AI Research Platform
AI Engineering · Data Engineering
Overview
A production RAG system that indexes 100M+ documents from 20,000+ sources and makes them queryable through an LLM-powered research assistant, with sub-300ms p99 latency at web scale.
Challenge
The client needed 100M+ documents (academic papers, patents, regulatory filings, and news articles) consolidated into a single searchable knowledge base. Keyword search missed semantic connections between documents and couldn't handle the volume or velocity of new data arriving daily.
Solution
We built a multi-stage ingestion pipeline that normalizes, chunks, and embeds documents into a vector database. A hybrid retrieval system combines dense vector search with sparse keyword matching for high recall and precision. The LLM-powered research assistant synthesizes results into coherent answers with source citations, and the entire system is optimized for throughput with async processing, batched embeddings, and intelligent caching.
Results
100M+
Documents indexed
20K+
Sources integrated
<300ms
p99 query latency
99.9%
Uptime SLA