Vector Store Strategy & Architecture Assessment

Applogika's Vector Store Strategy & Architecture Assessment helps organizations choose the right vector storage and retrieval architecture before committing to a production GenAI, Retrieval-Augmented Generation (RAG), enterprise search, or agentic AI platform. Designed for ML/AI engineering leaders, data architects, platform teams, and cloud architects, the engagement reduces the risk of costly vector-store redesigns by evaluating technology options against actual application and business requirements.

During the assessment, Applogika evaluates embedding strategy, vector dimensions, indexing and retrieval patterns, expected data volumes, query throughput, latency objectives, scalability, security requirements, operational complexity, and cost considerations. AWS-native options can include Amazon OpenSearch Serverless vector search, Amazon Aurora PostgreSQL with pgvector, Amazon MemoryDB vector search, and Amazon Bedrock Knowledge Bases. The assessment also considers how each option integrates with Amazon Bedrock and downstream RAG and generative AI architectures.

At the conclusion of the engagement, customers receive a recommended vector-store architecture, comparative technology assessment, target-state design, key architectural decisions, implementation considerations, and a prioritized adoption roadmap. Applogika's Forward Deployed Engineering team works with customer AI, data, and platform teams throughout the assessment. A typical engagement takes approximately 1–3 weeks, depending on architecture complexity, workload requirements, and the number of use cases evaluated.