VECTOR DATABASE SETUP
The vector store is the foundation. Get it right.
Pinecone, Weaviate, Qdrant, Milvus, or pgvector — we pick the right engine for your scale and stack, then tune the indexing, filtering, and hybrid search so retrieval is fast and accurate at production volume.
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Enterprise-grade delivery. Human-verified outcomes.
Built for teams that can't afford to guess.
Pinecone, Weaviate, Qdrant, Milvus, or pgvector — we pick the right engine for your scale and stack, then tune the indexing, filtering, and hybrid search so retrieval is fast and accurate at production volume.
Most retrieval problems are vector-store problems.
When a RAG or search system returns the wrong results, the cause is usually upstream of the LLM — in how vectors are indexed, filtered, and queried.
The wrong distance metric, an un-tuned HNSW index, missing metadata filters, or pure-dense search on a corpus full of exact-match terms (IDs, codes, names) all quietly tank precision. We treat the vector database as an engineering problem with measurable answers, not a checkbox you tick once.
Eval harness
Continuous quality on every change
Golden dataset + automated eval — regressions block deploy, not your users.
Choosing the right engine.
What we set up.
Engine selection
Benchmarked on your data volume, query patterns, latency target, and budget — with a TCO comparison.
Index tuning
HNSW/IVF parameters, distance metric, and quantization tuned for your recall/latency tradeoff.
Metadata + filtering
Schema for pre/post-filtering so queries respect tenancy, permissions, and freshness.
Hybrid search
Dense + sparse (BM 25) retrieval fused with reciprocal rank fusion for real-world precision.
Ingestion pipeline
Embedding, upsert, and re-index workflows — including backfills and incremental updates.
Monitoring
Latency, recall, and cost dashboards so you catch drift before users do.
Ways to engage.
Engine recommendation + TCO
Index + metadata design
Ingestion pipeline starter
Hybrid search + reranking
Tuned indexing at your scale
Monitoring + eval harness
Review of an existing setup
Recall + latency + cost findings
Prioritized fix plan
Hybrid retrieval, right in your database.
If you're on Postgres, you often don't need new infrastructure — pgvector plus full-text gets you hybrid search.
We tune the weights, index parameters, and distance metric against your data — and recommend a dedicated engine only when you'll actually feel the difference.
Eval harness
Continuous quality on every change
Golden dataset + automated eval — regressions block deploy, not your users.
The defaults are almost never right.
Out-of-the-box HNSW parameters, the wrong distance metric, and missing metadata filters quietly cap your recall.
We benchmark recall, latency, and cost on your corpus and query patterns, then tune the index until retrieval is both fast and accurate at production volume.
Bring us your retrieval problem.
Tell us your corpus size, query patterns, and latency target. We'll recommend an engine and an indexing strategy — and tell you if you don't need a vector DB at all.
If you need… — We usually recommend
Qdrant or Weaviate
Milvus or Turbopuffer
Turbopuffer / object-store-backed
In-house hire vs. our healthcare team.
Why most healthcare organizations outsource software development instead of building an engineering team from scratch.
| You're already on Postgres | pgvector | no new infra, transactional consistency |
| Zero ops, managed scale | Pinecone | serverless, predictable |
Ready to build?
Let's build your next intelligent platform.
Share your goals — we'll recommend a model, timeline, and team that fits Aanandi Technosoft.
Frequently asked questions
Do we even need a dedicated vector DB?+
Not always. If you're small and on Postgres, pgvector is often enough. We'll tell you when a dedicated engine is worth the operational cost.
Pure-vector or hybrid search?+
Hybrid wins on most real corpora because dense search misses exact-term queries. We default to hybrid and prove it on your data.
Can you fix our existing slow/inaccurate setup?+
Yes — the optimization audit exists for exactly this. Most issues are index parameters, missing filters, or no reranking.
How do you handle multi-tenant data?+
Metadata filtering and, where isolation matters, namespace/collection separation enforced at query time.