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.

How to Automate MEDDIC Qualification With AI Agents

MEDDIC is the best sales qualification framework most teams have. It’s also the one with the worst data in the CRM. Not because reps don’t understand it - because filling in six structured fields after every call is 15 minutes of admin that competes with sending the follow-up email, prepping for the next call, and everything else that feels more urgent.

The fix isn’t training. It’s not enforcement. It’s an agent for each MEDDIC field that reads the call transcript and populates the data automatically. The rep reviews in 30 seconds instead of writing for 15 minutes. The data goes from 20% complete to 90% complete overnight.

Why is MEDDIC data always incomplete?

MEDDIC - Metrics, Economic Buyer, Decision Criteria, Decision Process, Identify Pain, Champion - requires reps to translate an unstructured 45-minute conversation into six structured fields. After every call. For every deal.

The information exists. The prospect said their current process costs them $400K per quarter - that’s Metrics. They mentioned their VP of Finance needs to approve anything over $50K - that’s Economic Buyer. They said they need SOC 2 compliance and Salesforce integration - that’s Decision Criteria.

All of it was said on the call. All of it is in the transcript. None of it ends up in the CRM because nobody has time to go back through a transcript, find the relevant moments, extract the data points, and type them into HubSpot fields.

So the fields stay empty. And every downstream system that depends on MEDDIC data - deal scoring, forecast accuracy, pipeline reviews, coaching - operates on incomplete information.

The problem was never the framework. It was the data entry.

How does an agent swarm handle MEDDIC extraction?

Instead of one agent trying to extract everything, you build a specialized agent for each MEDDIC field. Each agent reads the same transcript but looks for different signals.

Metrics agent. Scans every transcript for quantified business impact. Dollar amounts, percentages, time lost, headcount affected, revenue at risk. “We’re losing about $400K a quarter to manual processing” - extracted, structured, written to the Metrics field in HubSpot. If no metrics were mentioned, the agent flags the gap so the rep knows to ask on the next call.

Economic Buyer agent. Listens for power signals. Who approves the budget? Whose name comes up when the rep asks “who else needs to sign off?” Who does the prospect defer to when discussing pricing or timeline? The agent maps these signals to contact records and flags whether the economic buyer has been directly engaged.

Decision Criteria agent. Extracts the specific requirements the prospect named - not the rep’s interpretation, the actual words. “We need it to integrate with Salesforce.” “Has to be SOC 2 compliant.” “Needs to handle 10,000 contacts minimum.” Each criterion captured verbatim and written to the field.

Decision Process agent. Looks for process signals. How many stages in their evaluation? Who’s involved at each stage? What’s the timeline? Is there a procurement review? A security audit? A legal review? The agent maps the buying process as the prospect describes it across multiple calls.

Identify Pain agent. Extracts the stated pain points - not features the prospect liked, but problems they described. “Our reps spend 4 hours a week on data entry.” “We have no visibility into pipeline health.” “We lost three deals last quarter because nobody noticed they went dark.” Pain in the prospect’s own words.

Champion agent. Identifies which contact is actively selling internally on your behalf. Who’s scheduling meetings with other stakeholders? Who’s sharing materials? Who’s pushing for a faster timeline? Champion identification is harder to automate than the other fields - the agent flags likely champions based on engagement patterns rather than making a definitive call.

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 with draft data. They review, correct if needed, and move on. Thirty seconds instead of fifteen minutes.

What changes when MEDDIC data is actually complete?

Deal scoring becomes real. A deal risk model that checks MEDDIC completeness can distinguish between “this deal is early” and “this deal is missing critical qualification data at a late stage.” When the fields are actually populated, the model can assess deal health based on what’s known - not just what stage it’s in.

Pipeline reviews get specific. Instead of “where are we on Acme?” the manager can say “the Economic Buyer hasn’t been engaged and we’re in Proposal stage - that’s the risk.” MEDDIC data turns vague reviews into focused strategy conversations.

Forecasting improves. Deals with complete MEDDIC data close at higher rates and with more predictable timelines. When your MEDDIC fields are populated across the pipeline, your forecast stops being a guess and starts being a model.

Coaching has something to work with. A manager can look at a rep’s MEDDIC data across their deals and see patterns. This rep consistently misses Metrics. This one never identifies the Economic Buyer early enough. That’s coachable. Empty fields aren’t coachable.

What if your team barely uses MEDDIC fields today?

That’s most teams. The fields exist in HubSpot. They were set up with good intentions. They’re 15-25% populated across the pipeline, and the data that is there is often vague or outdated.

Don’t start with a mandate to fill them in. Start with the agent swarm. Connect your call transcription tool (Gong, Fathom, Fireflies) to an extraction pipeline. Let the agents populate the fields from transcripts for two weeks without telling anyone. Then show your team the data.

When a rep sees their MEDDIC fields filled in accurately - with the prospect’s actual words, from the actual call - the conversation shifts. They’re not being asked to do data entry. They’re being asked to review data that’s already there.

Adoption follows accuracy. Build the agent, prove the data quality, and the team will trust it.

MEDDIC extraction is one part of a broader shift toward autonomous GTM agents that execute without being asked. Complete MEDDIC data also powers the pre-call brief agent and feeds into AI deal risk detection that catches dying deals early.

Your sales methodology is only as good as the data behind it. MEDDIC agents make the data real - not by changing rep behavior, but by removing reps from the data capture loop entirely.