Meet Magus - The Context Engine That Never Skips Facts
Standard RAG systems retrieve 20% of relevant information on an average and get half their answers wrong.
Magus retrieves more than 90% relevant information while maintaining extremely high accuracy 95%+ by validating every answer twice.
Built by the team that scaled AI at Microsoft, Lyft, and Salesforce.
Why Standard RAG Fails
Most enterprise AI tools use naive Retrieval-Augmented Generation (RAG). The problem? RAG retrieves information based on smart guesses not relevance - signal mixed with noise - and dumps it into the LLM hoping for the best.
The result:
20% recall (misses 80% of relevant information)
55% accuracy (nearly half the answers are wrong or incomplete)
Hours of manual review that erase productivity gains
9 out of 10 enterprise AI pilots stall in first phase due to inaccuracy
"Hopes for the best" isn't a strategy when you're responding to a $500M RFP.
The Magus Difference
Magus doesn't just retrieve and generate. It validates information twice - before the LLM sees it and after it generates output.
Think of it as building with the precision of search-grade systems, not the guesswork of standard RAG.
Step 1: Knowledge Graph
Clean, Structured Foundation
Your enterprise data organized into a lossless, traceable representation. Facts, policies, and version history - all connected at the source.
Step 2: Context Relevance Model
Pre-LLM Filtering
Before generation begins, this layer identifies precisely what matters. No noise. No outdated documents. Just relevant context.
The Proof
Not Incremental. Transformational.
