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.

How to Set Up Your First AI Sales Agent

You don’t need an engineering team to build an AI sales agent. You need a clear trigger, the right context, a specific action, and a system that connects them. Most RevOps teams can have their first agent running in under a week.

The reason most companies don’t have AI agents in their sales stack isn’t technical complexity. It’s that nobody broke the problem down into pieces small enough to build.

What is an AI sales agent, exactly?

An AI sales agent is a system that monitors a condition, reads context, makes a decision, and takes an action - without a human in the loop.

It’s not a chatbot. It’s not a dashboard. It’s not a report someone has to read.

A deal risk agent watches for activity decay across your pipeline. When a deal goes 14 days without contact, the agent reads the deal history, checks the last transcript, and sends a Slack message to the AE with the specific risk signal and a suggested next step. The rep didn’t ask for it. The agent just did it.

That’s the difference between a tool and an agent. Tools wait. Agents act.

How should you scope your first agent?

Start small. One trigger, one action, one output.

The mistake most teams make is trying to build the full autonomous ops layer on day one. Don’t. Pick the single most painful manual process your team runs every week and automate just that.

Three good first agents:

Deal risk alert. Trigger: deal with no activity in 14+ days still marked Active. Action: pull deal context from CRM, summarize last interaction, send Slack alert to the AE with next step recommendation. This replaces the “let me check on stale deals” task your manager does every Monday.

Contact enrichment on create. Trigger: new contact enters HubSpot. Action: enrich with job title, company size, tech stack, funding stage from external sources. Write back to CRM fields automatically. This replaces the rep who Googles every new lead for 5 minutes before doing anything.

Pre-call brief. Trigger: calendar event with external attendee in 30 minutes. Action: pull deal snapshot, last call summary, open risks, attendee profiles. Deliver as a Slack message. This replaces the 20 minutes of prep your reps skip because they don’t have time.

Pick one. Build it. Prove it works. Then build the next.

What does the architecture look like?

Every agent has four parts:

Trigger - what starts it. A CRM field change, a calendar event, a time interval, a webhook. This is the “when.”

Context - what the agent reads before acting. CRM deal data, call transcripts, email threads, LinkedIn profiles. This is the “with what information.”

Decision - what the AI evaluates. Is this deal at risk? Is this lead a good fit? What’s the most important question to ask on this call? This is the “thinking” part - and it’s where AI models replace the manual judgment that used to bottleneck everything.

Action - what happens. A Slack message sent, a CRM field updated, a task created, a workflow triggered. This is the “output.”

The connecting layer is usually an automation platform - n8n, Make, or Zapier - wired to your CRM and communication tools via APIs or MCPs. The AI model (Claude, GPT, etc.) sits in the decision step. Everything else is plumbing.

What if you’re running HubSpot with a small team right now?

You probably have 30-50 open deals. Your manager reviews them once a week. Half the review is spent on deals that are fine. The other half surfaces problems that were already problems three days ago.

Your reps probably spend 15-20 minutes per call on prep that could be automated. Multiply that by 4 calls a day, 5 days a week, across 8 reps - that’s 40+ hours of manual prep your team burns every week.

Your CRM probably has hundreds of contacts with missing fields that nobody will ever manually fill in.

None of these are hard problems. They’re just problems nobody wired a system to solve.

One agent, running in the background, checking your pipeline every hour, costs nothing after the initial build. It notices things faster than any human can. And it never forgets to check.

How long does it actually take to build?

A single-purpose agent - deal risk alerts, contact enrichment, or pre-call briefs - is a 2-3 day build. Not a quarter-long implementation. Not a vendor evaluation. A focused build that connects your existing tools, adds an AI decision layer, and delivers output where your team already works.

The prerequisite isn’t engineering resources. It’s clarity on what the agent should do, what data it needs, and where the output goes.

Once your first agent is running, explore five more agents you can build this week on HubSpot and Slack, learn how MCPs connect Claude to your CRM and calendar, and see how deal risk detection catches dying deals before your reps notice.

The companies building AI agents for sales right now aren’t waiting for a perfect tool to buy. They’re wiring their existing stack to do things it should have been doing all along.