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Deal inspection

Analyze [Account] — pull all calls, emails, deal history, and contacts. What's the full story and what should I be worried about?

Forecast call prep

Prep me for my forecast call — top deals at risk, biggest movers this week, and what I need to escalate.

Rep coaching brief

Analyze [rep name]'s last 10 calls. What are they doing well and where can they improve? Give me specific coaching points.

QBR deck

Build me a QBR deck for [Account] — deal history, milestones, engagement trends, open risks, and expansion opportunities.

Team meeting agenda

Create a 30-minute team meeting agenda based on this week's pipeline changes, wins, and risks. Include talking points.

Quarterly performance report

Build me a quarterly performance report for my team — pipe gen, close rate, win rate, avg deal size, cycle time. Compare vs. last quarter. As a deck.

Good rep vs. bad rep

Compare my top 3 reps vs. bottom 3 by win rate. What are the top performers doing differently in their calls and deal execution?

Stale deal cleanup

Find all deals on my team that haven't had activity in 14+ days. For each one, tell me the last touchpoint and recommend next steps.

“Von easily allows me to get as much or as little detail as I need in terms of — pipeline health, deal risks, rep activity, even what was said on the last customer call — in minutes.”
Jesse Dahms
Sales Manager
Why not just use Claude or ChatGPT?

Von is built specifically for the needs of revenue teams

You already pay for general-purpose AI tools, so you could probably build this yourself. But those tools weren’t built for the specific needs of revenue teams.

Explore Build vs. Buy
Claude / ChatGPT
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System connections
Each user sets up their own connections. Centralized access requires engineering to build and maintain an MCP server.
Admin connects once. Native, one-click integrations available to the entire org immediately.
Business context
Doesn’t know your fields, fiscal year, or churn definition. You write and maintain skill files for every use case.
Von builds a context graph in days. No skill files. Corrections from any user apply to everyone via org-wide memory.
Context limits
Even with a million-token window, 50 deals and hundreds of calls bury the middle. The model samples and gives you plausible guesses, not the full picture.
Pre-processes all unstructured data. Analyzes 10,000+ calls in a single query. Real answers, not sampled guesses.
Search quality
Limited to keyword search. Questions like “Why are we losing to competitor X?” return shallow answers.
True semantic search across all data. Deep answers on competitive losses, rep performance, and discovery patterns.
Enablement
Making it work for each persona is entirely on RevOps to figure out and maintain.
Von knows what works for every persona across 50+ companies. No training required from your side.
Timeline
3-6 months of engineering. You eventually assign 2-3 engineers full-time. Results uncertain.
Ready for the entire GTM team from day one. Productivity boost within two weeks.

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