Lemon AI
Predict campaign performance before you spend
The Challenge
Marketing teams were drowning in fragmented data across 12+ ad platforms. A single cross-channel report took 4-6 hours of manual work. Lemon AI needed a platform that could ingest, normalize, and predict campaign performance before launch.
Constraints & Requirements
- Processing millions of campaign events daily across 12+ platforms
- Reports generating in under 10 seconds, not minutes
- Multi-dimensional data visualization (channels × segments × time × metrics)
- Prediction accuracy must exceed 85% to maintain analyst trust
Our Approach
Discover
Shadowed 3 marketing analyst teams. Found 70% of analyst time was spent on data normalization, not analysis.
Design
Dashboard-first interface using customized MUI. Interactive prototypes tested with 8 analysts across 3 companies.
Build
Next.js frontend with React Query and Redux. Google Charts for visualization. WebSocket for live campaign monitoring. LLM-backed predictions and the AI Reports Chatbot using commercial model APIs, agent-style orchestration, and vector retrieval for reporting.
Launch
Beta with 5 pilot customers. Collected feedback on prediction accuracy and usability.
Iterate
Added segment comparisons, exportable PDF reports, and anomaly detection alerts.
The Solution
Built a Next.js + React platform with Material-UI, Styled Components, TailwindCSS, React Query, and Redux. Google Charts for visualization; Docker and Clickhouse for data pipeline; Sentry for monitoring. The AI layer follows common patterns: LLM APIs, agent frameworks, vector search and embeddings, and hosted models, powering predictions and the AI Reports Chatbot. SQL-driven reports, visualization, and real-time notifications via WebSockets. Deep segmented reports from raw data in seconds.