Retrieval-Augmented Generation (RAG)
Production-grade RAG systems with reranking, hybrid search, and verifiable document citations.
Operational Problems Solved
We build advanced RAG architectures that transcend basic toy demos. Utilizing hybrid dense/sparse search, Cohere reranking, and semantic chunking to deliver sub-second, accurate answers.
System Architecture & Execution Flow
DETERMINISTIC PIPELINEIngest & Index
Documents indexed with dense vector embeddings and BM25 sparse tokens.
Hybrid Retrieve
Simultaneous keyword and vector lookup retrieving top-25 candidate passages.
Rerank
Cross-encoder scores candidate passages down to top-5 most relevant chunks.
Grounded Generation
Generates answer strictly anchored to retrieved contexts with token tracking.
What We Deliver
Human-in-the-Loop Review Points
- Confidence threshold checks: answers below confidence threshold prompt user to refine keywords
- Automated evaluation suites comparing model output against verified ground-truth QA pairs
Data Privacy & Security Boundaries
- End-to-end TLS encryption and disk encryption at rest (AES-256)
- Dedicated private embedding generation without external document retention
Technical Questions
What is the difference between RAG and fine-tuning?
Fine-tuning teaches a model new style, tone, or specialized vocabulary, but is expensive to update and cannot reliably cite sources. RAG retrieves your live, constantly updated documents on the fly, providing exact citations and instantaneous updates without retraining.
Scope a Production Pilot
We typically deliver a functional staging proof-of-concept for this capability within 2–3 weeks.
Recommended Tech Stack
Ready to scope your Retrieval-Augmented Generation (RAG)?
Share your current tech stack and dataset requirements. We will prepare an architecture proposal within one business day.
Zero obligation • Direct technical conversation with engineers • NDA upon request