AI-Powered Search Solutions

Search & Retrieval Systems

Build intelligent search systems with vector embeddings, semantic search, and AI-powered retrieval

<50ms
Query Latency
99%
Accuracy
1B+
Vectors
10x
Faster Retrieval

Vector Search & Embedding Solutions

Semantic Search Engine

Understanding Context & Intent

AI-powered search that understands meaning, not just keywords, using state-of-the-art embeddings.

  • Multi-lingual embedding models
  • Context-aware ranking
  • Query expansion & synonyms
  • Intent classification

Hybrid Search System

Best of Both Worlds

Combine vector similarity with traditional search for optimal results across all query types.

  • BM25 + Vector fusion
  • Reciprocal rank fusion
  • Dynamic weight optimization
  • Faceted filtering

RAG Systems

Retrieval Augmented Generation

Build intelligent Q&A systems that retrieve relevant context for accurate LLM responses.

  • Document chunking strategies
  • Context window optimization
  • Multi-hop reasoning
  • Source attribution

Multimodal Search

Text, Image & Beyond

Search across different data modalities using CLIP and multimodal embeddings.

  • Text-to-image search
  • Image similarity search
  • Cross-modal retrieval
  • Video frame search

Vector Database Infrastructure

MongoDB Atlas

Document database with native vector search capabilities for AI applications.

  • Vector Search indexes
  • Atlas Search integration
  • Flexible data model

Pinecone

Fully managed vector database with seamless scaling and high performance.

  • Serverless architecture
  • Real-time indexing
  • Metadata filtering

Weaviate

Open-source vector database with built-in ML models and hybrid search.

  • GraphQL API
  • Module ecosystem
  • Multi-tenancy

Also Supporting

ChromaDB

Milvus

FAISS

Elasticsearch

Embedding Models & Technologies

Text Embeddings

OpenAI text-embedding-33072 dims
Cohere Embed v31024 dims
Google Vertex AI Embeddings768 dims
Sentence Transformers384-768 dims

Multimodal Embeddings

CLIP (OpenAI)Text + Image
ALIGN (Google)Cross-modal
ImageBind (Meta)6 modalities
Gemini VisionText + Vision

Implementation Process

1

Data Analysis

Content profiling & requirements

2

Model Selection

Choose optimal embeddings

3

Infrastructure

Deploy vector database

4

Optimization

Fine-tune & scale

Real-World Applications

E-commerce Search

Product discovery with visual and semantic search

Knowledge Base Q&A

Intelligent document retrieval and answering

Content Recommendation

Personalized content discovery systems

Fraud Detection

Similarity-based anomaly detection

Code Search

Semantic code similarity and retrieval

Customer Support

Smart ticket routing and solution matching

Elevate Your Search

Build Intelligent Search Systems

Transform how users discover and retrieve information

Whether you need semantic search, RAG implementation, or multimodal retrieval, I'll help you build search systems that understand context and deliver relevant results.