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Demo prep

I have a demo with [Account] tomorrow. Pull everything — their pain points, what we discussed on the last call, who's involved, and what competitors they're evaluating.

Technical scoping doc

Auto-generate a technical scoping document from the discovery call with [Account]. Include requirements, integrations, and timeline.

SC team performance

Build me a performance report for my SC team — deals supported, win rate when SC was involved vs. not, and average cycle time by SC. As a spreadsheet.

Competitive win/loss

Analyze all deals we've won and lost against [Competitor] in the last 6 months. What worked and what didn't? Help me build a strategy.

Win rate by SC involvement

What's our win rate when an SC is involved vs. not? Break it down by deal size and product.

Demo prep command

Create a command that preps me for any demo — pulls account context, prior calls, technical requirements, and competitive landscape. Save as /demo-prep.

Custom demo deck

Build me a custom demo deck for [Account] based on their discovery call — focus on the use cases they care about most.

Discovery-to-proposal

Based on the discovery call with [Account], generate a proposal outline with recommended solution, pricing tier, and implementation timeline.

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
Von logo lockup
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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