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 Evaluate AI for Your Sales Stack

The AI sales tools market is full of noise. Every vendor has “AI-powered” somewhere in their pitch deck. Most of them are rule engines with a language model generating summaries on top. The gap between what’s marketed and what’s delivered has never been wider.

Here’s how to evaluate AI for your sales stack without buying something that’ll sit unused in six months - and how to decide whether to buy a tool or build the capability yourself.

What should you actually look for in AI sales tools?

Does it write back to your CRM? This is the single fastest way to separate real from fake. If the tool generates insights in its own dashboard but doesn’t update HubSpot or Salesforce fields, create tasks, or trigger workflows in your actual systems - it’s a read-only layer. You’re adding a new screen to check, not removing work. The best AI operates inside your existing tools, not beside them.

Does it act on triggers without being asked? Open the tool. Close the tool. Wait a week. Did anything happen? If the answer is no - if the AI only works when someone remembers to open it and ask a question - it’s not autonomous. It’s a chatbot with sales data access. Real AI agents run on schedules, webhooks, and events. They monitor, decide, and act without a human initiating every interaction.

Can you explain what the AI actually does? Ask the vendor: “What specific model are you using, on what data, to produce what output?” If the answer is vague - “our proprietary AI analyzes your pipeline” - be skeptical. You should be able to trace the path from input (CRM data, transcripts, signals) to decision (scoring, risk assessment, routing) to output (field update, Slack alert, task creation).

Does it get better over time? A static rule engine produces the same quality output on day 300 as day 1. AI that learns from your data - your close patterns, your rep performance, your pipeline velocity - should produce measurably better output after three months than after one. Ask how the model improves with usage. If there’s no answer, it’s not learning.

What are the red flags in AI sales tool demos?

“AI-powered insights” with no follow-through. The demo shows a beautiful dashboard with risk scores and engagement metrics. Ask: “What happens after the insight surfaces?” If the answer is “your team takes action” - that’s a notification system, not an AI agent. You’re buying a smarter alert, not reduced workload.

Vague ROI claims. “Companies using our tool see 30% more pipeline.” More pipeline from what? Better qualification? More outbound? Magic? Demand specifics. Which metric improved? Over what timeline? With what implementation effort? Broad ROI claims without mechanism are marketing, not evidence.

Requires reps to change behavior. Any tool that needs your reps to open a new application, learn a new interface, or add steps to their workflow has an adoption problem built in. The best AI in sales is invisible to reps. It runs in the background, updates their CRM, delivers to their Slack. If the vendor’s success depends on your team using a new tool, factor in the realistic adoption rate (hint: it’s lower than you think).

“Just connect your CRM and it works.” Real AI implementation requires configuration. What fields matter for your scoring? What’s your sales cycle? What does your ICP look like? A tool that claims zero-setup AI is either applying generic logic that won’t fit your business or is oversimplifying what it actually takes to get value.

No data export or ownership. If you can’t export the models, the scores, or the workflow logic, you don’t own anything. You’re renting a black box. When the vendor raises prices or shuts down, you start over.

Should you buy a tool or build it yourself?

This is the question most teams skip - and it’s the most important one.

Buy when: The capability requires proprietary data you can’t access otherwise (intent data platforms, some enrichment databases). The integration is genuinely complex and maintained by a team (enterprise Salesforce connectors with ongoing API changes). The tool does something fundamentally different from what you could build (true ML models trained on cross-customer data).

Build when: The workflow is specific to your process and a generic tool won’t fit. The “AI” in the tool is essentially “connect CRM data to an AI model and take an action” - which is exactly what an agent built on your stack does. The tool costs $500+ per month for logic you could implement in a few days. You want to own and modify the infrastructure as your process evolves.

The honest reality: Most AI sales tools in the $200-2,000/month range are doing things you can build with your CRM, an automation platform (n8n or Make), and an AI model (Claude or GPT). Deal risk alerts, lead scoring, CRM enrichment, pipeline digests, pre-call briefs - these are agent patterns, not proprietary technology. The vendor’s value is that they’ve already built it. The tradeoff is you’re paying monthly for something you don’t own and can’t customize.

For most teams with any ops capability, the right answer is: build the core agent infrastructure yourself (you’ll learn your pipeline better in the process), and buy only where proprietary data or genuine technical complexity justifies the spend.

How do you evaluate whether your current AI tools are actually working?

Run this audit on every AI tool in your stack:

Usage check. How many people on your team used this tool in the last 30 days? If less than half your team is using it, the tool has an adoption problem that no feature update will fix.

Action check. Did the tool’s output result in a specific action in the last week? Not “someone looked at the dashboard” - an actual task completed, deal updated, follow-up sent, risk mitigated. If the tool surfaces insights that nobody acts on, it’s adding noise, not value.

Replacement check. If this tool disappeared tomorrow, what would break? If the answer is “we’d go back to doing it manually” - fine, the tool has value. If the answer is “nothing, nobody would notice” - cancel it.

Build check. Could an AI agent on your existing stack do what this tool does? If the tool is essentially “read CRM data, apply logic, send alert” - that’s an agent you can build in days and own forever.

What does the ideal evaluation process look like?

Week 1: Define the problem. Not “we need AI in our sales stack.” What specific workflow is broken? What manual process eats the most time? What data is missing? What decisions are being made on bad information? Start from the problem, not the technology.

Week 2: Build it yourself. Take the top problem and build an agent for it. Deal risk alerts. Lead scoring. CRM enrichment. Use your CRM, an automation platform, and an AI model. Spend 2-3 days. See if it works.

Week 3: Evaluate if you need more. If the agent you built solves 80% of the problem, you probably don’t need a vendor. If there’s a genuine gap - proprietary data, complex ML, cross-customer intelligence - now you know exactly what you’re buying and why.

This approach costs you one week of effort and saves you from a 12-month contract for a tool that sits unused after month two.

See why AI belongs in operations, not outbound, what an AI-native sales stack looks like when you build it right, and why most sales automation tools are becoming redundant.

The best AI in your sales stack might not be a tool you buy. It might be an agent you build - one that does exactly what your team needs, runs on your data, and gets better every quarter because you own it.