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 Agents That Don't Wait to Be Asked

There are two kinds of AI in a GTM stack. The kind you ask questions, and the kind that acts without being asked. Most teams have the first kind. The second kind is where the real operational change happens.

Here’s what autonomous GTM agents actually look like - and what they do that no human ops team can match at scale.

Why doesn’t reporting AI actually change anything?

A deal review AI that summarizes your pipeline is useful. Once. Then someone has to read it, decide what to do, and go do it. The bottleneck isn’t information - it’s execution.

Autonomous agents remove the human in the middle. They don’t generate a report for someone to act on. They take the action. They read the transcript, extract the signal, update the CRM field, trigger the next workflow - without anyone asking them to.

The distinction: AI that tells you something happened versus AI that responds to it.

How do autonomous agents handle MEDDIC qualification?

MEDDIC is the clearest example of where autonomous agents pay off immediately.

MEDDIC - Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, Champion - is a sales qualification framework. Every serious B2B sales org uses some version of it. Almost none of them have accurate MEDDIC data in their CRM, because filling in those fields requires reps to translate a 45-minute call into structured data after the fact. It rarely happens. When it does, it’s incomplete.

The fix isn’t better rep training. It’s a specialized agent for each MEDDIC field.

The Metrics agent listens to every call transcript and looks for one thing: quantified business impact. Numbers. Dollar amounts. Time lost. Percentage drops. “We’re losing about $400K a quarter to this” - extracted, structured, written to the Metrics field in HubSpot automatically.

The Economic Buyer agent listens for power signals. Who approves budget? Whose name comes up when the rep asks “who else needs to be involved?” It maps that to a contact record and flags whether the economic buyer has been directly engaged yet.

The Decision Criteria agent extracts what the prospect said they need in order to buy - the specific requirements they named on the call. Not a rep’s interpretation. The actual words.

Each agent runs in parallel the moment a transcript is available. By the time the rep opens HubSpot after the call, the MEDDIC fields are populated. They review, correct if needed, move on.

Your sales methodology is only as good as the data behind it. These agents make the data real.

How does a deal risk agent work in real time?

A separate agent runs continuously - not triggered by calls, but by time and inactivity.

It monitors every open deal for a set of risk signals: last activity date, contact engagement drop-off, champion job change, competitor mention frequency, deal age versus average sales cycle. When a deal crosses a risk threshold, it doesn’t create a dashboard entry. It creates a task, assigns it to the AE, and sends a Slack message with the specific signal that triggered it.

“Deal with Acme Corp: no activity in 18 days, champion Sarah Johnson changed roles 4 days ago. Suggested action: re-engage with new stakeholder mapping.”

The rep doesn’t need to remember to check. The agent checks. The agent acts.

How do agents capture competitive intelligence automatically?

Every call where a competitor is mentioned is a data point most orgs lose. Reps might note it in the CRM. Usually they don’t.

A competitive intelligence agent reads every transcript, flags competitor mentions, extracts the context - what the prospect said about them, how they compared, what objection was raised - and writes it to a Competitor field in HubSpot. It also routes a summary to the product or marketing team when a competitor comes up in more than three deals in a week.

Your competitive positioning improves automatically as your team has more conversations.

What does this agent architecture look like?

These aren’t three features of one AI tool. They’re specialized agents, each trained for a specific job, running in parallel on the same inputs.

The transcript comes in. The Metrics agent reads it for numbers. The Economic Buyer agent reads it for power signals. The Decision Criteria agent reads it for stated requirements. The deal risk agent reads every open deal for decay signals. Each writes its output back to a specific CRM field. Each triggers its own downstream workflow.

No single agent is trying to do everything. That’s the architecture that works.

What changes for your ops team when agents execute?

The RevOps team stops spending time on CRM hygiene enforcement. Stop chasing reps to fill in fields. Stop auditing pipeline quality manually. Stop building reports that someone has to read and interpret and act on.

The agents handle the structured data capture. The agents flag the risk. The agents trigger the workflows.

Your team focuses on the exceptions - the deals where something unusual is happening that a rule-based system wouldn’t catch. The judgment calls. The interventions that actually require a human.

Learn why AI belongs in operations, not outbound, how to replace your weekly pipeline review with real-time intelligence, or what Claude actually is for GTM teams beyond the chat window.

The shift from AI that informs to AI that executes is not incremental. It’s the difference between a tool your team uses and an ops layer that runs your process.