Question 1

Why do most enterprise RAG implementations fail in production?

Accepted Answer

The retrieval layer is where the system fails. Pure vector search misses exact-match queries. Pure keyword search misses semantic equivalents. Naive top-k vector similarity into an LLM fails 30 to 40 percent of the time. The fix is hybrid retrieval: dense, sparse, and a reranker, plus an evaluation harness that catches regressions before they productionize. Most teams build the LLM layer first and find the retrieval problem the week after the demo.

Question 2

Which vector database should we use?

Accepted Answer

It depends on workload. Under 5 million vectors with managed simplicity, Pinecone. Multi-modal or native hybrid search, Weaviate. Self-hosted with strong filtering, Qdrant. Vectors next to your existing PostgreSQL, pgvector. We pick after profiling your corpus, query patterns, and operational preferences. Not based on which vendor is loudest this quarter.

Question 3

How do you handle PII and regulated data in the retrieval layer?

Accepted Answer

Three layers. Document classification at ingest tags PII, sensitivity level, and regulated-data markers. Access-control-aware retrieval filters every query by the user's permission scope before retrieval, not after. The audit trail logs which documents were considered, retrieved, and returned, with groundedness on the response. This pattern is what SR 11-7, HIPAA, GDPR, and most enterprise classification policies require.

Question 4

What's the typical engagement cost and timeline?

Accepted Answer

Scoped pilot RAG, one corpus and one use case, runs $300K to $600K over 6 to 10 weeks to production. Enterprise RAG platform with multi-corpus, shared evaluation infrastructure, and governance runs $800K to $2M over 4 to 6 months. Pricing assumes a 3 to 5 person pod working with your team.

Question 5

Do you build only on cloud platforms, or open-source?

Accepted Answer

Both. We've productionized on AWS Bedrock with Knowledge Bases, Azure AI Studio with Azure AI Search, GCP Vertex AI Search, and pure open-stack (LangGraph plus open vector DB plus open embedding model). The selection follows your existing cloud commitment, data residency rules, and TCO math. Not preference.

Question 6

Can you work alongside our internal AI/ML team?

Accepted Answer

Yes. That's how we prefer to work. Our engineers pair with yours on retrieval design, evaluation infrastructure, and operational hardening. By the end of the engagement your team owns the architecture and can extend it without us.

RAG Systems & AI Agents

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Ready to begin?