Introducing Onfire: A New Breed of Vertical AI for Revenue Teams

After more than two years in stealth, today we are incredibly excited to announce the official launch of Onfire - the Revenue Intelligence platform built for software infrastructure companies. We’re also announcing a $20M initial round led by Grove Ventures and TLV Partners with participation by IN Venture and Leumi 77 Ventures. It’s been a long time coming, and we’re ready to shout about Onfire from every rooftop. We believe we’ve built something really valuable, and our early customers agree.

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Onfire helps revenue teams find, reach, and sell to technical buyers. It’s built from the ground up as a vertical AI solution - based on unique contextual data that we’ve spent years collecting, refining, and training models on. The platform allows teams to target engineering, data, and security leaders with unparalleled accuracy, based on signals such as community activity, event attendance, and product adoption.

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Let’s talk about how we got here, why, and what’s coming next.

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Horizontal AI is failing companies that sell to technical buyers

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Intent signals. Meeting insights. Lead scoring. Automated content generation. Sales and marketing teams are inundated with AI that’s meant to unlock productivity gains - and yet, SaaS GTM efficiency is hitting all-time lows. Speak with companies that sell to developers or other technical audiences, and they’ll tell you that easily 80% of their budget is spent on sending sales outreach messages (or showing ads) to irrelevant people. What’s going on here?

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After meeting hundreds of revenue leaders over the last few years, we believe that the problem is not the lack of AI - after all, today everyone has access to science fiction-level AI inference through tools like ChatGPT - but with the underlying data layer.

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Software infrastructure companies are looking for highly-specific audiences - for example, a FinOps sales team might want to reach the handful of people responsible for cloud cost optimization in companies using managed Kubernetes, on AWS. The current generation of data providers will claim they can find these buyers, but the not-so-secret secret is that the data is often stale or just plain wrong. It’s inferred based on keywords in job posts, LinkedIn job titles, and other information that’s easy to find - but doesn’t produce the granularity of insights needed to effectively sell specialized technical solutions.

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When the data is wrong, more AI is not the answer. Layering the best models in the world - e.g. for personalization or to identify buying intent - will not help when the data is wrong. ‘Garbage in, garbage out’ still applies. This is why the new generation of AI tools, from ChatGPT wrappers to AI SDRs, is not driving ROI; these tools rely on the same shaky data foundation as their predecessors.

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To solve this problem, you don’t need bigger models - you need to get into the smaller details of who the buyers are, how they share information, and how they actually communicate intent.

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The promise of vertical AI: how Onfire works

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The underlying assumption of horizontal GTM tools is that every company is essentially the same. With a few minor adjustments, you can use the same datasets and AI models to infer who’s most likely to buy your enterprise-wide HR solution, and who’s looking to replace an  AppSec tool in the next six months.

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As a vertical AI solution, Onfire assumes the opposite: every buyer is different.

Technical buyers have unique characteristics. On the one hand, they’re averse to traditional sales and marketing, and might not share the most important details on their LinkedIn profile; on the other, they are highly active in other public channels - OSS contributions, developer communities, professional conferences, and social media platforms such as Reddit.

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Ever since we founded Onfire, we have been perfecting our ability to collect and understand data from these specific sources. We use proprietary AI to de-anonymize and map every message and every discussion to a specific account, which is then further enriched with other data sources (such as what you might find on tools like ZoomInfo), and the internal data that each customer can provide (such as CRM and product usage).

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The result is an Account Intelligence Graph™ - a resolvable map of prospects, events, products, features, and outcomes - tailored to each customer’s unique business model and go-to-market motion.

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This approach helps us deliver a product that’s not for everyone - but is best-of-class for companies that sell to technical buyers.

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What makes Onfire unique?

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• Data that no one else has:  Accurate technographics for 91% of the companies in the world; thousands of prospect-level signals from technical buyers (e.g., a person asking about a competitor in a Slack community); and over 50 million engineering and tech decisionmakers in our database. All this data is combined with our customer’s internal records (per customer, no data sharing) to produce a complete, accurate picture of their target market.
• Vertical AI at the core: While other companies bolt on AI features, Onfire was built from the ground up for the age of agentic AI. Our models and data have been specifically designed to serve the verticals we specialize in: data, cybersecurity, dev tools, FinOps, and other software infrastructure sold to technical buyers.
• A complete solution for revenue teams: Onfire combines data enrichment, intent signals, martech and salesetch integrations, and agentic AI automation to provide a full-stack GTM platform.
• Validated with high growth companies: Even without officially launching and with basically zero marketing, we’re already serving dozens of paying customers including Aiven, Eon, Ox Security, and many others. Revenue teams using Onfire are seeing tremendous value and ROI, including over $50M in closed deals.

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Who we are, and how we got here

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Onfire was founded and is led by Shahar Shavit (CTO), Nitzan Hadar (CPO), and myself (CEO). We have known each other for more than a decade; in our previous lives, we worked together to build large-scale production AI tools that power mission-critical systems.

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Ahead of our next venture, we wanted to use our unique experience in identity resolution and extracting insights from massive, unstructured datasets - and we wanted to apply our knowledge to a big and very challenging problem. After speaking with 300+ revenue leaders, we knew that we found a major industry challenge that we are best suited to tackle.

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Companies are spending a lot of money buying technical tools; vendors are spending even more money selling them. The inefficiencies in this process are huge, and it’s creating a gap in the market and slowing innovation. Vendors waste budgets and time targeting the wrong people, and buyers are inundated with irrelevant and spammy information.

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So it’s a big problem. But can we solve it better than anyone else? That’s where our AI background comes in. As we’ve covered above - these elusive technical buyers, which everyone is struggling to find and reach, are actually very active on the public web. It’s a matter of knowing where to look, and building the AI models that can extract signals from massive amounts of noise - which is exactly what we know how to do.

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What’s next for Onfire?

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Today, along with the launch, we’re announcing the Onfire Agent - a revenue copilot that builds your accounts lists, answers complex GTM questions, creates your outreach sequence, and takes action in your systems.

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Over the next months, you can expect to see deeper integrations, better data coverage, and new AI capabilities. And with more market interactions and more data feeding in, our models will only get better and more accurate.

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If you’re selling software infrastructure, we’d love to show you everything Onfire can do. We’re certain it can outperform any tool you’re using today - but if it’s missing anything, we’d also love to hear your feedback. Drop us a line if you’re interested