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 existing revenue tracking tools.

During an initial product demonstration, Aggarwal showed how the platform could analyze 101 SMB accounts to identify churn risk in just over three minutes—a task he estimates would take a human analyst one to two weeks.

The platform’s primary interface resembles a chat environment, but the outputs are designed to be actionable revenue assets. Key functionalities include:

• Deal Health Monitoring: Cross-referencing calls and emails to surface "risky" commits that might otherwise go unnoticed until the end of a quarter.

• Automated Briefing: Generating pre-call context docs that draw from the entire history of an account, ensuring reps are briefed on every previous touchpoint.

• Win/Loss Analysis: Clustered analysis of transcripts to find the "true" reasons for lost deals, often finding that the recorded reason in the CRM does not match the customer's actual feedback.

• Revenue Operations Automation: Handling "low-level" Salesforce admin tasks, such as creating flows, validation rules, or cleaning up account territories.

The goal is to shift Revenue Operations (RevOps) from a "reporting queue" that handles ad-hoc data requests into an infrastructure layer.

As Kieran Snaith, SVP of Revenue Operations at Qualified, noted in a Von testimonial blog post, the goal is to allow leaders to "run the business in chat," asking complex questions about forecast confidence or pipeline risk and receiving data-backed answers instantly.

Pivoting into 'the next Salesforce'

Von is operated by Rattle Software Inc., a company that previously found success with "Rattle," a mid-seven-figure revenue business focused on Salesforce-Slack integrations. Aggarwal describes Von as a significant pivot toward a larger opportunity, aiming to build "the next Salesforce".

The business has seen rapid early traction, reportedly crossing $500,000 in revenue within its first eight weeks of launch, with projections to reach $10 million in its first year.

The product is governed by a commercial, proprietary license typical of enterprise SaaS. Unlike open-source tools, Von’s "restricted" license means the underlying source code and the "context graph" technology are proprietary to Rattle Software Inc.. Users are granted a non-transferable, non-exclusive right to use the software for internal business purposes, with the company maintaining all rights, title, and interest in the service.

This philosophy of deep integration extends to the broader SaaS ecosystem, where Aggarwal observes, "Point solutions in SaaS are essentially dead. They will have a very hard time surviving in this world, because point solutions can now be white-coded within a company."

Pricing follows a hybrid model of per-seat subscriptions and consumption-based credits. This structure is designed to scale with the persona using the tool; for instance, a Chief Revenue Officer (CRO) seat may cost $1,000 per month for deep strategic analysis, while individual seller seats may be as low as $20 per month for basic research and follow-up tasks.

The company is currently backed by several tier-one venture capital firms, including Sequoia Capital, Lightspeed, Insight Partners, and GV (Google Ventures).

Early adopter reaction

The reaction from early adopters highlights a shift in how AI is being integrated into the sales org.

Taylor Kelly, Head of Revenue Operations at Tapcart, remarked that "Von handles the analysis and insights that would normally require hiring another full-time analyst," specifically citing its ability to handle complex Salesforce configurations and deal risk assessments.

Similarly, Evan Briere, VP of Partnerships at DemandScience, noted that Von’s direct connection to data sources makes it "actually applicable" compared to more "theoretical" horizontal AI tools like ChatGPT.

Other community feedback from the platform’s early users includes:

• CJ Oordt, Sales Director at Coalesce: Described it as a "research assistant who knows every conversation and note".

• Rob Janke, Director of Revenue Operations at QuickNode: Stated that Von "solved this gap before we could even start building it ourselves".

• Sydney, Head of Renewals at 15Five: Highlighted its impact on renewal intelligence, allowing her to analyze actual conversation signals across an entire book of business in minutes.

The prevailing sentiment among these users is that Von serves as "additional headcount" rather than just a tool. This mirrors the company’s internal metrics, which report that Von is already completing over 10,000 revenue tasks per week for its customer base.

An autonomous revenue org

The introduction of Von signals a maturing of AI in the enterprise. We are moving past the era of "AI as a feature"—where a chatbot is simply bolted onto an existing CRM—toward "AI as a persona".

By training foundational models on a company’s specific business logic, Von is attempting to create a system that doesn't just return data but offers "judgment calls".As organizations look toward the rest of 2026, the challenge for RevOps leaders will be one of trust and infrastructure.

If Von can maintain its claimed 95% accuracy in predicting deal outcomes, the role of the human salesperson will inevitably shift toward higher-value relationship management, leaving the "data science" of sales to the agents.

For now, Von remains a high-growth experiment in whether the "intelligence layer" can finally bring the same level of revolutionary workflow to the people who sell as it has to the people who build.