Pinecone
Engineering.
We design production RAG retrieval layers on Pinecone: namespace strategy, hybrid search, re-ranking, all of it, so retrieval quality holds up past the demo and into millions of vectors.
Retrieval Is the Product
A RAG system's output quality is bounded by its retrieval quality, full stop. No amount of prompt engineering fixes a retriever that returns the wrong context. We treat the retrieval layer as its own engineered system: chunking strategy tuned to document structure, hybrid dense plus sparse search so exact-match terms like part numbers, error codes, and names aren't lost to embedding similarity, and a re-ranking pass that trades a small latency cost for a large precision gain on the top-k results the model actually sees.
Semantic chunking tuned per document type. A 512-token fixed window works for prose but destroys structure in tables, code, or legal clause hierarchies.
Hybrid search combining Pinecone's dense vector search with sparse (BM25/SPLADE) retrieval, so queries containing exact identifiers aren't degraded by pure semantic similarity.
Cross-encoder re-ranking (Cohere Rerank or a self-hosted BGE reranker) applied to the top 50 candidates before truncating to the top 5-8 that actually enter the prompt.
Namespace-per-tenant isolation with metadata filtering enforced at the query layer, verified with access-control tests, not just documented as a convention.
What we build
Retrieval Architecture
Designing namespace, metadata filtering, and hybrid (dense + sparse) search strategies tuned to your document structure and query patterns, not a generic default configuration.
Ingestion Pipelines
Building semantic chunking and streaming embedding pipelines with incremental upserts, so the index stays fresh without a costly full re-index on every document change.
Access-Controlled Retrieval
Document-level metadata filtering, tested against real multi-tenant queries, so a query only ever surfaces vectors the requesting user is authorized to see.
Retrieval Quality Evaluation
Building a retrieval eval harness (precision@k, recall@k against a labeled query set) so retrieval quality is measured continuously, not assumed to hold as the corpus grows.
What we fix
Retrieval that returns plausible-looking but irrelevant context, causing confident hallucinations
Index growth that silently degrades query latency and recall over time
No re-ranking step, leaving the top result to whatever raw cosine similarity picks first
Multi-tenant data leaking across namespaces due to missing or untested access controls
Chunking strategies that shred tables, code blocks, or structured data into meaningless fragments
No way to measure whether a retrieval change improved or hurt answer quality before shipping it
Stop guessing.
Start shipping.
Thirty minutes with a senior engineer. No sales deck, no junior SDRs. Just a technical conversation about what's actually breaking or slowing you down.