Vector Database Development
A vector database stores numerical representations of text, images or products so software can search by meaning rather than exact words. We design and run vector databases such as Pinecone, Weaviate, Qdrant, Milvus and PostgreSQL with pgvector for AI search, RAG and recommendations.
The right choice depends on data size, budget, hosting needs and whether you already run PostgreSQL. We often start with pgvector for simplicity and move to a dedicated database when scale requires it.
What we offer
- Database selectionManaged versus self-hosted, cost and scale.
- Embedding pipelinesConvert documents and products into vectors.
- Hybrid searchCombine keyword and meaning-based search.
- Access controlFilter results by user permissions.
- Performance tuningIndex settings for speed and accuracy.
Common use cases
- RAG knowledge assistants
- Semantic site search
- Similar-product search
- Duplicate detection
Technology
- Pinecone
- Weaviate
- Qdrant
- Milvus
- pgvector
- Elasticsearch
- Python
Business benefits
- Semantic searchthat finds results by meaning
- The foundation for RAGand AI assistants
- Similar-item recommendationsfor products and content
- Fast at scale,even with millions of records
Why work with Webtech Evolution
- 10+ years of delivery272+ projects for 118+ clients in 12+ countries since 2014.
- One team, end to enddesign, front end, back end, mobile, QA and DevOps under one roof, so nothing gets lost between vendors.
- Clear estimatesa written scope, timeline and price before work starts, and demos throughout the build.
- You own everythingcode, designs and documentation are handed over in full and covered by an NDA.
- Working hours that overlap with yoursMonday to Friday, 10:00–19:00 IST, with extended hours available for clients in the USA, Canada and New Zealand.
How we deliver
Discovery
agree the goal, users, data and how success will be measured.
Prototype
a working version on real examples within the first weeks.
Build and integrate
connect to your systems, add security, testing and monitoring.
Pilot
launch to a small group, measure results and improve.
Scale and support
roll out widely, with ongoing monitoring and updates.
Need people rather than a project? Hire AI developers.
Frequently asked questions
Not always. If you use PostgreSQL, pgvector may be enough. Large or fast-growing collections may need a dedicated vector database.
It depends on scale, budget and hosting. We compare options for your workload.
Yes, with proper access control, encryption and hosting in your chosen region.
pgvector if you already use PostgreSQL; Pinecone for a fully managed service; Weaviate, Qdrant or Milvus for advanced features or self-hosting. We recommend based on scale and budget.
Not always. PostgreSQL with pgvector, MongoDB Atlas or Elasticsearch can store vectors alongside your existing data.
Let's Talk!
Have a question about Vector Database Development? Send us a message and our team will reply with next steps.
- Reply within a few hours (Mon–Fri)
- NDA available
- You own the code
Prefer to talk? Call +91-9601965456WhatsApp ushello@webtech-evolution.com
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