AI & ML
Vector DBs
Semantic retrieval that grounds models in your private data.
How Shriram IT Ventures uses Vector DBs in production products — architecture choices, delivery patterns, and when we recommend it.
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What is Vector DBs?
How Shriram IT Ventures uses Vector DBs in production products — architecture choices, delivery patterns, and when we recommend it.
What is Vector DBs?
Why we use Vector DBs
Semantic retrieval that grounds models in your private data. We pick Vector DBs when it improves shipping speed, reliability, or maintainability for the problem at hand — not because it is fashionable.
How we deliver with Vector DBs
Discovery maps constraints and success metrics. Engineering ships iteratively with review gates, observability, and documentation your team can own after launch.
When Vector DBs is the right fit
We recommend Vector DBs 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
Knowledge bases
Semantic search
Use cases
Knowledge bases
Semantic search
How Vector DBs works
API information
Common stack pairings
Vector DBs + OpenAI
Common pairing in AI & ML engagements.
Learn more →Vector DBs + Claude API
Common pairing in AI & ML engagements.
Learn more →Vector DBs + LangChain
Common pairing in AI & ML engagements.
Learn more →Vector DBs + RAG pipelines
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.
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Related services
Capabilities where Vector DBs shows up in real delivery work.
Build with Vector DBs
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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