AI & ML
RAG pipelines
Chunking, retrieval, and grounding patterns that reduce hallucinations.
How Shriram IT Ventures uses RAG pipelines in production products — architecture choices, delivery patterns, and when we recommend it.
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What is RAG pipelines?
How Shriram IT Ventures uses RAG pipelines in production products — architecture choices, delivery patterns, and when we recommend it.
What is RAG pipelines?
Why we use RAG pipelines
Chunking, retrieval, and grounding patterns that reduce hallucinations. We pick RAG pipelines when it improves shipping speed, reliability, or maintainability for the problem at hand — not because it is fashionable.
How we deliver with RAG pipelines
Discovery maps constraints and success metrics. Engineering ships iteratively with review gates, observability, and documentation your team can own after launch.
When RAG pipelines is the right fit
We recommend RAG pipelines when the product shape, team skills, and operating model align. If another tool fits better, we say so early — with a clear trade-off narrative.
Key capabilities
Enterprise search
Support copilots
Use cases
Enterprise search
Support copilots
How RAG pipelines works
API information
Common stack pairings
RAG pipelines + OpenAI
Common pairing in AI & ML engagements.
Learn more →RAG pipelines + Claude API
Common pairing in AI & ML engagements.
Learn more →RAG pipelines + LangChain
Common pairing in AI & ML engagements.
Learn more →RAG pipelines + Vector DBs
Common pairing in AI & ML engagements.
Learn more →Considerations
Security considerations
Engagement model
Custom quote
We quote delivery work (discovery, build, hardening, handover) after a short technical review.
Project pricing depends on scope, integrations, and SLA — not the open-source or vendor license alone.
Get a quoteRelated services
Capabilities where RAG pipelines shows up in real delivery work.
Build with RAG pipelines
Tell us the constraint that actually hurts — hiring, compliance, speed, or integrations — and we’ll answer with a plan, not a pitch deck.
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