Billion-Scale Semantic Search & RAG

Billion-scale indexing, Vector Search, and Graph RAG architectures for complex data retrieval.

Vector Search Engine Implementation: Building high-performance discovery engines that use vector databases (e.g., Pinecone, Milvus, Qdrant) to enable search by meaning rather than just keywords.
Billion-Scale Indexing Solutions: Designing architectures capable of handling billion-scale image, text, and document knowledge bases using embedding-based search.
Hybrid Search Architectures: Implementing systems that combine the precision of classic keyword search (e.g., Elasticsearch, Opensearch) with the conceptual understanding of vector search.
Multimodal Discovery Engines: Creating unified embedding spaces that allow users to search across text, images, audio, and video simultaneously.

Enterprise RAG Pipelines: Developing end-to-end Retrieval-Augmented Generation systems that ingest vast internal documentation to provide accurate, fact-based answers.
Graph-Based RAG: Leveraging knowledge graphs to improve the quality of information retrieval.
Agentic RAG Workflows: Deploying “agentic” pipelines (using tools like ragbits) that allow AI assistants to autonomously plan and execute multi-step information retrieval tasks.

Automated Document Intelligence: Systems that automatically extract, classify, and summarize details from unstructured data (PDFs, contracts, spreadsheets).
Consultant/Operational Assistants: Centralized “pointers” to operational knowledge.
Semantic Data Curation & Enrichment: Cleaning and transforming legacy data into AI-ready formats.

Self-Hosted Knowledge Repositories: Setting up private models and databases to keep sensitive company secrets internal.
Sector-Specific Search Solutions: Custom-tuned search and retrieval systems for highly regulated or technical industries.