ZvenoAI
200+ AI models via unified API · B2B + B2C growth system
ZvenoAI gives access to 200+ AI models through a single OpenAI-compatible API, with payments in rubles and support for both individual and team use cases. The platform serves two structurally different audiences: developers and businesses integrating AI into workflows (B2B), and individual users running personal AI tasks (B2C).
The task was not to launch ads — it was to build a growth system for a complex product: separate the audiences, build campaign structure, craft differentiated messaging, and set up analytics that tracked real user actions inside the service, not just registrations.
The first structural decision: B2B and B2C cannot share a funnel. Mixing use cases — from personal chat to enterprise API integration — produced messaging that fit neither audience well.
- ·API access and integration
- ·Team and workflow automation
- ·Business use cases
- ·Developer scenarios
- ·AI chat and personal assistant
- ·Writing and content creation
- ·Individual task scenarios
- ·Day-to-day AI access
Search campaigns were rebuilt around demand intent, not product features. 1,300 keywords across 20 groups, segmented into 5 clusters — each with its own ad copy, landing path, and UTM tagging through to registration and balance top-up.
| CLUSTER | INTENT SIGNAL | AUDIENCE |
|---|---|---|
| API & Models | Direct API access, model comparison, OpenAI alternative | Technical / B2B |
| Business tasks | AI for teams, content, automation, workflows | B2B |
| Integration | API integration, SDK, compatible API | B2B / Dev |
| Multimodality | Image, voice, multi-modal queries | B2B / B2C |
| B2C scenarios | AI chat, personal assistant, writing help | B2C |
Registration alone was not a useful optimization signal for a freemium AI platform. Four Metrica goals mapped the full activation funnel, making it possible to evaluate campaigns by user quality, not just traffic volume.
For a freemium AI platform, a registration tells you nothing about user quality. Balance top-up is the first monetization signal. Building optimization around top-up rate, not registrations, changed what “good traffic” meant across every campaign.
Different use cases, different messaging, different conversion logic. API access and team workflows need entirely separate landing paths from personal chat use. Mixing them into one funnel diluted conversion at every stage.
20 groups by demand type — API, models, integration, business, B2C — meant that each cluster could be written, pruned, and evaluated independently. Structural precision replaced guesswork about which queries were delivering actual value.
Without tracking through to balance top-up, only traffic volume was visible. Full UTM coverage across all campaigns and groups revealed which exact campaign-group combinations produced paid users — not just visitors.
Comparing week-by-week dynamics in February and March showed that improvements to campaign structure, semantics, and ad copy had a measurable impact on top-up volume — not just on clicks. It made the connection between work and result visible.
A complex AI platform with mixed audiences was turned into a structured, measurable growth system. Audiences separated. Campaign architecture rebuilt around intent. Analytics extended beyond registration to the first monetization event. The result: 680 registrations and 137 balance top-ups from ads in the tracked period, with weekly dynamics showing improving user quality as the structure matured.
Analytics: 4 Metrica goals · UTM to balance top-up level
Share of new users from ads: 78% registrations · 68% balance top-ups
Channel: Yandex.Direct Search
Client name used with permission. Report ID is internal. Metrics reflect a specific measurement period — results vary by product maturity, funnel setup, and market conditions.