Advertising teams do not suffer from a lack of dashboards.
They suffer from fragmented data, unreliable attribution, disconnected customer journeys, and automation systems that can make consequential changes without explaining why.
We built RoasNova to address that problem.
RoasNova is an open-source advertising operations platform that connects campaign performance, customer intent, conversion data, and verified revenue—then turns those signals into explainable recommendations and controlled actions.
Managing advertising operations usually means switching between:
Each tool contains part of the truth, but none represents the complete decision.
A campaign may report a strong ROAS while producing low-margin customers. A channel may appear inefficient because conversions were not reconciled correctly. An AI agent may recommend increasing the budget without considering creative fatigue, data quality, attribution confidence, or operational risk.
We wanted to build a system that evaluates the entire evidence chain.
RoasNova currently includes:
The system is designed to move from:
advertising data → customer intent → verified revenue → evidenced decision → controlled action
We do not believe an AI agent should be given unrestricted access to live advertising accounts.
A recommendation is only useful when the operator can answer:
RoasNova therefore treats AI as a decision-support and controlled-execution layer.
Actions can be constrained through:
The development environment uses sandbox data. Optional provider writes are disabled by default and limited to creating d Meta campaign drafts after explicit approval. RoasNova cannot automatically activate ads or modify live budgets.
RoasNova is built as a TypeScript monorepo using:
The repository also contains connector foundations for Meta, Google Ads, and TikTok, along with billing, notifications, campaign planning, and deployment infrastructure.
Advertising infrastructure handles highly sensitive business data and can influence significant financial decisions.
We believe operators should be able to inspect how recommendations are generated, understand the safeguards around execution, and verify how their data is handled.
Open source also allows developers, agencies, and advertising teams to extend RoasNova for their own workflows instead of being locked into another opaque platform.
RoasNova is currently in the pre-launch stage.
The core platform and interactive product experience have been built, and we are preparing for initial pilot users and commercial validation.
We are currently looking for:
The project is available under the MIT License:
👉 [https://github.com/MuratKomurcu1/roasnova](https://github.com/MuratKomurcu1/roasnova)
If you find the project interesting, you can explore the architecture, open an issue, contribute, or give the repository a star.
We would especially like to hear how you currently connect advertising performance to verified revenue—and which parts of your advertising workflow still depend on spreadsheets and manual decisions.