Build production-grade multimodal search, RAG pipelines, and semantic retrieval systems using Google's Gemini Embedding 2 — the first natively multimodal embedding model that maps text, images, video, audio, and documents into a single vector space. Our Bengaluru team delivers end-to-end implementation from prototype to production.
Proof-First Delivery
What We Offer
Each module is designed as a production block with integration boundaries, governance hooks, and measurable outcomes.
Multimodal Embedding Architecture Design unified vector spaces that handle text, images, video, audio, and documents together using Gemini Embedding 2. We architect pipelines that support cross-modal search — find images with text queries, match audio to documents, and more. RAG Pipeline Development Build retrieval-augmented generation systems that combine Gemini Embedding 2 with vector databases like Pinecone, Weaviate, or pgvector. Our RAG pipelines handle multimodal knowledge bases — PDFs, images, video transcripts, and structured data in a single retrieval layer. Semantic Search Implementation Replace keyword search with meaning-based retrieval across your content. We implement semantic search over product catalogues, knowledge bases, support tickets, and enterprise documents with sub-second latency at scale. Vector Database Integration Integrate Gemini Embedding 2 with your vector store of choice — Pinecone, Qdrant, Weaviate, Milvus, or PostgreSQL pgvector. We handle indexing strategies, ANN algorithms, metadata filtering, and hybrid search configurations. Migration from Legacy Embedding Models Migrate from OpenAI text-embedding-ada-002, Cohere Embed, or earlier Gemini models to Gemini Embedding 2. We handle re-indexing, dimension mapping, performance benchmarking, and zero-downtime cutover. Production Deployment & Monitoring Deploy embedding pipelines with proper batching, rate limiting, caching, and observability. We set up monitoring for embedding drift, retrieval quality metrics, and cost tracking across Google Cloud.
Deep Google AI Ecosystem Experience We work across the full Gemini model family — embeddings, generation, and multimodal reasoning. Our team understands how to combine Gemini Embedding 2 with Vertex AI, Google Cloud, and BigQuery for enterprise-grade solutions. Production-First Approach We don't build demos. Every embedding pipeline we deliver is designed for production — with proper error handling, batching, caching, fallbacks, and cost controls from day one. Bengaluru Delivery Team Local collaboration with fast iteration cycles for companies building AI products in Bengaluru. Same-timezone coordination with your engineering teams for seamless integration.
Enterprise Knowledge Search Search across documents, presentations, images, and meeting recordings in a single query. Gemini Embedding 2's multimodal capabilities let employees find information regardless of format. E-Commerce Product Discovery Enable visual and textual product search — customers describe what they want or upload a photo and find matching products across your catalogue with high accuracy. Multimodal Content Recommendation Build recommendation systems that understand content across text, images, and video. Recommend articles based on image similarity, or surface video content matching text descriptions. Legal & Compliance Document Analysis Embed contracts, regulatory filings, and compliance documents for semantic retrieval. Find relevant clauses, precedents, and policy sections across thousands of documents instantly.
Delivery Proof
Selected engagements that show architecture depth, execution quality, and measurable business impact.
Delivery Advantages
Multimodal Embedding Architecture Design unified vector spaces that handle text, images, video, audio, and documents together using Gemini Embedding 2. We architect pipelines that support cross-modal search — find images with text queries, match audio to documents, and more. RAG Pipeline Development Build retrieval-augmented generation systems that combine Gemini Embedding 2 with vector databases like Pinecone, Weaviate, or pgvector. Our RAG pipelines handle multimodal knowledge bases — PDFs, images, video transcripts, and structured data in a single retrieval layer. Semantic Search Implementation Replace keyword search with meaning-based retrieval across your content. We implement semantic search over product catalogues, knowledge bases, support tickets, and enterprise documents with sub-second latency at scale. Vector Database Integration Integrate Gemini Embedding 2 with your vector store of choice — Pinecone, Qdrant, Weaviate, Milvus, or PostgreSQL pgvector. We handle indexing strategies, ANN algorithms, metadata filtering, and hybrid search configurations. Migration from Legacy Embedding Models Migrate from OpenAI text-embedding-ada-002, Cohere Embed, or earlier Gemini models to Gemini Embedding 2. We handle re-indexing, dimension mapping, performance benchmarking, and zero-downtime cutover. Production Deployment & Monitoring Deploy embedding pipelines with proper batching, rate limiting, caching, and observability. We set up monitoring for embedding drift, retrieval quality metrics, and cost tracking across Google Cloud.
Deep Google AI Ecosystem Experience We work across the full Gemini model family — embeddings, generation, and multimodal reasoning. Our team understands how to combine Gemini Embedding 2 with Vertex AI, Google Cloud, and BigQuery for enterprise-grade solutions. Production-First Approach We don't build demos. Every embedding pipeline we deliver is designed for production — with proper error handling, batching, caching, fallbacks, and cost controls from day one. Bengaluru Delivery Team Local collaboration with fast iteration cycles for companies building AI products in Bengaluru. Same-timezone coordination with your engineering teams for seamless integration.
Enterprise Knowledge Search Search across documents, presentations, images, and meeting recordings in a single query. Gemini Embedding 2's multimodal capabilities let employees find information regardless of format. E-Commerce Product Discovery Enable visual and textual product search — customers describe what they want or upload a photo and find matching products across your catalogue with high accuracy. Multimodal Content Recommendation Build recommendation systems that understand content across text, images, and video. Recommend articles based on image similarity, or surface video content matching text descriptions. Legal & Compliance Document Analysis Embed contracts, regulatory filings, and compliance documents for semantic retrieval. Find relevant clauses, precedents, and policy sections across thousands of documents instantly.
FAQ
Ready to implement multimodal search and RAG with Google's latest embedding model? Talk to our Bengaluru team about your use case.