Vector Database Development and Semantic Search
Embeddings pipelines, vector databases and semantic search that power RAG assistants, product discovery and recommendations, designed for accuracy, speed and sensible running costs.
- Pinecone, pgvector, Qdrant, Weaviate, Milvus
- Hybrid keyword + semantic search
- Embedding pipelines that stay in sync
- Benchmarked for speed and cost
Rated 5.0 on Clutch & 4.8 on GoodFirms by verified clients in the USA, UK, Australia, Europe & India
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The search layer behind useful AI
- Retrieval for RAG assistants and chatbots
- Semantic product search for online stores
- Similar item and content recommendations
- Document and contract search
- Duplicate and anomaly detection
Vector database development is the part of an AI project most teams underestimate. Every RAG assistant, semantic search box and recommendation engine depends on turning content into embeddings, storing them well and retrieving the right items in milliseconds.
We choose the right store for your scale and stack, whether that is pgvector inside your existing PostgreSQL, a managed service like Pinecone, or self-hosted Qdrant, Weaviate or Milvus. Then we build the pipelines that keep vectors in sync with your source data, and tune retrieval with filters, hybrid search and re-ranking.
The result is search that understands meaning, not just keywords, with the monitoring and benchmarks you need to trust it in production.
Vector database services
Every engagement starts with a free consultation and a written, fixed quote.
Database selection
An honest comparison of pgvector, Pinecone, Qdrant, Weaviate, Milvus and Elasticsearch for your data size, latency, hosting and budget.
Embedding pipelines
Chunking, metadata and embedding generation with batch and real-time updates, so the index always matches your source data.
Semantic and hybrid search
Meaning-based search combined with keyword matching, filters and facets for results people actually expect.
Re-ranking and relevance tuning
Re-ranking models, query rewriting and test sets to measure and improve relevance over time.
Ecommerce product discovery
Semantic product search, similar-item and recommendation features for Shopify, Magento and custom stores.
Migration and scaling
Moving between vector stores, sharding, index tuning and cost optimisation as data and traffic grow.
Planning semantic search or a RAG project?
Share your data size and use case. We will recommend the right vector database and send a fixed quote for the build.
- 100% free
- No obligation
- Reply within 24 hours
Why it matters
Better search results
Users find what they mean even when they use different words from your content.
Accurate AI answers
Good retrieval is the single biggest factor in whether a RAG assistant answers correctly.
Fast at scale
Indexes and infrastructure sized so queries stay fast as your data grows into millions of items.
Predictable costs
Right-sized hosting, embedding choices and caching keep monthly costs under control.
- pgvector
- PostgreSQL
- Pinecone
- Qdrant
- Weaviate
- Milvus
- Elasticsearch
- OpenSearch
- Redis
- OpenAI embeddings
- Cohere
- Sentence Transformers
- Python
- Node.js
From first call to launch, with no surprises.
Clear milestones, weekly demos and a fixed price agreed before any work starts.
Assess
We review your data, query types, volume, latency targets and hosting constraints.
Benchmark
Short tests of candidate databases and embedding models on your real data.
Build pipelines
Ingestion, embeddings, indexing and sync jobs with error handling and logging.
Tune relevance
Hybrid search, filters and re-ranking measured against a test set of real queries.
Operate
Monitoring, scaling, backups and cost reviews after launch.
Senior engineering, with the safeguards that make outsourcing safe.
7+ years of building and maintaining software for clients in the USA, UK, Europe, Australia and India. Recognised by Clutch as a top development company.
Pay per milestone
You never pay for work you have not seen and approved.
You own everything
Full source code, designs and accounts are transferred to you. No lock-in.
NDA before you share
We sign your NDA before you tell us about your idea. Your IP stays yours.
Full transparency
Weekly demos, a shared task board and direct access to your developers.
Your time zone
Overlapping hours with the USA, UK, Europe, Middle East and Australia.
Senior engineers
Your project is built by experienced developers, not trainees.
What clients say about working with Ravindra and the team.
Verified reviews from Clutch, GoodFirms, LinkedIn and long-term clients in the USA, UK, Australia, Europe and India.
“I've been working with Ravindra and the Ecomsolver development team consistently for several years now and will continue to do so. During this time, the team has worked across the Magento, Shopify and WordPress platforms and on everything from small changes to full website builds. Throughout this period the team has continued to provide excellent service and inventive, skillful solutions…”
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Vector Database Development: frequently asked questions
Ask us on WhatsApp. A real person replies, usually within minutes during working hours.
Ask on WhatsAppWhat is vector database development?
Vector database development means designing and building the system that stores embeddings, numeric representations of text, images or products, and retrieves the most similar items quickly. It includes choosing the database, building embedding pipelines, tuning search relevance and running it reliably in production.
Do we need a separate vector database?
Not always. If you already use PostgreSQL and your data is moderate in size, pgvector is often the simplest and cheapest option. Dedicated databases like Pinecone, Qdrant or Weaviate make sense for very large datasets, heavy traffic or advanced filtering. We recommend based on a quick benchmark with your data.
Can you add semantic search to our online store?
Yes. We build semantic and hybrid product search and recommendations for Shopify, Magento and custom stores, so shoppers find products from natural descriptions and see relevant similar items.
How do you keep vectors in sync with our data?
We build pipelines that update embeddings when source records change, using webhooks, scheduled jobs or change-data-capture, with logging so failed updates are caught and retried.
Which embedding model should we use?
It depends on language, content type, accuracy needs and budget. We test a few options, from hosted APIs to open-source models, on your real queries and pick the best balance of quality and cost.