Adaptive RAG Playground
Enterprise multi-document causal reasoning across operational reports. Watch semantic search and graph traversal compete on the same query — then see the auto-router explain why it chose each method.
BFS across 201 entities and 175 relationships extracted from 8 operational documents.
Semantic and graph responses side by side — see which retrieval method wins and why.
Auto-router explains its choice: keyword signals, named entity count, and confidence.
201
entities
175
relationships
8
documents
20
sample queries
BrewPulse Coffee
Real enterprise knowledge looks like this: incident reports, supplier updates, customer feedback, staffing memos — scattered across systems, written by different people, never designed to be queried together.
BrewPulse Coffee simulates exactly that. The only way to answer "why did Leeds underperform in Q1?" from these eight documents is to read all of them manually — or use RAG.
Synthetic corpus, manually curated. Full methodology and engineering decisions on the dataset page.
02_incident_leeds.md
Espresso Machine Failure — Leeds Central
Formal incident log INC-2024-0312. The technical anchor for cross-document reasoning.
03_supplier_northbrew.md
NorthBrew Supplies Oat Milk Disruption
Wakefield depot logistics failure. Documents a prior Sep 2023 incident — critical for recurrence detection.
04_customer_feedback.md
Customer Feedback Summary
47 submissions, 61% negative. "Watery espresso" complaints — never explicitly linked to the valve fault.
08_logistics_mobile.md
Mobile Ordering Rollout Disruption
Orda POS modifier sync bug. Bridges the supplier thread and technology thread across the corpus.
THE CENTRAL CAUSAL CHAIN — Q20 DEMO HIGHLIGHT
Wakefield depot systems migration fails
file 03
40% oat milk shortfall at Leeds
files 01, 03
Mobile oat flat white order unfulfillable
file 08
Formal complaint + Google Review posted
file 04
No single document tells this story. Graph traversal reconstructs it; semantic search misses hops 2 and 3.