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Ramp VoC Agent Overview
How Ramp built a composite AI system that turns raw customer signals into actionable product and sales intelligence.
What It Is
- Not a single tool — a composite system spanning multiple AI agents, data platforms, and third-party tools
- Demoed publicly by Geoff Charles (CPO) — described as achieving "8 days of research in 8 minutes"
- Answers strategic questions: Why did customers churn? Why are they angry in support? Why did they pick a competitor?
Three Core Internal Agents
| Agent | Purpose |
|---|---|
| Voice of Customer | Synthesizes feedback across Gong, Zendesk, Salesforce into insights |
| Analyst | Natural language data queries ("get insights using English") |
| Inspect | Internal coding agent (30% of PRs — separate from VoC) |
The Stack
- LLM: Anthropic Claude (primary) via Claude Code and Snowflake Cortex
- Data Platform: Snowflake + dbt + Hightouch CDP — "low petabytes" of unstructured data
- Agent Orchestration: LangChain connecting to Salesforce, Gong, BigQuery, Apollo, Gmail, LinkedIn, Exa
- Sales Intelligence: Actively.ai (Gong + Salesforce + third-party signals)
- Revenue Ops: Momentum.io (auto-populates CRM from call transcripts)
- Customer Support: Sierra AI (knowledge management feedback loops)
Impact
- 500+ features shipped in 2025 with only 25 PMs
- 80%+ of sales workflows run through the internal platform
- 50% of code written by AI (targeting 80%)
- Every function required to onboard and work with agents
Core Philosophy
"The moat is proprietary data. Not the model. Not the prompt." — Parth Gujare, Senior PM at Ramp
The AI flywheel: more customers using Ramp -> more spending data -> better insights -> more customers. AI commoditizes data interpretation, not data access.
Key Takeaways
- VoC is a system-of-systems, not a single agent — it spans data infrastructure, agent orchestration, and multiple SaaS integrations
- Data infrastructure (Snowflake CDP) must exist before agents can be useful
- Ramp embeds VoC directly into PM and sales workflows rather than building standalone dashboards
- Small teams win — 2-4 person GTM engineering teams with single ownership outperform large projects
- Focus on automation metrics (tasks completed), not engagement metrics (sessions)
Sources
- Behind the Craft — Geoff Charles Interview
- Sacra Research — Ramp AI
- LangChain Breakout Agents — Ramp
- American Banker — Ramp Data Cloud