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Get retrieval right and everything built on it improves. Talk to ProsperaSoft search engineers about your vector database and semantic search design.
Vector Search Engineering
We work with dedicated vector databases such as Pinecone, Qdrant, Weaviate and Milvus, with pgvector in PostgreSQL, and with vector search in Elasticsearch, OpenSearch and Solr. The right choice depends on data size, filtering needs, update rates, hosting and the systems you already run.
Our background is enterprise search: relevance tuning, analyzers and ranking with Elasticsearch, OpenSearch and Solr. We combine that with embeddings and re-ranking to build hybrid search that beats pure vector or pure keyword search.
Why ProsperaSoft for Vector Databases
Many teams add a vector database and discover that results are still poor. Retrieval quality depends on chunking, metadata, filters, hybrid ranking and evaluation, which is search engineering we have done for years.
We measure relevance with test queries from your users, benchmark latency and cost at your data volumes, and avoid adding a new database when your existing PostgreSQL or Elasticsearch can do the job.
Vector Database Services
Architecture and Selection
Compare pgvector, Pinecone, Qdrant, Weaviate, Milvus, Elasticsearch and OpenSearch for your workload and budget.
Embedding and Chunking Design
Choose embedding models, chunk sizes and metadata so the right passages are retrieved.
Hybrid Search and Re-ranking
Combine BM25 keyword search, vector similarity and re-ranking models with filters and boosting.
Migration and Integration
Move vectors between databases, add vector search to existing Elasticsearch or PostgreSQL, and connect RAG pipelines.
Performance and Cost Tuning
Index types, quantization, sharding and replicas tuned for latency, recall and memory cost.
Relevance Evaluation
Test sets and metrics such as recall and nDCG to track search quality with every change.
Vector Search Use Cases
RAG Assistants
Retrieve the right passages for AI answers over company documents.
Site and Product Search
Search that understands meaning and synonyms, not only exact words.
Recommendations
Similar products, articles and content based on embeddings.
Deduplication and Matching
Find near-duplicate records, documents and support tickets.
TECHNICAL EXPERTISE
Frequently asked questions
Do we need a dedicated vector database?
Not always. PostgreSQL with pgvector, Elasticsearch and OpenSearch handle many workloads well. A dedicated vector database makes sense at very large scale or with specific latency and filtering needs.
What is hybrid search?
Hybrid search combines keyword ranking (BM25) with vector similarity, often followed by a re-ranking model. It usually outperforms either method alone, especially for product names, codes and exact terms.
Which vector database is best?
There is no single best one. We compare options on your data for recall, latency, filtering, update speed, hosting model and cost, and recommend the simplest option that meets your needs.
How do you measure search quality?
We build a set of real queries with expected results and track metrics such as recall, precision and nDCG, so every change to chunking, embeddings or ranking is measured.
Can you add vector search to our existing Elasticsearch?
Yes. Elasticsearch and OpenSearch support dense vectors and approximate nearest neighbour search, so we can add semantic and hybrid search without a new database.