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 Build an AI Lead Scoring Agent in HubSpot

Most lead scoring in HubSpot is astrology. Someone filled out a form - 10 points. They visited the pricing page - 15 points. They’re a VP - 20 points. Add it up, call it a score, route it to a rep.

The problem isn’t the math. It’s the inputs. Form fills and page views tell you someone is interested. They don’t tell you whether they’ll close. An AI lead scoring agent uses different inputs entirely - firmographic fit against your actual ICP, behavioral patterns from deals that actually closed, and enrichment data your reps never had time to look up.

The result: fewer leads routed, but dramatically higher conversion on the ones that are.

Why doesn’t traditional HubSpot lead scoring work?

Traditional scoring assigns points to actions. Download a whitepaper: +10. Open an email: +5. Visit the website three times: +15. Cross a threshold, become an MQL, get routed to sales.

The flaw is that these actions measure engagement, not fit. A student researching for a thesis will score higher than a CFO who visited your pricing page once and left. A competitor’s analyst downloading every resource you publish will look like your hottest lead.

Point-based scoring also can’t learn. It scores lead #5,000 exactly the same way it scored lead #1. It doesn’t know that companies with 50-200 employees in fintech close at 3x the rate of companies with 500+ in healthcare. It doesn’t know that leads who come through paid search convert at half the rate of referrals. That pattern is in your data. Traditional scoring can’t see it.

AI scoring can.

How does AI lead scoring actually work?

An AI lead scoring agent evaluates every new lead against a model trained on your historical close data - what your actual won deals look like.

Firmographic fit. Company size, industry, tech stack, funding stage, geography. Not just “are they in our target market” but “how closely do they match the profile of companies we’ve actually closed.” The agent enriches this data automatically when a lead enters the CRM - pulling from Clearbit, Apollo, or other sources - so it’s scoring on complete information, not whatever the lead filled in on a form.

Behavioral signals. Not just page views - the specific patterns that correlate with closed deals. Maybe leads who visit your integrations page and then your pricing page within 48 hours close at 4x the rate. Maybe leads who engage with three emails in a week but don’t book a meeting need a different routing path. The AI model finds these patterns in your data. A point-based system never will.

Historical match. The agent compares every new lead against your closed-won deals from the last 12-18 months. It’s not asking “did they take the right actions?” It’s asking “do they look like the deals we actually won?” That’s a fundamentally different question with a fundamentally more useful answer.

The output is a score - but unlike traditional scoring, the score actually means something. A high score means “this lead matches the pattern of deals we close.” Not “this lead clicked a lot of things.”

What does the agent architecture look like?

Trigger: New contact or company created in HubSpot (or existing contact hits a re-score event like a form submission or meeting booked).

Enrichment step: Agent calls enrichment APIs to fill missing firmographic data - company size, industry, tech stack, funding round, employee count. Writes back to HubSpot properties.

Scoring step: AI model evaluates the enriched lead against your ICP model and historical close patterns. Produces a 0-100 score plus a confidence level and the top 3 factors driving the score.

Action step: Score is written to a custom HubSpot property. If the score exceeds your threshold, the lead is routed to the right rep based on territory, product fit, and rep capacity. A Slack message alerts the rep with the lead details and why the score is high. If below threshold, the lead enters a nurture workflow instead of wasting a rep’s time.

The whole process runs in seconds. The rep gets a Slack message with a scored lead, enriched data, and routing rationale before they even know the lead exists.

What if you’re running HubSpot with default lead scoring right now?

You’re probably routing too many leads to sales. Your reps are probably spending time on leads that will never close - and they know it, which is why they don’t trust the scores and cherry-pick from the queue anyway.

Your conversion rate from MQL to SQL is probably below 30%. That means 70% of the leads you’re sending to reps are wasting their time. Not because the leads aren’t interested - because interest isn’t the same as fit.

An AI scoring agent typically cuts routed volume by 40-60% while increasing conversion rates on routed leads by 2-3x. Fewer leads, better leads, more closed deals. Your reps stop complaining about lead quality because lead quality actually improves.

The build takes 3-5 days: enrichment pipeline, scoring model trained on your historical data, routing logic, Slack delivery. No new tools for your reps to learn. The score shows up in HubSpot. The alert shows up in Slack. Everything else is invisible.

Once your scoring works, the next step is AI-powered lead routing that replaces round-robin. Better scores also depend on clean CRM data that agents maintain automatically, and here’s how AI enrichment compares to manual and bulk approaches.

Lead scoring should tell you who will buy, not who clicked. AI is the only way to make that distinction at scale.