When sales teams chase intent signals, they’re usually digging through traffic logs, third-party panels, or content consumption data that hints at interest, but rarely offers clarity.
Onfire, a fledgling AI startup, is positioning itself as a watershed shift: rather than chasing proxies, it claims to deliver contextual, person-level intent by mining developer and technical user behavior in real time. If it works, this could be the first time a sales AI platform combines signal precision with narrative context. It’s not just “who clicked,” but “what trouble they are describing, in their own words.”
Why Context Has Always Been the Missing Piece
Intent data is a thriving industry. According to DataIntelo, the global “Contextual Intelligence Platforms” market reached about $4.7 billion in 2024, and is projected to skyrocket to $36.6 billion by 2033. This growth trajectory is a clear indicator of the increasing demand for more precise signals, faster, to sharpen outreach and shorten sales cycles.
Yet many organizations report that their intent data comes without sufficient context. A survey reveals that 35% of B2B marketers identify maintaining accuracy across multiple sources as their biggest challenge. Another 35% say the harder part is turning intent data into action. Meanwhile, the often-criticized drawbacks of intent tools include misinterpretation (mistaking curiosity for purchase intent), stale or outdated data, and weak matching to relevant roles or technologies.
Onfire’s Approach: Precision + Narrative
Onfire defines itself as an “AI-powered revenue intelligence platform that helps sales teams identify high-intent prospects by analyzing developer activity, detecting real-time buying signals, and enriching target accounts with the right decision-makers and technologies.” The key differentiator is combining what people are saying with who is saying it, along with what technologies it already uses. For example, this could mean posts in technical forums or developer communities.
Instead of issuing alerts when someone visits a whitepaper or researches a broad topic (which many intent tools do), Onfire captures signals like: “seeing memory leaks with framework X,” or “we need horizontal scaling support for architecture Y.” These are far richer than generic interest indicators.
That technical dimension of developer activity, technical signal matching, and decision-maker identity, is what turns raw intent into actionable, contextual intelligence.
The Challenge of Noise, Scale, and Signal Validation
Delivering on that promise, however, demands solving hard problems. To begin with, signal quality: developers discussing bugs, feature requests, or architecture are plentiful, but distinguishing noise (casual commentary, hypothetical discussion) from purchase-readiness is nontrivial. Effective resolution between these is vital.
Then there is source coverage: the breadth of forums, communities, tech stacks, languages, and frameworks to monitor. Gaps in coverage can produce blind spots or bias. According to intent data research, a lack of adequate signal coverage is one of the chief reasons companies struggle with existing solutions.
Also, timeliness matters. The data needs to be real-time (or close to it), and decision-maker enrichment must be accurate and up-to-date. Outdated profiles or mismatched roles diminish value quickly. Research shows that stale data and poor signal verification can degrade the ROI of intent programs.
Market Opportunity & Timing
The macro environment favors what Onfire is doing. With buyer journeys becoming increasingly complex, especially in technical enterprise B2B or developer tools, decision-making is often embedded in technical forums, open-source communities, issue trackers, or developer Q&A sites. Vendors who fail to account for those technical signals risk missing early indicators of interest.
Moreover, the contextual intelligence platforms market indicates that there is room for innovation. In parallel, the “context-aware computing” market (which includes predictive services and real-time analysis of context) is estimated to reach over $122.2 billion by 2030.
What’s New And What’s at Stake
Onfire is doing something rare: it takes real conversations (where people describe their technical problems in everyday language) and connects them to the right decision-makers and technologies. Instead of guessing what people care about, it understands their actual challenges, the words they use, and what’s at stake for them.
For sales teams, that means better timing, more fitting messaging, and potentially fewer wasted outreach cycles. It also promises stronger alignment with product teams, since the details of what issues people are wrestling with emerge more clearly.
More Than Signals, They’re Stories
OnFire isn’t riding the coattails of “intent” optics or noise-filled dashboards. By building an engine that listens for where technical problems are being discussed, what they are, by whom, and with what technology context, OnFire could mark a turning point in sales tech. The difference may be what separates wasted outreach from resonant, revenue-driving conversations.
For the first time, sales AI may move from “who clicked” to “what’s actually broken,” and that could matter more than anything else.
