We Built RoasNova: An Open-Source AI Advertising Operations Platform

Murat Komurcu — Thu, 20 Aug 2026 23:26:54 +0000

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.

The problem we experienced

Managing advertising operations usually means switching between:

Meta Ads Manager
Google Ads
TikTok Ads
Analytics and attribution platforms
CRM systems
Spreadsheets
Social inboxes
Internal approval processes

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.

What RoasNova does

RoasNova currently includes:

Multi-channel advertising account monitoring
Campaign performance analytics
Conversion and revenue reconciliation
Explainable AI recommendations
Decision evidence and confidence scores
Revenue CRM and lead tracking
Social inbox operations
AI-assisted copy generation
Versioned campaign plans
Guarded campaign draft creation
Human approval workflows
Role-based access and operational limits
Data-health monitoring
Immutable audit trails
Scheduled synchronization and automation jobs

The system is designed to move from:

advertising data → customer intent → verified revenue → evidenced decision → controlled action

Why “guarded” AI matters

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:

Why was this action recommended?
Which data and revenue signals support it?
How reliable is the underlying data?
What is the potential downside?
Does the action require human approval?
Can every step be audited afterward?

RoasNova therefore treats AI as a decision-support and controlled-execution layer.

Actions can be constrained through:

Confidence thresholds
Maximum budget changes
Cooldown periods
Daily execution limits
User roles and permissions
Explicit approval requirements
Provider-write restrictions
Immutable audit records

The development environment uses sandbox data. Optional provider writes are disabled by default and limited to creating paused Meta campaign drafts after explicit approval. RoasNova cannot automatically activate ads or modify live budgets.

Technical architecture

RoasNova is built as a TypeScript monorepo using:

Next.js 16 and React 19 for the web application
Fastify 5 and Zod for the API and runtime contracts
PostgreSQL 18 and Prisma 7 for tenant-scoped persistence
Redis 8 and BullMQ 6 for synchronization and background jobs
S3-compatible storage for private creative assets
Turborepo, Vitest, ESLint, and Prettier for development and quality controls

The repository also contains connector foundations for Meta, Google Ads, and TikTok, along with billing, notifications, campaign planning, and deployment infrastructure.

Why we made it open source

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.

Current status

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:

Developers interested in AI agents, advertising infrastructure, and data systems
E-commerce brands willing to participate in pilot testing
Agencies managing multiple advertising accounts
Technical contributors and early feedback

GitHub

The project is available under the MIT License:

👉 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.

DEV Community: Murat Komurcu