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.

The Leap: Why AI Models Can Finally Handle Real Sales Work

AI in sales has been promised for a decade and has mostly disappointed. Predictive lead scoring that didn’t predict. Chatbots that frustrated buyers. “Intelligence” tools that generated reports nobody read. The technology wasn’t ready. The models couldn’t do what sales work actually requires.

That changed. Not gradually - there was a specific capability leap in the last 18 months that made AI in sales work for the first time. Understanding what changed explains why the opportunity is now and why the tools from two years ago are already obsolete.

Why did previous AI in sales fail?

Previous-generation AI in sales was statistical, not reasoning. Machine learning models looked at historical patterns - which leads converted, which deals closed, which emails got replies - and applied correlations to new data. Lead A looks like Lead B, which closed. Therefore Lead A gets a high score.

This worked in narrow cases with clean data and stable patterns. It failed everywhere else.

It couldn’t handle nuance. A model that scored leads based on company size and industry couldn’t tell the difference between a VP of Sales actively searching for a solution and a student filling out a form to download a whitepaper. Both had the same firmographic profile. The model scored them the same.

It couldn’t read context. A call transcript is the richest data source in sales - what the prospect actually said, what concerns they raised, what they’re comparing you to. Statistical models couldn’t process unstructured conversation. They worked from CRM fields that were three layers abstracted from reality.

It couldn’t take action. Even when models generated a useful output - a risk score, a routing recommendation - they stopped at the insight. A human had to see it, interpret it, decide what to do, and do it. The gap between insight and action was never automated.

It couldn’t chain reasoning. Sales processes are multi-step. Qualifying a deal requires evaluating multiple factors, weighing them against each other, and making a judgment call that considers the full context. Statistical models evaluate one variable at a time. They don’t reason across a conversation, a deal history, and a market context simultaneously.

What specifically changed in AI models?

Three capabilities crossed a threshold that makes sales AI work for the first time.

Long-context reasoning. Current models can read an entire call transcript - 10,000+ words - understand the conversation, and extract specific data points: what the prospect said about budget, who they mentioned as decision-makers, what competitors they’re evaluating, what timeline they committed to. Not keyword matching. Actual comprehension of a sales conversation.

This single capability enables MEDDIC extraction, competitive intelligence capture, call summarization, and action item extraction - all from reading a transcript the way a human would, but for every call, every time, without forgetting.

Tool use and function calling. Models can now interact with external systems - reading from HubSpot, writing to Slack, calling enrichment APIs, creating tasks, updating fields. This is the capability that turns AI from an analysis tool into an execution layer. The model doesn’t just identify that a deal is at risk. It reads the CRM, evaluates the signals, and sends a Slack alert with context and a suggested action.

Previous models generated text. Current models take actions. That’s the difference between a report and an agent.

Multi-step planning. Current models can break a complex task into steps, execute each step, evaluate the result, and adjust the next step based on what they found. “Process this call transcript” becomes: read the transcript, extract MEDDIC data, check which fields are already populated in HubSpot, update only the fields that have new information, identify action items, create tasks with appropriate due dates, evaluate deal risk based on what was discussed, and alert the rep if risk signals are present.

This isn’t a predefined workflow. The model plans the steps, adapts based on what it finds at each step, and handles exceptions without a human scripting every branch in advance.

What does this mean for sales teams right now?

The practical implication: workflows that were impossible to automate two years ago are now buildable in days.

Automated qualification. Not lead scoring based on clicks - actual qualification based on what prospects say in conversations. An agent reads every call and populates qualification frameworks (MEDDIC, BANT, SPICED) from the conversation. The data is specific, current, and based on the prospect’s own words.

Context-aware deal management. An agent that doesn’t just check “days since last activity” but reads the last transcript, understands where the conversation left off, evaluates whether the silence is concerning or expected, and makes a judgment call about risk. That reasoning was impossible for previous models.

Natural language ops. A RevOps lead describes a workflow in English: “Every Monday, find deals that are at risk and send each rep their three most urgent deals with context and next steps.” An AI agent builds and executes that workflow. No code. No rule configuration. No workflow builder. The model understands the intent and implements it.

Adaptive processes. Instead of rigid automation that follows the same rules regardless of context, agents that adapt. A deal in fintech with a 6-month enterprise sales cycle gets different risk thresholds than a mid-market deal with a 30-day cycle. The agent knows the difference without being explicitly programmed for each case.

Why does the timing matter?

The capability leap happened. The adoption hasn’t.

Anthropic’s data shows AI agents in Sales & CRM represent only about 4% of agent deployments. Software engineering is at 50%. The technology that makes sales agents work is the same technology that’s already transforming software development - it just hasn’t been applied to sales at scale yet.

This is the window. The teams that build AI agent infrastructure for their sales operations now - while the market is still figuring out whether to buy another AI SDR tool - will have a compound advantage by the time everyone else starts building.

The models can finally do the work. The question is who builds the systems that put them to work first.

AI in sales didn’t fail because sales is too complex for AI. It failed because previous AI wasn’t complex enough for sales. That’s no longer true - and the teams that recognize it first won’t be waiting for the next generation of tools. They’ll be building now.