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 Deal Risk Detection: Know Before Your Rep Does

The deal was dead two weeks before anyone noticed. Last activity was 18 days ago. The champion changed jobs. The close date slipped twice. All of it was visible in the CRM - but nobody was looking at that deal because 47 other deals were also in the pipeline and the weekly review only covered the top 10.

An AI deal risk agent would have flagged it on day 5. Instead, the team found out at the end of the quarter.

What signals actually predict deal risk?

Deal risk isn’t one thing. It’s a combination of signals that compound, and most of them are already sitting in your CRM unmonitored.

Activity decay. Days since last email, call, or meeting. When a deal crosses 14 days with no activity and it’s past discovery stage, something is wrong. The rep might know. They might not. Either way, the system should surface it.

Champion engagement drop. Your main contact stopped replying. Emails opened but not responded to. Meeting invites declined. This is the earliest signal that a deal is going sideways - and it’s almost never caught until the deal stalls.

Champion job change. One of the highest-signal risk indicators that exists. If your champion leaves the company mid-deal, your close probability drops dramatically. Most teams find out weeks later. An agent watching LinkedIn data catches it in days.

Stage stall. Every deal stage has an average time-in-stage for your org. When a deal exceeds that average by 50%, it’s not progressing - it’s stuck. A deal sitting in “Proposal Sent” for three weeks when your average is eight days isn’t waiting for feedback. It’s dying.

Competitor mention frequency. If the prospect mentioned a competitor once in discovery, that’s normal. If they mentioned them three times in the last two calls, that’s a signal your positioning is losing.

Decision timeline slip. The prospect said they’d decide by March 1. Then March 15. Then “end of Q1.” Each slip is a data point. Two slips in a row means the decision process has changed and your rep may not know why.

None of these signals are hard to track. They’re just impossible to track manually across 50+ deals.

How does a deal risk agent actually work?

The agent runs on a schedule - hourly or daily - and scans every open deal in your pipeline against a risk model.

It pulls deal metadata from HubSpot or Salesforce: stage, days in stage, last activity date, contact engagement history, close date changes. It cross-references against your org’s benchmarks - average sales cycle, typical time-in-stage per stage, historical win rates by deal profile.

When a deal crosses a risk threshold, the agent doesn’t update a dashboard. It takes action.

A Slack message fires to the AE: “Deal with Acme Corp flagged high risk. No activity in 16 days. Champion Sarah Johnson changed roles 5 days ago. Close date has slipped twice. Suggested action: re-engage VP of Ops, request new stakeholder mapping.”

The message includes context the rep would need 10 minutes to assemble manually. The agent assembled it in seconds.

If the risk is severe enough - champion gone, no activity in 21+ days, multiple signals compounding - the agent also notifies the sales manager and creates a task in the CRM for intervention.

What changes when you catch risk early?

The math is straightforward. If your average deal is worth $40K and you have 60 open deals, even saving two deals per quarter that would have gone dark is $80K in recovered pipeline.

But the bigger change is behavioral. When reps know the system is watching for risk signals, they stop letting deals drift. When managers get risk alerts on Monday morning instead of discovering stale deals in a Thursday pipeline review, they intervene before the deal is unsalvageable.

Early detection doesn’t just save individual deals. It changes how your team treats pipeline hygiene as a system.

What if you’re running a team of 10 reps right now?

You probably have 50-80 open deals at any given time. Your manager reviews them weekly. Maybe 15 minutes per rep, if you’re disciplined.

In that weekly review, the manager is trying to do what the agent does continuously: spot the deals that are going sideways. But they’re doing it from memory, from whatever the rep volunteers, and from a HubSpot view they haven’t had time to customize.

The agent checks every deal, every day, against every signal. It doesn’t forget. It doesn’t get busy. It doesn’t skip the deals that seem fine.

A deal risk agent isn’t replacing your manager’s judgment. It’s making sure the judgment gets applied to the right deals at the right time - not the ones that happen to come up in a review.

See how autonomous GTM agents run these checks without being asked, why you should stop holding weekly pipeline reviews entirely, or explore five AI agents you can build this week on HubSpot and Slack.

The best sales teams don’t find out a deal is at risk in a pipeline review. They find out the day it happens - because a system told them before anyone had to ask.