What AI tools do you use?

We build on top of the tools you already have - HubSpot, Salesforce, n8n, Make - and layer in AI from OpenAI, Anthropic, and others where it makes sense. No proprietary platform, no new vendor to manage.

Is this actual AI or just rule-based automation?

Both, depending on the job. Simple routing and alerts are rule-based - that's the right tool for that. Scoring, summarization, intent detection, and research use real LLMs. We match the approach to the problem.

Do I need to replace my CRM?

No. We build on top of what you have. Whether that's HubSpot, Salesforce, or something else, we connect to it - we don't replace it.

Do I need a technical team to manage this?

No. The automations run on their own. We build it, hand it off, and your team uses the outputs - alerts, scores, summaries - without touching the infrastructure.

How do you handle our data?

Your data stays in your stack. We don't store it, sell it, or route it through proprietary systems. The pipelines we build connect your existing tools directly.

What does onboarding actually look like?

A 30-minute discovery call, then we scope the work and send a proposal. Once you're in, we go heads-down - no lengthy kickoffs or status meetings. You see working software fast.

What if the AI gets it wrong?

It will sometimes, especially early. That's why we build human review steps where it matters and tune based on your data. Accuracy improves over time - it's not set-and-forget.

How is this different from HubSpot's or Salesforce's native AI?

Native AI works within the walls of that one tool. We connect your entire stack and build logic that runs across it - enrichment, scoring, alerts, and summaries that pull from multiple sources and trigger actions anywhere.

Topic

AI RevOps

RevOps is fundamentally an operational role - and most of that operation is repeatable, data-driven work that AI handles well. Lead routing, CRM hygiene, pipeline reporting, deal alerts, forecast generation - none of these require human judgment on every instance. They require consistent execution of defined logic at scale. That's exactly what AI agents do.

The question isn't whether AI can handle RevOps work. It's which RevOps work to hand off first, how to evaluate whether it's actually working, and what "autonomous" really means in a sales context (spoiler: it doesn't mean unsupervised).

For teams scaling past what one RevOps hire can handle, or teams that can't justify the headcount at all, AI gives you execution capacity that compounds. The first agent runs in the background. The second one builds on what the first learned. By month six, you have infrastructure that a three-person RevOps team would have taken two years to build.

These guides cover the strategic and operational side: when to use AI vs. traditional automation, how to build your first agent without an engineering team, how to evaluate whether your AI stack is actually working, and how revenue teams are running leaner operations without sacrificing coverage.

AI RevOps

HubSpot AI for RevOps: What's Native, What's Missing, and What to Build

Do You Need a GTM Architect Role?

Best AI Tools for RevOps in 2025 (Actually Useful Ones)

Can AI Reduce Your RevOps Hiring Costs?

Why AI Can Finally Handle Real Sales Work

How to Build Your First AI Sales Agent (Step-by-Step)

How to Know if Your Sales AI Is Actually Autonomous

Where Is AI ROI Higher - Sales Ops or Outbound?