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.

The Pre-Call Brief Your Rep Never Has Time to Build

Every rep has jumped on a discovery call they weren’t ready for. Skimmed the CRM for 90 seconds in the Uber. Opened LinkedIn while the prospect was still on hold. Tried to remember what was said on the last call while also trying to listen to this one.

It’s not a discipline problem. It’s a time problem. Building a proper pre-call brief takes 20 minutes of pulling from five different places. Nobody has 20 minutes between back-to-back calls. So it doesn’t happen, or it happens badly, and the rep walks into a conversation at 60% when they could be at 100%.

This is exactly the kind of work an AI agent should be doing instead.

What does a pre-call brief AI agent actually build?

Thirty minutes before every calendar event that includes an external contact, the agent runs. It pulls from your CRM, your call transcription tool, your email thread, and LinkedIn. It produces a single Slack message - not a dashboard, not a report - with everything the rep needs to walk in sharp.

Here’s what’s in it:

Deal snapshot. Stage, close date, deal value, days in current stage versus average. If it’s been sitting somewhere too long, that surfaces immediately.

Last interaction summary. What was said on the most recent call - the actual substance, extracted from the transcript. What the prospect said they needed. What the rep committed to following up on. Whether that follow-up happened.

Open risks. Missing MEDDIC fields. Contacts who haven’t engaged in 21+ days. A decision timeline that’s slipping. The agent flags these before the call, not after.

Who’s on the call. For every attendee, a two-line profile: title, tenure, what they’ve said in past interactions, and whether they’ve been identified as champion, blocker, or economic buyer.

One suggested question. Based on what’s missing from the qualification picture, the agent surfaces the single most important thing the rep should try to learn on this call.

The whole thing takes 45 seconds to read. The rep is more prepared than if they’d spent 20 minutes doing it manually.

How does automated pre-call prep work in practice?

If you’re running a team of 12 reps with an average of four external meetings per day each, that’s 48 calls happening every day. Right now, maybe a third of those have any real prep behind them. The other two-thirds are going in cold.

Each underprepared call has a cost. Discovery questions that were already answered. Objections that could have been anticipated. Decision criteria the rep didn’t know to ask about. Small friction that compounds across a quarter into deals that stall, close slower, or don’t close at all.

The agent doesn’t just save prep time. It raises the floor on every single conversation your team has.

Why can’t you build pre-call briefs manually?

The reason this doesn’t exist at most companies isn’t that nobody thought of it. It’s that the data lives in five places that don’t talk to each other.

Calendar events are in Google or Outlook. Deal data is in HubSpot or Salesforce. Call transcripts are in Gong or Fathom. Email history is somewhere else entirely. LinkedIn is its own island. A human pulling this together has to context-switch across all of them, know what’s relevant, and synthesize it into something useful - every time, for every call, for every rep.

An agent does all of that in under a minute. It knows which fields to pull, which transcript to summarize, which contacts to profile. It runs on a schedule triggered by calendar events. It delivers to Slack where your reps already live.

What changes when every rep is fully prepared?

The obvious benefit is better individual calls. The less obvious one is what happens to your pipeline data.

A prepared rep asks better questions. Better questions produce more complete qualification data. More complete qualification data means your AI agents - the deal risk agent, the forecast model, the routing logic - are working with accurate inputs instead of gaps.

The pre-call brief agent isn’t just making individual calls better. It’s feeding better information into everything downstream.

The pre-call brief is one piece of a broader shift toward autonomous GTM agents that execute without being asked. The same architecture powers deal risk detection, MEDDIC extraction, and pipeline intelligence.

Preparation used to be a competitive advantage for the rep who made time for it. Build this agent and it’s table stakes for everyone on your team.