Bi-encoders are an efficient architecture for semantic Question Answering (QA) systems, where both questions and answers are embedded into a vector space, allowing semantically similar pairs to be close to each other. Using Cohere's co.embed API and Qdrant, a vector search database, simplifies the setup of such systems, offering scalable and maintainable solutions without the need for personal infrastructure. This approach is exemplified by implementing a QA system on biomedical data using the pubmed_qa dataset, where embeddings of questions and answers are stored in Qdrant and queried to find relevant answers. The system's performance is evaluated using top-k accuracy metrics, showing satisfactory results, with accuracy increasing as more results are considered. The combination of Cohere and Qdrant provides a convenient SaaS solution for building and querying QA systems, allowing for customization and fine-tuning with specific datasets for improved domain-specific performance.