Production retrieval-augmented generation with vector stores, hybrid search, reranking, citation, and observability — so you can trust the answers.
Talk to an AI architectRetrieval-augmented generation (RAG) lets LLMs answer questions grounded in your documents — but most RAG demos fall apart in production. They hallucinate, miss obvious context, return stale data, and give no way to verify answers. The gap between a LangChain tutorial and a production RAG system is enormous.
CB4UHost builds production RAG systems that actually work. We handle vector store selection (pgvector, Pinecone, Weaviate, Qdrant), hybrid search (keyword + semantic), reranking (Cohere, cross-encoders), citation, source attribution, and observability so you can see exactly which chunks informed each answer.
We're vector-store-neutral. The right choice depends on your data volume, query pattern, latency targets, and existing stack — not on which vendor has the best marketing page. We benchmark 2-3 options before recommending.
Every RAG engagement ends with eval reports (precision, recall, answer quality), observability dashboards, and runbooks for adding new documents, reindexing, and troubleshooting bad answers.
We benchmark pgvector, Pinecone, Weaviate, and Qdrant against your data and recommend the right fit.
ETL for your documents (PDF, Confluence, Notion, web, databases) with chunking strategy tuned to your content.
Keyword + semantic search with cross-encoder reranking — dramatically better recall than pure vector search.
Every answer cites the source chunks so users can verify — critical for legal, medical, and compliance use cases.
Dashboards showing query latency, retrieval precision, answer quality, and failure modes — so you can trust the system.
vLLM/TGI serving, caching, rate limiting, and fallback to GPT-4/Claude when the open-source model is unsure.
We define the use case, inventory your documents, and establish baseline quality metrics for current answer quality.
We benchmark 2-3 vector stores against your data volume and query pattern, then recommend.
We build the ingestion, embedding, retrieval, and generation pipeline with hybrid search and reranking.
We run a held-out eval set, iterate on chunking and retrieval, and report precision/recall/answer quality.
We deploy to production with observability and hand over runbooks for adding documents and troubleshooting.
Interactive tools related to this service.
It depends. pgvector is great if you're already on Postgres and have <10M vectors. Pinecone is best for managed scale. Weaviate and Qdrant are excellent self-hosted options. We benchmark before recommending.
Three ways: (1) hybrid search with reranking retrieves better context, (2) the prompt instructs the model to say 'I don't know' when context is insufficient, and (3) every answer cites sources so users can verify.
Yes. We can build RAG on top of GPT-4, Claude, or open-source models. Many clients start with GPT-4 + RAG, then migrate to a fine-tuned open-source model once they have eval data.
We build reindexing pipelines with change detection (file mtime, database triggers, webhook from your CMS) so the index stays current. We also add cache invalidation for answers that reference updated documents.
Adapt open-source LLMs (Llama, Mistral, Qwen, DeepSeek) to your domain — with LoRA, QLoRA, instruction tuning, and safe deployment guardrails.
End-to-end ML pipelines — experiment tracking, model registry, feature stores, CI/CD for models, drift detection, and rollback. Built on Kubernetes.
Compare H100, A100, L40S, and consumer GPU pricing across 8+ providers — benchmarked against your actual workload, not affiliate payouts.
Tell us about your project. We'll come back with a scoped proposal and a fixed-fee quote.
Talk to an AI architect