Take post-call processing. After a sales call, the right workflow depends entirely on what happened on that call. If the prospect mentioned a competitor, that needs to be logged and the competitive brief updated. If they revealed budget constraints, the deal risk profile changes. If they introduced a new stakeholder, the contact needs to be created and associated. If they committed to a decision timeline, the close date needs updating.

No single trigger-action automation handles this. You’d need dozens of branches to cover every combination - and you’d still miss scenarios because human conversations don’t follow predefined paths.

An AI agent reads the transcript, identifies everything that’s relevant, and takes the appropriate actions for that specific call. Different actions for different calls. No predefined branches. Context-aware execution.

What does a multi-step AI workflow actually look like?

Here’s a real example: the post-call processing chain.

Step 1: Transcript ingestion. A call ends. The transcript becomes available in Gong or Fathom. This triggers the agent chain.

Step 2: Content extraction. The AI reads the full transcript and extracts structured data - action items the rep committed to, objections raised, competitors mentioned, MEDDIC fields discussed, timeline signals, and any new contacts referenced.

Step 3: Decision routing. Based on what was extracted, the agent decides which downstream actions to take. This is the step that separates AI workflows from rule-based ones. The agent doesn’t follow a predefined branch - it evaluates what it found and routes accordingly.

Step 4: CRM updates. MEDDIC fields populate in HubSpot. Competitor mentions log to the competitive intelligence field. Close date updates if the prospect gave a new timeline. New contacts get created and associated with the deal. Each update is specific to what was actually said on this call.

Step 5: Task creation. Every action item the rep committed to becomes a HubSpot task with a due date. “Send the case study by Friday” becomes a task due Friday. “Loop in your solutions engineer” becomes a task for tomorrow. The agent infers appropriate timelines from the conversation context.

Step 6: Risk evaluation. The agent assesses whether anything from this call changes the deal’s risk profile. Budget pushback? Champion uncertainty? Timeline slip? If risk signals are present, a Slack alert fires to the rep and their manager with the specific concern.

Step 7: Summary delivery. A formatted call summary - not the raw transcript, but the operationally relevant points - delivers to Slack. The rep has a record of what matters. The manager has visibility. The CRM is updated. Tasks are created. Risks are flagged. All from one trigger.

Seven steps. Multiple decisions. Context-dependent routing. Zero human intervention between the call ending and all the outputs being delivered.

How do multi-step agents handle branching and exceptions?

The key difference from traditional automation: the agent doesn’t need every branch predefined.

In a rule-based system, you build: “If competitor mentioned AND deal stage is past Discovery, THEN alert the AE AND log to competitive field.” You need a separate rule for every combination.

An AI agent operates differently. It reads the context and decides. The instructions are more like: “Read the transcript. If competitors are mentioned, log them to the competitive field with the context of what was said. If the mention suggests the prospect is actively evaluating a competitor, escalate to the AE with a Slack alert.” The agent uses judgment to determine whether a casual name-drop is the same as an active evaluation - something a rule can’t distinguish.

Exceptions get handled naturally. If the transcript is unusually short (the call was cut short), the agent recognizes there’s insufficient data and flags it for the rep to manually update. If the transcript mentions a topic the agent doesn’t have instructions for (like a legal concern), it logs it as an unclassified note for human review.

The agent doesn’t need to anticipate every scenario in advance. It reads what happened and responds appropriately.

Where do multi-step agents have the biggest impact in sales?

Post-call processing is the most immediate win - every call generates 5-7 downstream actions that currently depend on rep discipline.

Lead qualification chains are second. A new lead needs enrichment, scoring, routing, and notification - each step depending on the previous one’s output. An AI agent chains all four, handling the entire flow from form submission to rep notification in under a minute.

Deal stage transitions are third. When a deal moves from Discovery to Proposal, a multi-step agent can verify MEDDIC completeness, check that all stakeholders are mapped, confirm the business case is documented, and flag any gaps - then either advance the deal or create tasks for the rep to close the gaps before proceeding.

End-of-quarter pipeline cleanup is fourth. An agent scans every open deal, evaluates whether the close date and stage are realistic based on activity and engagement data, identifies deals that should be pushed or closed-lost, and generates a recommended action list for each rep.

Each of these is a multi-step process that requires reading context, making decisions, and taking different actions based on what’s found. Single-trigger automation can’t handle them. Multi-step AI agents can.

Learn how autonomous GTM agents execute multi-step workflows without being asked, how to automate MEDDIC qualification with an agent swarm, or how Claude Code lets RevOps teams build these systems themselves.

The next generation of sales automation isn’t faster triggers or more branches. It’s agents that read context, chain decisions, and run entire workflows end to end - the way a great ops person would, but at scale and without forgetting.