Production-Grade Retrieval-Augmented Generation: Designed, Built, and Managed
Building a RAG pipeline that works in a demo is easy. Building one that works reliably at enterprise scale, with accurate retrieval, low latency, proper access control, and continuous data freshness, is an engineering challenge. Our RAG services team designs, builds, and operates custom RAG pipelines tailored to your data, your security requirements, and your accuracy targets.
Document ingestion, chunking strategy selection, embedding model optimization, vector store configuration, and retrieval pipeline architecture tailored to your corpus and query patterns.
Multi-step reasoning workflows where the AI agent decides when to retrieve, what to search for, and how to synthesize results across multiple knowledge sources.
We process your documents (PDFs, wikis, codebases, databases, APIs) into optimized knowledge bases with metadata enrichment, deduplication, and freshness tracking.
Ensure retrieved documents respect user permissions. PII detection and redaction. Toxicity filtering. Injection attack prevention on retrieval queries.
Automated relevance scoring, retrieval accuracy monitoring, and periodic retuning of chunking and embedding strategies as your data evolves.
Combine dense vector search with sparse keyword matching and cross-encoder reranking for maximum retrieval accuracy.
Share your goals and constraints. We'll map a practical path to production.
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