Question 1

How much do custom RAG development services cost?

Accepted Answer

Costs vary by document volume, security requirements, and integration complexity — a single-source internal knowledge base runs differently than a multi-tenant, HIPAA-aligned deployment. Pricing is scoped after the discovery and data audit phase, once retrieval complexity and infrastructure requirements are clear.

Question 2

Why choose custom RAG over fine-tuning an LLM?

Accepted Answer

Fine-tuning bakes knowledge into model weights at a fixed point in time, so it goes stale the moment data changes and requires retraining to update. RAG retrieves current information at query time, making it cheaper to keep accurate and easier to audit — every answer can be traced back to a specific source document.

Question 3

How do you prevent data leaks and enforce permissions inside vector search?

Accepted Answer

Document-level RBAC is enforced at the retrieval layer, filtering by user permission scope before ranking occurs, not after. This ties into existing identity providers such as Okta or Azure AD so vector search never surfaces documents outside a user's authorized access.

Question 4

What vector databases and embedding models do you recommend?

Accepted Answer

It depends on scale and constraints: Pinecone for managed infrastructure and fast setup, Qdrant for self-hosted deployments needing fine-grained filtering, and Milvus for large-scale on-prem systems. Embedding model choice follows similar logic, with domain-specific fine-tuned models outperforming general-purpose ones once query volume justifies the investment.

RAG Development Services Built for Production, Not Demo Day