| # Cortex Demo Script |
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| **Link:** https://sk-aiu-cortex.hf.space · **Access PIN:** from the demo owner · **Format:** live walkthrough, with a recording as backup |
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| ## Before you start (10 minutes ahead) |
| 1. Open the link. If you see "warming up", wait 1-3 minutes (the Space sleeps when idle). |
| 2. **Settings → Access PIN → Unlock.** The header should show "Shared: 20 of 20 left today". |
| 3. Ask sample question 1 once. The answer is then cached, so it's instant and $0 during the demo. |
| 4. Keep a second tab on **Scenarios** as a fallback if the LLM is busy. |
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| ## Executive track (10 minutes) |
| | Min | Screen | Say | Show | |
| |---|---|---|---| |
| | 0-1 | Overview | "Most AI here finds *similar text*. Real questions need *connected facts*. Same 73 PDFs, same model; the only difference is the graph." | Header stats: 1M+ nodes, 0.4 ms query | |
| | 1-4 | Ask Cortex, question 1 (EU AI Act) | "Left is today's RAG. Right is GraphRAG on FalkorDB." | GraphRAG lists all 6 controls, 4 procedures and 2 open findings. Point at the sources and the tokenomics strip | |
| | 4-5 | Engineer view → Evaluation | "We measured it on 34 questions with known answers." | **97% vs 70%** recall · **31 vs 19** fully correct · multi-hop **97% vs 47%** · same tokens per correct answer | |
| | 5-6 | Compare two roles (CRO vs Branch Teller) | "Security is in the graph. The teller never sees audit findings; the model never even receives them." | "N sources withheld" | |
| | 6-8 | Scenarios → Fraud rings (role: Compliance) | "Same database, beyond RAG: 60 hidden fraud rings in a million records, in under 2 seconds, no false alarms." | Then switch to Branch Teller: access denied | |
| | 8-9 | Scenarios → Regulatory impact / Risk roll-up | "A new regulation lands: here's everything it touches, instantly. And the bank's risk score, from one control up to the board." | Impact table; bank score 60/100 | |
| | 9-10 | Close | "Free to run, open to inspect, and you can try it yourself today." | One-pager with the link | |
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| ## Engineer track (5 extra minutes) |
| 1. **Engineer view → Pipeline:** LangChain loader and splitter, local embeddings, and pattern extraction (295/295 relationships correct, 0 tokens). |
| 2. **Last queries:** the exact Cypher for vector and GraphRAG retrieval. |
| 3. **Performance panel:** FalkorDB execution times on the 1M-node graph. |
| 4. **X-Ray:** every layer of the stack with its source file, live FalkorDB internals (version, memory, graphs, labels, indexes), real GRAPH.EXPLAIN / GRAPH.PROFILE plans, and the life of a question with timings. |
| 5. **Upload:** drop a PDF and ask about it. It goes into a private sandbox graph that's wiped after 24 hours. |
| 6. **Code:** `github.com/Cosmius-SK/cortex/tree/main/cortex`. No Docker needed: `python app.py` runs it all locally. |
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| ## Likely questions |
| | Question | Answer | |
| |---|---| |
| | "Isn't this rigged for the graph?" | Same PDFs, chunks, embeddings, LLM and prompt. Simple questions tie at 100%, and we show that. | |
| | "Would Neo4j do the same?" | Out of scope for this demo; a comparison write-up can follow. | |
| | "Our documents don't have IDs." | Uploads use LLM extraction; try it on the Upload tab. | |
| | "What does it cost?" | $0 here (free tiers). Tokens per correct answer are about the same as RAG. | |
| | "Is the data real?" | No. Cortex Bank is 100% synthetic. | |
| | "Licensing?" | FalkorDB is SSPL; check with Legal before production use. | |
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| ## Recording checklist (backup video) |
| - 1080p browser window, notifications off, PIN already unlocked, questions pre-cached. |
| - Recorded videos: `docs/video/cortex-exec.mp4` (executive highlights, ~2 min) and `docs/video/cortex-full.mp4` (full tour of every menu, ~3 min). |
| - Re-record with `python docs/record_demo.py --mode exec|full` after pre-caching the questions. |
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