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Von is built for
Partnerships

What can Von do for Partnerships?

Partner pipeline review

Show me all deals with [Partner] involvement this quarter — stage, ARR, and owner. How's the co-sell pipeline looking?

Partner QBR prep

Build me a QBR deck for our partnership with [Partner] — co-sell deals, win rate, pipeline generated, and joint opportunities.

Cross-sell identification

Which accounts have both our product and [Partner]'s product? Where are the cross-sell opportunities we're not pursuing?

Co-sell deal tracking

Give me a status update on all active co-sell deals with [Partner] — stage, owner, and next step.

Partner-sourced revenue report

Build me a report showing partner-sourced vs. partner-influenced revenue this quarter, broken down by partner and segment.

“Von is the first thing I open in the morning and the last thing I check before I log off. I run scheduled commands for pipeline updates, 1:1 prep, and competitive intel. It's not a tool; it's become my operating system.”
Evan Briere
Global Director of New Business
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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