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Adaptive RAG Playground

Portfolio demo · AI retrieval research

Compare semantic search and graph traversal on the same query, then see the auto-router explain why it chose each method.

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 a retrieval system like this one (RAG).

Try the Demo

20 ready-made questions · one click to run

Details below

201

entities

175

relationships

8

documents

20

sample queries

Built on production-grade AI infrastructure

Next.js 16Claude Haiku 4.5Voyage AI voyage-3pgvectorSupabaseVercel
SYNTHETIC ENTERPRISE CORPUS

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.

Full corpus documentation →

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.

Graph Traversal

201 entities, 175 relationships, 8 documents — follows the chain.

Comparison Mode

Same query, two methods, side by side. See which one wins.

Decision Log

The router explains its choice — signals, entity count, confidence.

THE CENTRAL CAUSAL CHAIN — QUESTION 20 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.

COMPARE MODEsample output · Q20
auto router →graph3 named entities detected
semantic3 docs retrieved

NorthBrew Supplies experienced delays at the Wakefield depot in early Q1, impacting oat milk deliveries to North England branches. Customer feedback noted supply-related complaints across several locations.

files: 01, 03, 07

↳ fragments retrieved, chain incomplete

graph4 docs · 2 hops

The Wakefield depot systems migration (file 03) caused a 40% oat milk shortfall at Leeds. This made the oat flat white unfulfillable via mobile ordering (file 08), generating a formal complaint and a negative Google Review (file 04).

files: 01, 03, 04, 08

↳ full 4-hop chain reconstructed

Graph found file 04 (Google Review complaint) and file 08 (mobile order) via entity chain — semantic missed them because "Google Review" doesn't appear in file 03.

Same query, two methods — graph reconstructs what semantic misses.

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This project built with Claude Code agentic workflow · CLAUDE.md context preservation · custom extractor via Claude tool use