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 Does an AI-Native Sales Stack Actually Look Like?

Most sales stacks aren’t stacks. They’re collections. A CRM here. A transcription tool there. An enrichment vendor. A Slack workspace. A spreadsheet someone built for forecasting. Each tool does its job in isolation. Data moves between them manually - if it moves at all.

An AI-native sales stack is different. Every tool is connected. AI agents sit between the systems, reading data from one, making decisions, and writing actions to another. The stack doesn’t just store information. It acts on it.

Here’s what that architecture looks like, layer by layer.

What are the layers of an AI-native sales stack?

Five layers. Each one feeds the next.

Layer 1: Data sources. These are the tools that generate raw information. Your CRM (HubSpot, Salesforce) holds deal and contact data. Your call transcription tool (Gong, Fathom) captures conversation content. Your email platform holds communication history. Your calendar tracks meetings. None of this is new - you already have these tools. The difference is what sits on top of them.

Layer 2: Connection layer (MCPs). Model Context Protocol connections give AI agents read-write access to every data source. Instead of exporting CSVs and pasting into prompts, Claude reads directly from HubSpot, Slack, Gong, and Calendar through MCPs. This is the plumbing that makes everything else possible.

Layer 3: Intelligence layer (AI agents). Specialized agents that monitor, analyze, and act. A deal risk agent scans pipeline health hourly. A MEDDIC extraction agent reads every new transcript. An enrichment agent fills missing contact data. A scoring agent evaluates every inbound lead. Each agent has one job and does it continuously.

Layer 4: Execution layer (workflows). When an agent makes a decision, something happens. A Slack alert fires. A CRM field updates. A task gets created. A lead gets routed. This layer is built in n8n, Make, or native HubSpot workflows - the automation infrastructure that turns AI decisions into completed actions.

Layer 5: Delivery layer. Where your team actually sees the output. Slack messages, HubSpot task queues, email notifications. Not a new dashboard. Not a new tool. The systems your team already checks 50 times a day.

What does the data flow look like end to end?

Follow a single deal through the stack.

A new lead fills out a form. The enrichment agent fires immediately - pulling company size, industry, tech stack, and funding stage from external sources. Writes it all back to HubSpot. The scoring agent evaluates the enriched lead against your ICP model and historical close patterns. Score: 82. Above threshold. The routing agent checks territory fit, product match, and rep capacity. Assigns it to the right AE. A Slack message hits the rep’s DM with the lead details, score breakdown, and why this one matters. Total elapsed time: under 60 seconds.

The rep books a discovery call. Thirty minutes before the meeting, the pre-call brief agent fires. It reads the deal record, the original form submission, any email exchanges, and the contact’s LinkedIn profile. Delivers a brief to Slack: company context, likely pain points based on firmographic profile, and a suggested opening question.

After the call, the transcript hits Gong. The MEDDIC agents run in parallel - extracting metrics, identifying the economic buyer, capturing decision criteria, mapping the decision process. Fields populate in HubSpot. The competitive intelligence agent checks for competitor mentions. The action item agent creates follow-up tasks.

Two weeks later, activity on the deal slows. The deal risk agent catches it on its hourly scan - 12 days since last contact, champion hasn’t engaged in 8 days. Slack alert to the AE with context and a suggested re-engagement approach. The rep acts the same day instead of discovering the problem at next week’s pipeline review.

End of quarter, the forecast digest agent compiles all pipeline movement - deals that advanced, deals that slipped, deals that closed - and delivers it to the CRO Monday morning. No one built a report. No one compiled a spreadsheet. The forecast is current because the system is current.

Every step was automated. Every decision was made by an agent. Every action was executed without anyone asking.

How is this different from buying an “AI sales tool”?

Most AI sales tools do one thing. An AI SDR sends emails. A conversational intelligence tool summarizes calls. A forecasting tool predicts close dates. Each operates independently, using only its own data.

An AI-native stack isn’t a tool. It’s an architecture. The agents work together, sharing data through the CRM as the central hub. The enrichment agent’s output improves the scoring agent’s accuracy. The MEDDIC agent’s data improves the deal risk agent’s predictions. The deal risk agent’s alerts improve the pre-call brief agent’s context.

Each agent makes every other agent better. That’s the compound return you don’t get from point solutions.

What if you’re starting from a standard HubSpot setup today?

You don’t build all five layers at once. You build them in order.

Week 1: Connect HubSpot and Slack via MCPs. Build one agent - deal risk alerts or contact enrichment. Prove the value of a single automated workflow.

Week 2: Add Gong MCP. Build MEDDIC extraction agents. Now you have transcript-powered data flowing into your CRM automatically.

Week 3: Add calendar trigger for pre-call briefs. Build the scoring agent for inbound leads. Your stack is now actively helping reps on every call and every new lead.

Week 4: Add the forecast digest and routing logic. The full stack is operational.

Four weeks. No platform migration. No new tools for reps to learn. Just connections and agents layered onto the tools you already own.

The AI-native sales stack isn’t a product you buy. It’s an architecture you build - once - and it compounds from there.