VentureBeat Apr 21, 02:55 PM
What AI model should you use for revenue intelligence? Von says all the big ones, and it will automate mixing and matching for you Looking at enterprise AI adoption, VentureBeat has anecdotally observed a fairly wide divergence when it comes to specific roles: For those who build—engineers and developers—the arrival of AI has been transformative, moving through the workflow with the speed of tools like Claude Code and Cursor to automate the heavy lifting of syntax and architecture.
Yet, for those who sell, the "revenue stack" has remained a fragmented collection of data silos, manual CRM entries, and anecdotal reporting.
Von, a new AI platform emerging from the team behind process automation startup Rattle, aims to bridge this gap. By positioning itself not as another "point solution" but as a foundational "intelligence layer," Von seeks to do for Go-To-Market (GTM) teams what the modern IDE has done for the developer: provide a single, reasoning interface that understands the entire business context.
“AI has revolutionized the workflow for people who build things, but there is nothing that has revolutionized the workflow for people who sell those things," Von CEO Sahil Aggarwal said in a recent video call interview with VentureBeat. "That is what we are trying to build with Von”.
Technology: The context graph and multi-model engine
At the core of Von’s capability is a departure from the traditional "search bar" approach to enterprise AI. While standard LLMs often struggle with the sprawling, unstructured nature of sales data, Von begins its deployment by building a "context graph" of a company’s entire business.
This process involves ingesting structured data from CRMs like Salesforce and HubSpot, alongside unstructured data from call recorders (Gong, Zoom, Chorus), email threads, and internal documentation.
"Once Von builds this context graph, it will understand your business better than anyone else in the company," Aggarwal said.
This understanding is rooted in a company’s specific "ontology"—the unique language of its deal stages, territory definitions, and institutional knowledge.
"We train these foundational models on a company’s own business and ontology to make the model work for them," the CEO addded.
Instead of relying on a single large language model, Von utilizes a "mixture of models" strategy to optimize performance and cost. In this architecture, Anthropic's Claude is deployed for high-level reasoning and "thinking," ChatGPT handles bulk data processing, and Google’s Gemini is utilized for generating creative assets such as decks and reports.
This technical approach allows Von to resolve a common frustration in Sales Operations: the gap between what is logged in a CRM and what actually happened in a meeting. By cross-referencing call transcripts with Salesforce records, the system can identify discrepancies in "lost reasons" or verify deal health based on sentiment rather than just a rep’s manual update.
From reporting queues to AI headcount
Von is designed to function as an "AI Data Scientist" or a "VP of RevOps" that lives on top of the enterprise's exis