Monetizing consumer AI has long been a puzzle: Subscriptions work for power users, but most people will not pay — and inference costs are only going up. Koah, a startup building a “full-stack” ad-tech platform for online and social apps in AI-driven environments, has picked up $5 million in seed funding to make interactive and “contextually relevant” advertising a staple part of the conversation without ruining the user experience.
The round was led by Forerunner, with participation from South Park Commons and AppLovin co-founder Andrew Karam. Koah’s wager is fairly simple: there’s a long tail of AI apps—especially outside the U.S.—that need an ad business model that works with conversational interfaces, not the feed or banner paradigms of the mobile age.
Why AI chats are catching on with ads
Early consumer AI services favored prosumers and relied on monthly plans. That was good for early traction, but it hampers reach and raises churn. In most markets, charging $20 a month is a mass adoption blocker, and developers still eat the cost of models and hosting, which rise with usage, not revenue.
Compounding these problems, brands are moving budgets to high-intent, measurable environments. U.S. digital ad revenue is now estimated by the Internet Advertising Bureau at more than $200 billion, and marketers are still pursuing surfaces where relevancy and attention are high. Chat interfaces—where users express intents in natural language—are naturally intent-rich. The task is crafting formats that feel useful rather than intrusive.
Koah’s strategy: directly native, intent-based placements
Koah places sponsored suggestions where they’re relevant, labels them as such, and lets users opt into that content or not. Consider utility over distraction: if you ask an AI assistant about go-to-market steps, for example, we might have seen a useful Upwork prompt to hire a fractional marketer. The point isn’t to put banners on top of a chat window but rather, to provide relevant options at the right time in context of the conversation.
Crucially, Koah is not aiming to cram ads into closed foundational platforms. It targets third-party apps on popular models who are looking to control UX and who depend on sustainable revenue. This “monetization layer” aims to be plug-and-play for builders that need to offset inference costs without sacrificing completion quality or session duration.
Early traction: partners, performance and payouts
Koah says it is already live around apps like AI assistant Luzia, parenting app Heal, student research tool Liner, and creativity platform DeepAI. Advertisers range from freelance marketplaces to health and education, including companies like Upwork, General Medicine and Skillshare.
The company says clickthrough rates are in the range of 7.5% — approximately 4x to 5x the percentages of what many of them say they see from traditional mobile ad networks, in similar scenarios — and early partners made around $10,000 in their first month. That’s relevant for AI developers whose unit economics are razor-thin: even a small ad ARPU can be the difference between growing a business and shutting it down when every query comes with a marginal cost.
Koah says its method leads to lower sesh drop-off than legacy adtech integrations built for apps and games not turn-by-turn, goal-driven chats. The goal is bigger: placements that don’t just not hurt engagement, but rather increase task completion, as tasks have a relevant next step.
Sitting in the middle of the purchase funnel
Koah characterizes AI chats as a mid-funnel experience. Users research their options, compare features and plan projects in conversation — but many complete transactions elsewhere all the same, generally with a search or a brand’s own site. That makes “intent capture” key: sponsored prompts, lead-gen actions and trial sign-ups also tied to that query can carry you from exploration to action without injecting an intermediary checkout inside the chat.
Forerunner’s investment thesis reflects that perspective: Subscriptions alone will not usher in the era of consumer AI, and many monetization models will exist side by side. If ads can be designed to feel, look and sound like help — native to the flow, clean and useful — it seems they have a plausible path to becoming a core revenue stream for AI utilities and companions.
What this means for builders and brands
For developers, a conversational UX ad layer can create new addressable markets. Freemium models are a go where subscription penetration is weak. For brands, AI chats provide high-signal targeting without being restricted to third-party identifiers, the user’s words are the context. Done properly, that’s privacy-forward and performant.
The practical questions are around measurement and scaling.
In a basic economy textbook model, water falls into two buckets, one for firms and the other for households, and that’s it. Advertisers will seek clean attribution to downstream actions and publishers will be looking for any lift or drag on retention. If Koah can show durable LTV gains and predictable yield across a large array of AI apps, it will have proven a new ad surface, instead of a mere niche experiment.
Risks, guardrails and the road ahead
Conversational ads require careful guardrails: clear sponsorship labels, strict policy filters and an intolerance for false claims. … [The] regulators have indicated that native ad disclosures must be clear and conspicuous and AI contexts will be no different. There is also a trust issue — if recommendations look paid for, users should know they are, and be able to dismiss them easily.
Still, the opportunity is real. If the AI moment is when consumers vocalize problems and balance solutions, so too, belong relevant commercial options. Now Koah is wagering that it can make those choices additive, rather than abrasive, and it doing so, finally grant AI makers business model that scales with use, instead of against it.