The authors developed a Contextual Sequential Two Tower Model to improve ad recommendations on Pinterest, especially for context-specific surfaces like Related Pins. The initial model lacked real-time context, hindering its effectiveness because it relied solely on historical user behavior. To solve this, they integrated a context layer into the model's architecture, allowing the model to incorporate information from the user's current activity. They used synthetic data during training, injecting pseudo-context derived from conversion events to teach the model. A hybrid serving flow was adopted, where most of the user tower processing is done offline, but the context layer is processed online. This allows for dynamic user embeddings influenced by real-time context, improving relevance. Offline evaluations showed a significant improvement in Recall@K compared to the previous production model. The new model increased candidate survival rates and improved ad relevance, especially on the Related Pins surface. This resulted in a measurable increase in conversion-related business metrics, particularly Return on Ad Spend (ROAS). Future work includes expanding the model to other surfaces like Search and experimenting with advanced fusion techniques, such as cross-attention. This work demonstrates the importance of incorporating real-time context for enhancing ad relevance and user experience.