Automate Revenue Operations with Real AI.
Most RevOps tools show you dashboards. Von runs real datascience — 95% deal prediction accuracy, answers in minutes.
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See Von in action with your data.
Some of the teams that handed Von their backlog
Why RevOps teams are switching to Von.
Von is RevOps automation powered by real data science – not an LLM wrapper, not a dashboard with a new label. Ask a question in plain English and Von runs SQL, predictive models, and pattern analysis across your Salesforce, calls, and emails. Revenue operations answers in minutes.
RevOps automation that goes beyond dashboards.
“Von has completely changed how I manage my team…directly connected to our data sources. ”
Your RevOps platform, one question away.
Unlike legacy revenue intelligence tools, Von doesn’t require dashboard setup, report configuration, or analyst support.
How is this different from Clari or Gong?
You can — and it’ll work great for the first few questions. But you’ll hit three walls fast.
- Keyword vs. semantic. MCPs do keyword search. Ask “why are we losing to Competitor X?” and Claude only finds calls where someone literally said “Competitor X.” It misses every call where the prospect described their features without naming them.
- Context window. Claude has a ~1M token window. A single call transcript is 10–20K tokens. 200 deals × 3 calls = 5× more data than Claude can hold. It’ll analyze the first 10 and quietly ignore the other 190. And it won’t even tell you.
- Every user, every setup. No org-wide access, no shared memory. You manage dozens of individual setups instead of one platform.
Does this replace our BI tool?
You can — and it’ll work great for the first few questions. But you’ll hit three walls fast.
- Keyword vs. semantic. MCPs do keyword search. Ask “why are we losing to Competitor X?” and Claude only finds calls where someone literally said “Competitor X.” It misses every call where the prospect described their features without naming them.
- Context window. Claude has a ~1M token window. A single call transcript is 10–20K tokens. 200 deals × 3 calls = 5× more data than Claude can hold. It’ll analyze the first 10 and quietly ignore the other 190. And it won’t even tell you.
- Every user, every setup. No org-wide access, no shared memory. You manage dozens of individual setups instead of one platform.
What if our data is messy?
You can — and it’ll work great for the first few questions. But you’ll hit three walls fast.
- Keyword vs. semantic. MCPs do keyword search. Ask “why are we losing to Competitor X?” and Claude only finds calls where someone literally said “Competitor X.” It misses every call where the prospect described their features without naming them.
- Context window. Claude has a ~1M token window. A single call transcript is 10–20K tokens. 200 deals × 3 calls = 5× more data than Claude can hold. It’ll analyze the first 10 and quietly ignore the other 190. And it won’t even tell you.
- Every user, every setup. No org-wide access, no shared memory. You manage dozens of individual setups instead of one platform.