Semantic search and RAG retrieval with Pinecone vector infrastructure.
Fremen Consulting implements Pinecone vector databases for semantic search and RAG — index design, embedding pipeline architecture, metadata filtering, hybrid search, and production scaling for AI-powered applications.
Problems we solve for businesses like yours
Default chunk sizes and naive embedding strategies retrieve tangentially related documents, causing LLM hallucinations grounded in wrong context.
Wrong metric, dimension mismatch, or missing metadata indexes create slow queries and inability to filter by tenant, date, or document type.
Unplanned index growth and query volume spikes inflate Pinecone bills without namespace strategy or pod type optimization.
Solutions tailored to your industry and growth goals
Pinecone index design with appropriate pod type, namespace strategy for multi-tenancy, metadata schema, and hybrid search configuration.
Document ingestion, chunking strategy, OpenAI or open-source embedding generation, and incremental upsert pipelines for fresh data.
LangChain or custom retrieval integration with reranking, score thresholds, and citation formatting for production Q&A systems.
Measurable outcomes from projects in this space
Pinecone-powered RAG over 50,000 support articles achieved 85% retrieval precision and enabled automated tier-1 ticket resolution.
Clear answers to common questions in this industry
Pinecone is a managed vector database for storing and querying embeddings. It powers semantic search, RAG retrieval, recommendation systems, and anomaly detection by finding the most similar vectors to a query embedding.
Pinecone excels at scale, low-latency search, and managed operations without DBA overhead. pgvector suits teams already on PostgreSQL with moderate scale. We assess your volume, latency, and ops requirements.
Yes. Chunk size, overlap, and semantic splitting strategy significantly affect retrieval quality. We test multiple approaches against your content type and evaluation metrics before production deployment.
Yes. We implement namespace-per-tenant or metadata filtering strategies to isolate customer data in shared Pinecone indexes while maintaining query performance.
Basic semantic search setup takes three to five weeks. Full RAG pipeline with ingestion, evaluation, and production integration typically takes six to ten weeks.
Tell us about your business and goals. We will recommend the right approach for your industry, timeline, and budget.