Search engines need to process huge amounts of data and serve many users at the same time. This makes it challenging to implement real-time passage reranking algorithms that can work at a large scale. However, our proposed modified cross encoder architecture provides a good solution by using pre-trained transformer models and a unique scoring method. The two-stage architecture first encodes query-document pairs using a pre-trained transformer model. Then, it uses a novel scoring technique that extracts logits and calculates the cosine similarity between the [CLS] token embeddings of the query and document. During training, it minimizes a combined loss function using sigmoid cross-entropy and cosine similarity. This allows the Modified cross encoder to effectively distinguish between relevant and irrelevant documents, making it better than base models like BM25, SparseBiEncoder, DenseBiEncoder, and cross encoder. Our proposed model demonstrated significant performance enhancements, achieving a higher nDCG@10 accuracy of 0.77 and improving other relevant metrics.