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ENTRY TYPE · framework

The RevOps tech stack

Last updated 2026-05-02 RevOps

A RevOps tech stack is the set of tools that run a B2B SaaS company’s revenue operations — CRM, sales engagement, data enrichment, conversation intelligence, automation, and now AI orchestration. The right stack depends on stage and motion; the categories are the same.

The seven layers

Every modern B2B SaaS RevOps stack has tools in seven categories. Some companies bundle multiple layers into a single vendor; others buy best-of-breed in each.

1. CRM (the system of record)

The single source of truth for accounts, contacts, opportunities, and pipeline. Either HubSpot (sub-$50M ARR, mid-market norm) or Salesforce ($50M+ ARR, enterprise default). Picking one and committing is more important than which one — migrations are expensive.

2. Data enrichment (the inputs)

Where lead and account data comes from. Clay is the leverage layer — orchestrates ZoomInfo + Apollo + LinkedIn + 100 niche providers + AI columns into one substrate. Use Clay alongside HubSpot/Salesforce, not instead of them.

3. Sales engagement (the execution)

Sequences, dialer, email tracking. Apollo for sub-30-rep teams (free tier + $49/seat); Outreach or Salesloft for 30+ reps where complexity justifies the upgrade.

4. Conversation intelligence (the listening layer)

Recording, transcribing, analyzing customer conversations. Gong is the default for $5M+ ARR; lighter alternatives (Fathom, Granola, Otter + Claude analysis) work for smaller teams. Pairs with the engagement layer.

5. Automation (the glue)

Connects everything to everything. n8n for AI-native and self-hosted; Zapier/Make for simpler use cases. n8n’s MCP support and AI agent nodes make it the obvious choice for new builds.

6. AI orchestration (the reasoning layer)

Where structured analysis, drafting, and agentic workflows happen. Claude is the default — Skills, MCP, long context, agent mode. Pair with Cursor for the technical end (GTM engineers writing code).

7. Analytics & forecasting (the read layer)

How the team sees what’s happening. Native CRM reporting + a BI tool (Hex, Mode, Looker) on top of the data warehouse. Many teams use Gong’s forecast as a complement to rep-submitted forecasts.

The reference stacks

Sub-$10M ARR (5-15 reps)

CRM:                    HubSpot
Enrichment:             HubSpot built-in (start) → Clay (when budget allows)
Sales engagement:       Apollo
Conversation intel:     Fathom or Granola + Claude analysis
Automation:             n8n or Zapier
AI orchestration:       Claude Pro
Analytics:              HubSpot reporting

Total ~$200-400/seat/month all-in.

$10-50M ARR (15-50 reps)

CRM:                    HubSpot or Salesforce
Enrichment:             Clay + ZoomInfo
Sales engagement:       Apollo (15-25 reps) or Outreach/Salesloft (30+)
Conversation intel:     Gong
Automation:             n8n
AI orchestration:       Claude Pro/Team + Cursor for GTM engineers
Analytics:              CRM native + Hex on warehouse

Total ~$500-1000/seat/month all-in.

$50M+ ARR (50+ reps)

CRM:                    Salesforce
Enrichment:             Clay + ZoomInfo + a vertical-specific provider
Sales engagement:       Outreach or Salesloft + dialer
Conversation intel:     Gong + Forecast
Automation:             n8n self-hosted + custom code
AI orchestration:       Claude Enterprise + Cursor for GTM engineers
Analytics:              Salesforce + Looker/Hex + Gong Forecast

Total ~$1000-2000/seat/month all-in.

Building order

Don’t build all seven layers at once. The order:

  1. CRM first. Everything plugs into it.
  2. Sales engagement second. The execution layer.
  3. AI orchestration third. The leverage starts here.
  4. Enrichment fourth. Quality of data inputs.
  5. Conversation intelligence at 25+ reps.
  6. Automation as the orchestration grows.
  7. Analytics last — once the rest is producing real signal.

Skipping any of these is fine if your motion doesn’t need it (PLG companies often skip sales engagement; very small teams can defer conversation intelligence). Building them out of order — analytics before signal sources, conversation intelligence before sales engagement — wastes budget.