Collection design, hybrid dense + sparse search, quantization and scaling for RAG systems, agents and semantic search. Our background is search engineering, so we treat a vector database as a search engine: measured on your queries, tuned for recall, latency and cost.
Embeddings get you started. Production retrieval needs the right schema, filters, fusion and memory budget - and a way to prove each change helps.
Evaluation sets and offline metrics, so every collection or ranking change is proven on your queries.
Dense and sparse vectors on the same point, fused and rescored in one query.
Quantization, rescoring and on-disk storage chosen against measured recall, not defaults.
Payload indexes planned up front, so filters and tenants stay fast as data grows.
The vector layer behind assistants and agents that must answer from your data, with citations.
Semantic and hybrid search across documents, wikis and support content.
Similarity and hybrid search that combines vectors with catalog filters.
One cluster serving many customers, with tenant isolation that stays fast.
Several embeddings per item - text, image - stored as named vectors on one point.
Moving vectors and metadata in, with retrieval quality compared before cutover.
Review your data, queries and current engine, and agree on recall, latency and cost targets.
Build the collection, hybrid queries and quantization settings, and test them on your real queries.
Shard, replicate and roll out with benchmarks, monitoring and a safe migration path.
Ongoing retrieval tuning and capacity planning - or a clean handover with runbooks and training.
Designing, building and running Qdrant in production: engine selection, collection and payload design, hybrid dense + sparse search, quantization and memory tuning, filtering performance, sharding and replication, migrations into Qdrant, RAG and agent integration, and retrieval evaluation.
Choose Qdrant when vector retrieval is the core workload and you want a dedicated engine for it: named and multi-vectors per point, sparse vectors, quantization options and filtering built into the vector index. Stay on Elasticsearch when you also rely on its lexical analyzers, aggregations or logs and analytics, or when adding vectors to an existing cluster is simpler than running a second system.
Yes. Qdrant stores dense and sparse vectors on the same point, and its Query API prefetches candidates from each and fuses them with Reciprocal Rank Fusion (RRF) or Distribution-Based Score Fusion (DBSF). Multi-stage queries can also rescore candidates with a larger vector or a multi-vector model.
Mostly with quantization and storage placement. Qdrant supports scalar, binary and product quantization, keeps the original vectors so results can be rescored, and lets originals live on disk. We pick the settings by measuring recall and latency on your own queries, not by default.
Yes. Qdrant provides a migration tool that supports these sources among others. The data copy is the easy part; we also redesign the collection, re-create filters and ranking, and compare retrieval quality before and after so the cutover is a measured decision.
An architecture or performance review typically takes 1-2 weeks. A new build or a migration typically takes 4-12 weeks depending on data volume and complexity. We agree on timelines and success metrics up front.
Tell us about your data, queries and current setup. In a free 30-minute call we will review where you are, what it would take, and how we would measure success.
