Technology
Qdrant
Qdrant implementation for production software delivery with clean architecture, maintainability, and predictable rollout.
Best For
Ideal use cases
Teams wanting self-hosted control for vector search
Products requiring private/VPC deployment options
Systems needing strong metadata filtering and performance tuning
What We Build
Projects we deliver
Qdrant deployment and operations setup
Schema design with metadata filters
Ingestion pipelines and retrieval tuning
Ecosystem
Compatible tools & integrations
Seamless Integrations
Works with your existing stack
Use Cases
Recommended use cases
Private knowledge assistants
Semantic search for internal tools
RAG systems with tenant isolation needs
Delivery
How we deliver
We deploy Qdrant with monitoring, backups, and runbooks when self-hosted.
Retrieval is tuned using a query set and measurable targets.
We document operations so teams can maintain reliability over time.
FAQ
Frequently asked questions
Yes. With proper deployment, monitoring, and tuning, Qdrant is production-ready for many retrieval workloads.
Yes. Qdrant is a common choice for private deployments where you want more control over data boundaries.
Yes. We can pair vector retrieval with keyword search layers and reranking where it improves relevance.
AI
Add AI on top of this stack
Two common AI services that pair well with this technology, plus a fixed-scope gig to start quickly.
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