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.

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

Most “best AI tools for RevOps” lists are vendor roundups with affiliate links. This isn’t that. These are the tools that actually show up in working RevOps stacks - the ones doing routing, enrichment, pipeline monitoring, and reporting without requiring a data science team to maintain them.

CRM: HubSpot or Salesforce (still)

AI doesn’t replace your CRM. It reads from it and writes to it. HubSpot has improved its native AI features meaningfully - predictive lead scoring, deal summaries, AI-assisted sequences - and for teams under 200 reps, it’s usually sufficient. Salesforce has more horsepower for complex enterprise orgs but also more overhead.

The choice matters less than the data quality inside it. AI tools are only as good as the CRM data they’re working with.

Enrichment and list building: Clay

Clay has become the default enrichment layer for RevOps teams that want to build custom data pipelines. It connects to 100+ data sources, runs AI steps inline, and writes structured output to your CRM. If you’re scoring leads against multiple signals - funding, headcount growth, tech stack, job postings - Clay is the tool that makes that possible without custom engineering.

Call intelligence: Gong or Fathom

Gong is the enterprise standard. It captures calls, transcribes them, surfaces deal risk signals, and has a robust API for pulling data into other tools. If you’re a RevOps leader managing 20+ reps, Gong’s analytics are worth the price.

Fathom is the right call for smaller teams. It’s free for individuals, inexpensive for teams, and produces clean transcripts you can feed into downstream AI workflows. It doesn’t have Gong’s revenue intelligence layer, but for teams that just need transcripts to work with, it’s the most practical option.

Workflow automation: n8n or Make

This is the connective tissue layer - the thing that fires when a lead comes in, routes it, enriches it, scores it, and updates the CRM. n8n is open source, more flexible, and better for complex multi-step logic. Make (formerly Integromat) has a better visual interface and is easier for non-technical ops people to maintain.

Zapier works too but gets expensive fast and has limitations on data transformation that matter when you’re doing real AI workflows.

AI reasoning layer: Claude

When your workflows need to read a call transcript and extract structured data, summarize a deal’s history, or generate a rep brief from scattered CRM fields - you need a model with strong instruction-following and long context. Claude (via API) is what most production RevOps AI workflows are running on for these tasks. It connects cleanly to n8n, Make, and direct API calls.

Forecasting: your CRM + a model

Purpose-built AI forecasting tools exist but most are overpriced for what they do. A well-configured HubSpot or Salesforce forecast with deal health signals and a simple model reading pipeline velocity will outperform a $50k/year overlay tool for most teams. The better investment is fixing your CRM data quality so any model can read it accurately.

What to skip

AI SDR tools, AI email writers, and “AI-powered outreach” platforms. The ROI on these has been consistently disappointing as reply rates have dropped and buyers have gotten better at spotting AI-generated email. The tools worth investing in are the ones doing operational work - routing, enrichment, intelligence - not the ones trying to replace human judgment in outbound communication.

The RevOps AI stack that works isn’t exotic. It’s a clean CRM, a good enrichment layer, call intelligence, a workflow engine, and a model smart enough to reason across all of it. If you’re thinking about whether AI can replace a RevOps hire, this is the stack that makes that case.