How Vertical AI Can Save GTM for Software Infrastructure Companies

In this guide, you'll learn:

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• Why the current generation of GTM data tools is failing software infrastructure sellers
• How most data providers collect their data, and where the gaps are
• Why intent signals from traditional providers miss technical buyers entirely
• How vertical AI and Onfire's 4-pillar approach deliver the accuracy that horizontal tools can't

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Why GTM is Broken for Software Infrastructure Companies

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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. What's going on here?

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An example might help us understand the problem. Say you're selling a cloud cost monitoring solution. Your ICP is traditional enterprises (such as banks and financial services) who are using Azure at a very high scale. If you check a tool like ZoomInfo, Apollo, or Seamless, you will end up with a list of thousands of companies, and often dozens or hundreds of prospects within each company.

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Who do you contact? What do you say?

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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. Even with expensive AI tooling, they feel like they're still guessing their way through outreach.

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They're right.

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The problem is not the lack of AI- after all, today everyone has access to science fiction-level AI inference through tools like Claude Cowork- but with the underlying data layer. When the data is wrong, more AI is not the answer. Layering the best models in the world, for personalization, buying intent, whatever- won't help when the data is wrong.

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'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 problem starts with the data

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The issue starts with where most GTM data actually comes from. Firmographics — company size, industry, headcount — are sourced from the public web. Technographics are inferred from job posts: if a company is hiring for “Azure engineers,” that gets logged as an Azure account. And prospect-level data is mostly job titles pulled from LinkedIn.

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None of this is useless. But for software infrastructure sellers, it’s not nearly enough.

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Account-level accuracy is a guess. The fact that a company is hiring for Azure doesn’t mean they have a large-scale Azure implementation. They might be planning a migration, or just hedging their bets in the hiring market. “PostgreSQL experience preferred” in a job post doesn’t mean the company runs PostgreSQL in production.

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Prospect-level data is too vague. Most companies don’t have a “head of cloud costs engineering” role. The person you actually need to reach might be a cloud engineer, a software engineer, or an infrastructure specialist. They could be “senior,” “manager,” or “director.” In a larger engineering org, there might be hundreds of “senior software engineers” and dozens of “engineering managers” — and the job title alone won’t tell you who evaluates new tools, has budget influence, or can champion your product internally.

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Intent signals miss technical buyers entirely. 95% of your target market isn’t in-market at any given moment. You need to know who’s actively looking. Most intent platforms — 6sense, Demandbase, and others — rely on IP-based tracking and web activity monitoring: they detect when someone from a target company visits vendor websites, downloads content, or engages with ads. 

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But technical buyers don’t behave like your textbook enterprise SaaS buyer. They’re not downloading whitepapers or clicking display ads (which they’ll make every effort to block). The places where they are active- communities, meetups, open-source repositories — are harder to reach, and collecting data from these sources is simply not part of the business model for horizontal GTM platforms.

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When an engineer is asking about a competitor on Reddit, or evaluating alternatives in a Slack community, that’s a genuine buying signal. But traditional providers can’t see it.

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The result: your BDRs spend most of their time on manual research or chasing the wrong people. Layering more AI on top of this — for personalization, lead scoring, whatever — doesn’t fix the problem. It just automates the guesswork.

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The Vertical AI Revolution

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Horizontal AI fails because it assumes that all buyers are the same. The underlying assumption is that with a few minor adjustments, you can use the same datasets and AI models to infer who's most likely to buy an 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.

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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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Onfire monitors over 100,000 sources where technical buyers actually communicate, often anonymously (or pseudonymously): GitHub, Stack Overflow, Reddit, Quora, Hacker News, Discord, Slack communities, X, and more. When an engineer at Company X is actively contributing to a PostgreSQL extension on GitHub, or troubleshooting replication issues in a developer forum, that's a much stronger signal than some keyword appearing on a hiring portal.

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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. Rather than targeting "every senior engineer at company X," you're identifying the specific person who manages the CI/CD pipeline, or who is in charge of a DevSecOps initiative.

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Under the Hood: How Onfire Builds Its Data Advantage

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So how does this work in practice? Onfire's approach to data is built around 4 key pillars:

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1. A solution tailored to each customer's GTM

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Rather than relying on generic data and targeting, Onfire's specialists work with each new customer to create a bespoke data strategy. This includes:

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• Aligning on ICP-fit accounts and 'golden personas' that can drive a deal forward, based on their actual role in the organization rather than their job title. For example, a golden persona might be "the engineering lead responsible for CI/CD".
• Tuning Onfire's AI and data collection so that it can identify the right prospects at scale, and prioritize accounts which are showing buying intent right now.
• Integrating Onfire into the workflows and tools that your sales team is already using — so that rather than fiddle with filters, your BDRs get a fresh list of prospects to target at the start of the day, and your AEs get all the intel they need before a sales call.

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Because of the flexibility of Onfire's AI architecture, this entire process is typically completed in a matter of days and does not require costly professional services contracts.

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2. Data sources that no one else tracks

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In addition to the 'standard' sources that other data providers collect — such as job changes and company firmographics — Onfire tracks another category of signals that are highly relevant for technical audiences:

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• Communities: Platforms like Stack Overflow, Slack communities, Discord, X, and Reddit. These are the places where technical buyers express their needs, ask for advice, and look for insight before exploring new tooling.
• Open source activity: OSS tells you which technologies are being used and which problems are being solved. Onfire tracks OSS contributions (via GitHub pull requests) and other adoption signals at the account and prospect level.
• Tech conferences: Offline matters too. Event data from Luma, Meetup, and other social platforms can provide important context. If a prospect is attending re:Invent sessions on database refactoring, that's a strong intent signal — and Onfire will collect that.

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3. First-party data integration

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An oft-ignored source of insight is the data you've already collected in your CRM, marketing automation, and web analytics tools. This can provide valuable signals: accounts showing high activity on trial or freemium editions, approval chains mentioned on previous sales calls, or prospects who have interacted with certain categories of content.

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Onfire combines this first-party data with our third-party data to create a richer, more detailed picture of the account. This works in both directions: Onfire's AI uses your data as another signal to surface new potential buyers, while Onfire's data enriches your CRM and marketing automation workflows (such as PQL/MQL scoring or automated outreach).

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And this is always done on a per-customer basis — your first-party data is never used as a signal for other customers.

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4. Identity resolution and knowledge graph technology

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All the data in the world won't help you close a single deal if you can't tie it back to a specific action that a BDR or AE needs to take right now. This is especially important for the type of messy, semi-anonymous signals we're dealing with here. You need a way to correlate a semi-anonymous Reddit question, a list of competitors mentioned on a sales call, and a recent hire. And you need to translate it into a single actionable step: John Smith is asking about competitors; share comparison materials.

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This is where Onfire's proprietary technology comes into play. Using techniques developed and battle-tested at massive scale — and tailored to each organization's unique GTM motion- Onfire will:

• Create a living knowledge graph of your target account: This goes beyond 'keywords in job title'. Onfire builds a detailed topography of the technologies used in each account, layering domain knowledge onto the raw data (for example, an account using "EKS" will be associated with Amazon Web Services); and it maps technologies to specific teams, persons, and org charts within larger accounts.
• Connect anonymous signals to actual buyers: Technical audiences will often use channels such as Reddit or Stack Overflow anonymously, or pseudonymously. Onfire builds a comprehensive profile of anonymous user activity, and uses AI to automate the 'detective work' of connecting these profiles to identifiable and contactable people within an account — transforming sources such as Reddit and Stack Overflow into lead generation opportunities.
• Find the golden personas in each account: Based on the personas you decided you're after, Onfire will find the specific people in the account you should target. This won't be "engineers who have Kubernetes listed as a LinkedIn skill"; it will be an evidence-based insight into the specific person responsible for a managed Kubernetes implementation in a specific cloud provider. You can see the actual evidence, with links to the specific data sources that indicate technology choices and buying intent.

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The Bottom Line for Revenue Teams

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At the end of the day, here's what matters: your BDRs and AEs need to know who to target, when to target them, and what to say. Everything else is plumbing.

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With Onfire, revenue teams get:

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• Buyer data that's actually accurate: The vertical approach and unique data sources enable us to deliver data that's reliable and relevant, which is why our customers see results such as 3x higher reply rates.
• Specific buyers rather than endless lists: With other data providers, you end up with lists of dozens or hundreds of prospects to reach out to. Most of them will be irrelevant, wasting your credits and (more importantly) your time. Onfire gives you the specific people who will champion your technology and help you land an account.
• More time spent selling: Sifting through lists, double-checking data, building additional automations — all of this isn't sales, and it distracts your BDRs and AEs from what they're actually hired to do. Onfire automates everything that isn't actually selling — so your reps spend a fraction of the time on account research, and more time directly growing their pipeline.

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At the end of this process, BDRs and AEs get just the bottom line:

• Who should they be targeting today?
• Which accounts are at risk?
• Who is going to an upcoming event?
• What technologies is this organization already using?

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And they have all this information in the tools they're already using- CRM, outbound messaging tools, marketing automation, or Sales Navigator.

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That's why we're inviting you to challenge us- tell us what you're looking to achieve, see the data we provide, and compare it to any tool you're currently using or evaluating.
‍Get in touch to get started.

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