RAG development services for production-grade retrieval AI.

RAG (retrieval-augmented generation) development services are engagements that build production retrieval systems grounding LLM answers in a private corpus — combining a vector store, retrieval architecture, prompt orchestration, citations, refusal layer, and eval harness into a deployable system. Good RAG development services treat retrieval as the load-bearing system, not the LLM.

Dense embeddings (semantic match) miss queries with specific keyword requirements like product codes, drug names, or legal citations. BM25 lexical search catches those but misses semantic intent. Hybrid retrieval runs both and re-ranks — measurably better recall in production, especially on long-tail and out-of-distribution queries. Most production RAG failures we audit are from naive vector-only setups.

Four layers. Hybrid retrieval grounds answers in your corpus. Required citations link every answer to a source document — if the citation is missing, the answer is rejected before being shown to a user. A refusal layer activates when retrieved context is insufficient — system says "I don't have enough information to answer" instead of inventing. An eval harness blocks any change that regresses citation accuracy or refusal correctness.

pgvector (Postgres extension) for teams that want to keep retrieval inside their existing database. Qdrant for high-throughput, hybrid-search workloads. Weaviate for multi-modal and graph-style queries. Pinecone if you need a managed service. We benchmark embedding throughput and recall on your specific corpus before recommending — there is no single right answer.

Yes. Self-hosted Llama 3 on vLLM, self-hosted pgvector or Qdrant, self-hosted embedding model — zero customer data leaves your cloud. AWS Mumbai supported for Indian data residency, AWS EU for GDPR-aligned EU residency. Reference deployment: a medical-inquiry RAG running fully inside hospital VPC with no egress.

Most RAG v1 engagements at Aiinfox land between $25,000 and $90,000 fixed-price for a focused build (one corpus, one or two retrieval modes, one channel). Multi-corpus enterprise RAG with permission-aware retrieval and SSO typically reaches $100,000 to $180,000. Pilots ship in 2-3 weeks with deflection or accuracy guarantees written into scope.

Two to three weeks for a single-corpus RAG pilot with citations and a refusal layer. Six weeks for production-grade RAG with multi-channel chatbot, eval harness, and observability. Ten to twelve weeks for enterprise RAG with permission-aware retrieval, SSO, multi-tenant isolation, and on-prem deployment.

Three modes. Real-time: documents are embedded on ingest via a streaming pipeline (Kafka or webhook to your embedding service). Batch: nightly or weekly re-embedding for slowly-changing corpora. Delta: only changed documents are re-embedded based on content hashes. The right cadence depends on how often your corpus changes — we recommend based on your specific scenario.