RAG system returning irrelevant results that make your AI answer poorly?
Evaluating vector database options for your scale and budget requirements?
Vector Database Development
Semantic search, RAG pipelines, recommendation engines, and AI memory all depend on the same underlying infrastructure: a vector database that stores embeddings and retrieves similar content fast.
We design and build vector database systems for production AI applications -- selecting the right store, building the embedding pipeline, and integrating retrieval into your AI workflows.
Pinecone, Weaviate, Qdrant, Chroma, and pgvector depending on your requirements
Embedding pipelines from your documents, products, and structured data
Hybrid search (semantic + keyword) for higher precision retrieval
Production-grade indexing, updates, and retrieval monitoring
RaftLabs builds vector database infrastructure for RAG pipelines, semantic search, recommendation engines, and AI memory applications. We select the appropriate vector store (Pinecone, Weaviate, Qdrant, pgvector) for your scale and operational requirements, build the document embedding pipeline, implement hybrid retrieval strategies, and integrate the vector search layer into your AI application. We also build re-ranking and evaluation frameworks to measure and improve retrieval quality.
Retrieval quality is what makes RAG work
The language model is the visible part of a RAG system. The vector database is the foundation. If retrieval is poor -- returning irrelevant documents, missing the most relevant content, or retrieving at too high a latency -- the model cannot produce good answers no matter how capable it is.
Most RAG systems that produce poor outputs have a retrieval problem, not a model problem. We build the retrieval layer right.
What we build
RAG pipeline infrastructure
Complete retrieval infrastructure for RAG systems: document ingestion and chunking, embedding model selection and API integration, vector store setup (hosted or self-managed), hybrid retrieval with re-ranking, context assembly for the language model, and evaluation framework for retrieval quality. The foundation that makes your AI assistant or document Q&A system actually accurate.
Semantic search
Search applications that understand meaning rather than matching keywords. A user searching for "ways to reduce employee turnover" finds content about "retention strategies" and "engagement initiatives" -- because they mean the same thing. Semantic search replaces keyword search for knowledge bases, help centres, product catalogues, and internal document search. Higher user satisfaction and lower failed-search rates than traditional search.
Recommendation engines
Product, content, and document recommendation systems using vector similarity. Users who engaged with content A get recommended content B -- because they are semantically similar, not because they share exact keywords. Item-to-item similarity for "you might also like" recommendations. User preference modelling from interaction history. Works for e-commerce product discovery, content platforms, knowledge management, and learning systems.
AI memory systems
Long-term memory for AI assistants and agents: previous conversations, user preferences, and interaction history stored in a vector database and retrieved when relevant. Enables AI assistants that remember past interactions without requiring the full conversation history in context on every call. Useful for customer support systems, personal assistants, and any AI application where continuity across sessions matters.
Multi-modal vector search
Vector indexing for images, audio, and mixed-modal content alongside text. Image similarity search for e-commerce product matching and content deduplication. Cross-modal retrieval -- text queries that return relevant images or audio segments. Multi-modal embeddings (CLIP, ImageBind) for applications that need to search across content types in a unified index.
Embedding pipeline engineering
Production-grade embedding infrastructure: document preprocessing and chunking strategy, batch embedding API calls with rate limit handling, incremental indexing for data that changes frequently, deletion and update handling for content that is modified or removed, and metadata filtering for structured attribute constraints alongside semantic similarity. The pipeline that keeps your vector index current as your data changes.
Building RAG or semantic search?
Tell us your data types, expected query volume, and retrieval accuracy requirements. We'll design the right vector database architecture.
Related services
RAG Pipeline Development -- complete RAG system beyond the vector database layer
AI Orchestration -- orchestration layer connecting retrieval to generation
AI Knowledge Management -- knowledge base applications powered by vector search
Semantic Search Development -- search applications using vector retrieval
Custom AI Development -- end-to-end AI product development
Frequently asked questions
A vector database stores high-dimensional vector representations (embeddings) of text, images, or other data, and retrieves the most similar vectors to a query vector at high speed. Language models represent meaning as vectors -- similar concepts produce similar vectors. A vector database makes it possible to find semantically relevant content (content with similar meaning) rather than just keyword-matching content. This is the foundation of RAG pipelines (finding the documents most relevant to a user's question before generating an answer), semantic search (search that understands intent), and AI memory (retrieving relevant past interactions).
Pinecone: fully managed, no infrastructure to operate, strong production reliability, higher cost at scale. Best for teams that want a managed service without infrastructure overhead. Weaviate: open-source with managed cloud option, supports hybrid search natively, broader data model including structured metadata filtering. Qdrant: high performance, low resource usage, good for self-hosted deployments with tight resource constraints. pgvector: PostgreSQL extension -- keeps vector search in your existing database, no additional infrastructure, sufficient for most applications under 10M vectors. Chroma: simple, developer-friendly, best for prototyping. We recommend based on your scale, operational preference, and existing infrastructure.
Hybrid search combines semantic vector search with traditional keyword (BM25) search and merges the results. Semantic search excels at finding conceptually similar content even when the exact words differ. Keyword search excels at exact term matching -- product codes, proper nouns, technical identifiers. Hybrid search outperforms either alone for most real-world retrieval tasks. We implement hybrid search using re-ranking or reciprocal rank fusion. For RAG pipelines where retrieval quality directly affects answer quality, hybrid search is usually worth the additional complexity.
Small, fast models (text-embedding-3-small, all-MiniLM-L6-v2): lower cost, sufficient accuracy for most general-purpose retrieval tasks. Large, accurate models (text-embedding-3-large, BGE-large-en): better accuracy for domain-specific content, higher cost. Domain-specific models: fine-tuned embeddings for medical, legal, or technical content significantly outperform general models on domain vocabulary. We select the embedding model that balances accuracy requirements, inference cost, and query latency for your specific content and retrieval use case.
Retrieval evaluation metrics: Recall@K (what fraction of relevant documents are retrieved in the top K results?), Precision@K (what fraction of the top K retrieved documents are relevant?), MRR (Mean Reciprocal Rank -- where does the first relevant result appear?), and NDCG (Normalized Discounted Cumulative Gain -- a quality-weighted ranking metric). We build an evaluation dataset from representative queries and expected relevant documents, then measure your retrieval system against this benchmark. Poor retrieval is the primary cause of poor RAG output -- evaluating it explicitly is not optional.
Building a production vector database system -- embedding pipeline, indexing, hybrid retrieval, and integration with your AI application -- typically runs $15,000--$45,000 for a focused use case. More complex systems with custom re-ranking, multiple collections, multi-modal indexing, and evaluation frameworks run $40,000--$90,000. Ongoing infrastructure costs depend on vector count and query volume -- pgvector is the most cost-effective for self-hosted; Pinecone is the most operationally simple for managed.