Vector Database Development Services

Power Semantic Search, RAG & Recommendation Systems at Scale

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Build High-Performance Vector Databases for Next-Gen AI Applications

From Pinecone & Weaviate to Milvus & Qdrant — we design, deploy, and optimize production-grade vector databases that power semantic search, RAG pipelines, and real-time recommendation engines.

What is a Vector Database?

A specialized database designed to store, index, and query high-dimensional vector embeddings at scale. Unlike traditional databases, vector DBs use approximate nearest neighbor (ANN) algorithms like HNSW, IVF, and PQ to enable lightning-fast similarity search.

• Sub-millisecond latency on billions of vectors
• Real-time ingestion & hybrid search (vector + keyword)
• Metadata filtering & payload storage
• Built for AI: RAG, agents, recommendation, fraud detection

Vector Databases We Master

Empowering enterprises with high-performance vector intelligence and search capabilities built on leading platforms

Pinecone

Enterprise-grade vector infrastructure

We help businesses deploy scalable semantic search and recommendation systems using Pinecone’s managed vector infrastructure — enabling real-time insights, personalization, and data intelligence at scale.

Weaviate

AI-native knowledge systems

Our team builds AI-powered knowledge retrieval solutions using Weaviate’s hybrid search — connecting structured and unstructured data to power intelligent chatbots, contextual assistants, and discovery tools.

Milvus

Large-scale vector data management

We architect data-intensive AI pipelines on Milvus for clients managing billions of embeddings — driving use cases like similarity search, document intelligence, and multimodal retrieval across industries.

Qdrant

Secure and high-performance retrieval

With Qdrant, we deliver lightning-fast, secure, and cost-effective search solutions for enterprises needing on-prem or hybrid deployments — ideal for privacy-sensitive industries like finance and healthcare.

FAISS

Optimized similarity search

We implement FAISS-based custom retrieval engines for high-performance search and recommendation — enabling faster content discovery, deduplication, and knowledge mapping across data sources.

ChromaDB

Lightweight vector intelligence

For startups and innovation teams, we use ChromaDB to prototype and deploy lightweight embedding-based applications — integrating seamlessly with LangChain, OpenAI, and local knowledge bases.

Real-World Applications We Deliver

Semantic Search

Contextual document retrieval beyond keywords

RAG Pipelines

Ground LLMs with private, up-to-date data

Recommendation Engines

Personalized content & product suggestions

Our Vector Database Implementation Process

Discovery & Design

Workload analysis, embedding strategy, indexing plan

Deployment

K8s, Docker, cloud-native or managed service

Optimization

HNSW tuning, quantization, sharding

Monitoring

Latency, recall, cost dashboards

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