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.

Why Your CRM Data Is Worse Than You Think

Your CRM data is bad. Not “could be better” bad. Structurally, operationally bad in ways that are silently undermining every decision you make about your pipeline.

The average B2B CRM has 30-40% of contact records missing critical fields like job title or company size. It has deals marked Active that haven’t had activity in weeks. It has close dates that slipped months ago but were never updated. It has duplicate contacts, orphaned companies, and custom properties that were populated once during an import and never touched again.

Every report you pull from that CRM is wrong. Every forecast is wrong. Every lead score built on that data is wrong. And the solution isn’t better rep discipline - it’s agents that fix the data continuously without requiring a human to do anything differently.

How bad is CRM data quality, really?

Pull up your HubSpot right now and run three checks.

Missing fields: Filter contacts by “Company Size is unknown” or “Job Title is empty.” In a typical CRM with 5,000+ contacts, you’ll find 1,500-2,000 with missing critical fields. These aren’t old dead leads. Some of them are contacts on active deals. Your reps are selling to people whose basic information you don’t have recorded.

Stale deals: Filter deals by “Last Activity Date is more than 21 days ago” AND “Deal Stage is not Closed Won/Lost.” In a pipeline of 60 deals, you’ll typically find 12-15 that haven’t had any activity in three weeks but are still showing as active pipeline. Your forecast includes these. It shouldn’t.

Slipped close dates: Filter deals by “Close Date is in the past” AND “Deal Stage is not Closed.” These are deals where the expected close date passed and nobody updated it. In some CRMs, this is 20-30% of open pipeline. Your forecast model is using these dates. The dates are fiction.

Every one of these problems makes your pipeline reporting less accurate, your lead scoring less reliable, and your forecasts less trustworthy.

Why can’t you fix CRM data with rep training?

You’ve tried. Every RevOps team has tried. You sent the email about filling in MEDDIC fields. You built the dashboard showing CRM completeness scores. You brought it up in the team meeting. Maybe compliance improved for two weeks. Then it went back to baseline.

This isn’t a discipline failure. It’s a system design failure.

Reps are paid to sell. Every minute they spend updating CRM fields is a minute they’re not spending on a call, a follow-up, or a demo. The incentive structure actively discourages the behavior you need. Threatening consequences for bad CRM hygiene doesn’t change the math - it just adds resentment.

The reps who do update their CRM consistently are the exception. Building your data strategy around the exception is how you end up with a CRM that’s 60% complete on a good day.

The fix isn’t changing human behavior. It’s removing humans from the data capture loop entirely.

How do AI agents fix CRM data?

Three types of agents handle the three categories of bad data:

Enrichment agents fix missing fields. When a new contact enters HubSpot - from a form fill, a meeting booking, a manual creation - an agent immediately enriches it. Job title, company size, industry, tech stack, funding stage, LinkedIn URL. It pulls from enrichment APIs, cross-references sources, and writes structured data back to the CRM. The contact record is complete before the rep even opens it.

For existing contacts with gaps, the same agent runs a backfill sweep - batching through your database, enriching records that are missing critical fields, flagging the ones where no data could be found. A one-time cleanup that takes hours instead of weeks.

Hygiene agents fix stale and inaccurate data. A deal that hasn’t had activity in 14 days gets flagged - not on a dashboard, but as a Slack alert to the AE with the specific gap. A close date that’s passed without an update triggers a task for the rep to either update it or close the deal. A contact whose job title changed on LinkedIn gets their CRM record updated automatically.

These agents run continuously. They don’t wait for the quarterly CRM audit. They catch problems the day they happen.

Deduplication agents fix structural data problems. Duplicate contacts, orphaned company records, contacts associated with the wrong company. The agent identifies likely duplicates based on name, email, and company matching, then either merges automatically (for clear matches) or flags for human review (for ambiguous cases).

What changes when your CRM data is actually clean?

Everything downstream gets better.

Lead scoring works. When firmographic data is complete and accurate, scoring models can actually distinguish fit from noise. Your MQL-to-SQL conversion rate improves because you’re routing on real data instead of gaps.

Forecasting works. When stale deals are flagged and close dates are current, your pipeline report reflects reality. Your CRO stops discounting the forecast by 30% because they’ve learned not to trust it.

AI agents work. Every AI agent you build - deal risk, pre-call briefs, competitive intelligence - is only as good as the data it reads. Clean CRM data isn’t just nice to have. It’s the prerequisite for everything else.

Rep trust improves. When reps open a contact record and the data is already there - enriched, current, complete - they start trusting the CRM instead of keeping their own spreadsheets. The system becomes useful instead of bureaucratic.

Clean data unlocks everything else. Compare AI vs manual CRM enrichment approaches, see how clean data powers AI lead scoring that actually predicts who will buy, and learn how MEDDIC agents populate qualification fields automatically.

Your CRM isn’t a data problem. It’s an infrastructure problem. Agents that enrich, monitor, and clean your data continuously are the infrastructure most teams are missing.