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.

What Autonomous Actually Means in Sales AI

Every sales tool launched in the last two years calls itself AI-powered. Most of them aren’t autonomous. They’re notification systems with a language model bolted on.

The distinction matters. An AI tool that tells you a deal is at risk is useful. An AI agent that detects the risk, creates a task, alerts the rep, and suggests a next step - without anyone asking - is a different category of technology. One adds information. The other removes work.

Most of what’s sold as AI in sales is the first kind pretending to be the second.

What is the difference between AI-assisted and AI-autonomous?

AI-assisted means the tool surfaces an insight and waits for a human to act on it. A dashboard shows deal health scores. A notification says “this lead might be a good fit.” A summary appears after a call. In every case, a human has to see it, interpret it, decide what to do, and then do it.

The bottleneck isn’t information. Sales teams are drowning in information. The bottleneck is execution - the gap between knowing something and doing something about it.

AI-autonomous means the system detects a condition, makes a judgment, and takes an action. No human in the middle. No dashboard to check. No notification to interpret. The agent reads the context, decides what should happen, and does it.

A deal goes cold. The autonomous agent doesn’t create a dashboard entry. It reads the deal history, checks the last transcript, identifies the gap, creates a task in HubSpot for the AE, and sends a Slack message with the specific signal and a recommended next step. The rep’s job is to review and act - not to discover and diagnose.

That’s the line. AI-assisted adds to your workflow. AI-autonomous replaces parts of it.

Why do most sales AI tools stop at assisted?

Three reasons.

It’s easier to build. Generating a summary or a score is a single model call. Building an agent that reads from multiple systems, makes a decision, and writes back to your CRM requires integration architecture, error handling, and workflow logic. Most vendors choose the easier product.

It’s easier to sell. “AI-powered insights” is a safe pitch. It doesn’t threaten anyone’s job. It doesn’t require ops changes. It’s an add-on. Autonomous agents require deeper integration and more trust - harder to sell to a risk-averse buyer, even though the value is higher.

It’s easier to demo. Show a dashboard with AI scores. Show a call summary. Show a “recommended next step” tooltip. It looks impressive in a 30-minute demo. Whether anyone acts on it after the demo is a different question - and the answer, for most tools, is “not consistently.”

The result: a market full of AI tools that inform but don’t execute, sold to teams that are already overwhelmed with information and starving for execution.

What does autonomous AI look like in practice?

Here’s what actually happens in a stack with autonomous agents running:

Monday 8am. The forecast digest agent scans every open deal. It compiles what changed over the past week - stage advances, close date slips, new deals, closed deals - and sends a formatted summary to the sales manager and CRO via Slack. Nobody built a report. Nobody opened HubSpot. The intelligence arrived.

Monday 10am. A new lead fills out a demo request form. The enrichment agent fires, fills in company size, industry, tech stack, and funding stage. The scoring agent evaluates the lead - 87, high fit. The routing agent checks rep capacity and segment match. Assigns it to Sarah. A Slack message hits Sarah’s DM with the lead details, score, and context. Total time from form submit to rep notification: 47 seconds.

Tuesday 2pm. The deal risk agent’s hourly scan catches a problem. An enterprise deal hasn’t had activity in 16 days. The champion’s LinkedIn shows a job change from 5 days ago. The agent creates a task in HubSpot: “Re-engage - champion departed. Map new stakeholders.” Sends a Slack alert to the AE with full context. The AE acts that afternoon instead of discovering this at Thursday’s pipeline review.

Wednesday after a call. Transcript hits the system. MEDDIC extraction agents run - Metrics, Economic Buyer, Decision Criteria populated from the conversation. Competitive intelligence agent flags a new competitor mention. Action item agent creates follow-up tasks. All fields updated in HubSpot before the rep finishes their post-call notes.

None of this required a human to initiate, interpret, or execute. The agents monitored, decided, and acted. The humans reviewed and focused on the judgment calls - deal strategy, relationship building, negotiation - that actually require being human.

How do you evaluate whether a tool is actually autonomous?

Ask three questions:

Does it write back to your CRM? If the AI generates insights but doesn’t update fields, create tasks, or trigger workflows in your actual systems, it’s not autonomous. It’s a read-only layer that requires a human to close the loop.

Does it act on a trigger without being asked? If you have to open the tool, click a button, or run a report to get value from it, it’s not autonomous. Autonomous agents run on schedules, webhooks, and events. They don’t wait for you to remember to use them.

Does the output include a completed action, not just a recommendation? “This deal is at risk” is a recommendation. “This deal is at risk - task created for AE, Slack alert sent, next step suggested based on deal context” is a completed action. The difference is whether the agent stopped at the insight or carried it through to execution.

Most tools fail all three. They read from your data but don’t write back. They require manual activation. They recommend but don’t execute. That’s AI-assisted, and it’s a fundamentally smaller category of value.

The sales teams that pull ahead in the next two years won’t be the ones with the most AI tools. They’ll be the ones with AI that actually does the work - not just talks about it.