About
We build the missing revenue layer for AI apps
Monetzly is ad infrastructure for LLM applications. We let a developer monetize the conversations their app is already having, without a paywall, a credits system, or a message cap.
The company exists because of a gap that shows up in almost every AI product: usage grows, inference bills grow with it, and the subscription only ever reaches a small minority of users. Everything else is cost.
Why we started
Every AI app we looked at had the same shape of problem. A small paid tier funding a large free tier, with per-conversation costs that rise as engagement rises. The standard responses — raise the price, throttle the free tier, gate the good model — all trade away usage to protect margin.
Meanwhile the free tier is full of commercial intent that nobody is pricing. A user asking an assistant which laptop suits their work has told you more about their purchase than any ad platform could infer. That intent is real value, and it was being thrown away because there was no format for it.
So we built the format: a sponsored recommendation that lives inside the answer, matched to what was actually asked, always labelled, and rare enough that the assistant stays worth reading.
What we believe
The answer comes first
A sponsored mention has to be something that would belong in the response even without the sponsorship. If it would not, it should not run — regardless of what it pays.
Disclosure is non-negotiable
Every sponsored element is labelled, visibly, inside the response. Not in a policy page. An undisclosed ad inside an assistant's recommendation is the failure mode that would end this entire channel, and it is not a setting anyone can turn off.
Context, not surveillance
Matching reads the conversation happening now. It does not depend on a cross-site behavioural profile, because the conversation is a far better signal than a tracking graph ever was.
Low volume, held on purpose
Most turns carry no ad. We treat a low fill rate as the product working correctly, and we will not loosen the relevance threshold to make a number look better.
The developer keeps control
Categories, competitors, sensitive contexts and frequency stay with the app. The app knows its users; we do not know them better.
What we ship
- A server SDK that wraps your existing LLM response stream and injects a matched, labelled placement when one qualifies — passing the stream through untouched when none does.
- Contextual matching against campaign briefs, with relevance thresholds, category blocks and sensitive-context suppression enforced before a match.
- Session-level attribution and reporting, so revenue is measurable against the conversation rather than a page view.
Where we are
Monetzly is in private access. We are onboarding developers and advertisers by conversation rather than self-serve sign-up, because the first cohort of placements sets the standard for the format and we want to be involved in each one.
Practically, that means: join the waitlist and we will provision a key, or book a call if you would rather ask questions first. The changelog is public and the status page is where we post incidents.
Come build with us
If you run an AI app with real conversation volume, or you advertise products people research before buying, we want to talk.