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Weekly team digest

Give me a summary of what my team closed, what moved forward, and what slipped in the last 7 days.

Weekly Team Digest

A complete weekly digest with wins, slips, and risks — in seconds.

What should I worry about

What should I be worried about this quarter? Look at pipeline coverage, deal velocity, and engagement trends across all teams.

What should I worry about

A prioritized risk briefing based on actual pipeline and engagement data.

Deal risk assessment

Which deals in commit and best case this quarter are at risk? Check calls, emails, and activity — give me an honest assessment.

Deal risk assessment

Risk scoring based on actual engagement signals from calls and emails.

1:1 coaching prep

I have a 1:1 with my teammate tomorrow. Pull their pipeline, recent calls, and deal activity. What should I be asking them about?

1:1 coaching prep

A coaching brief with specific talking points and deal-level insights.

Board prep

Prep me for my board meeting — pipeline health, bookings vs. target, biggest risks, and the story I should tell. As a slide deck.

Board prep

A board-ready deck with narrative and data — generated in minutes.

Exec meeting prep

Prep me for my exec meeting with Acme’s leadership — pull the full relationship history, open deals, renewal timeline, and any risks from recent calls. What should I know walking in?

Exec meeting prep

A complete exec briefing with relationship history, deal context, and risk flags — ready in seconds.

Headcount planning

Based on current pipeline coverage, win rates, and quota attainment by segment, where do we need to add headcount to hit our annual target?

Headcount planning

Scenario-based headcount recommendations from live pipeline and performance data.

Competitive landscape

Across all deals this quarter, which competitors are showing up most? Where are we winning and losing against each?

Competitive landscape

Competitive heatmap pulled from actual deal conversations — not rep self-reporting.

“Pipeline questions, deal research, Salesforce execution — it handles it all without needing a setup conversation every single time. It has the context and just goes. Most AI tools show you where the work needs to happen. Von just does the work and continually surprises me with something else it can do.”
Eric Choronzy
Chief Revenue Officer
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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