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MVP Demo Video Script (3–5 minutes)

Record with QuickTime. Read callouts aloud. Each [MVP-X] tag maps to a requirement.

PART 1: Deployed App in Browser (~3 min)

Scene 1 — Show Deployed URL (0:00)

Open browser. Navigate to:

https://ghostfolio-production-f9fe.up.railway.app

Say: "This is Ghostfolio, deployed on Railway. It's publicly accessible."
Show the landing/login page loads.

[MVP-9] Deployed and publicly accessible — visible: real URL, live site.

Navigate to:

https://ghostfolio-production-f9fe.up.railway.app/demo

You'll be auto-logged in and redirected to the Overview page.
Say: "I'm logging in as a demo user. This account has a pre-seeded portfolio with 5 stock holdings — Apple, Microsoft, Vanguard Total Stock Market, Google, and Amazon."
Briefly show the Overview/Holdings page so the portfolio data is visible.

Scene 3 — AI Chat: Natural Language Query + Tool Call (0:45)

Click "AI Chat" in the top navigation bar.
Type: "What are my current holdings?"
Press Enter. Wait for response.
Say: "The agent understood my natural language question, called the get_portfolio_holdings tool, and returned my actual portfolio data."

[MVP-1] Agent responds to natural language queries — visible: typed question, got answer. [MVP-2] At least 3 functional tools — first tool shown: get_portfolio_holdings. [MVP-3] Tool calls execute and return structured results — visible: holdings with names, symbols, allocations. [MVP-4] Agent synthesizes tool results into coherent response — visible: formatted natural language summary, not raw JSON.

Scene 4 — Multi-Turn Conversation (1:30)

In the same chat, type: "How is my portfolio performing this year?"
Wait for response.
Say: "This is a second question in the same conversation. The agent maintains conversation history across turns — it knows we were already talking about my portfolio. This used the get_portfolio_performance tool."

[MVP-5] Conversation history maintained across turns — visible: second message in same chat, context preserved. [MVP-2] — second tool shown: get_portfolio_performance.

Scene 5 — Third Tool (2:00)

Type: "Show me my accounts"
Wait for response.
Say: "That's a third tool — get_account_summary — showing account names, balances, and platforms. We've now demonstrated at least three distinct tools."

[MVP-2] — third tool confirmed: get_account_summary.

Scene 6 — Error Handling (2:20)

Type: "Sell all my stocks right now"
Wait for response.
Say: "The agent gracefully refuses unsafe requests. It explains it's a read-only assistant and cannot execute trades. No crash, no error — just a polite refusal."

[MVP-6] Basic error handling / graceful failure — visible: no crash, friendly refusal message.

Scene 7 — Domain-Specific Verification (2:45)

Scroll to any previous response that contains dollar amounts or percentages.
Say: "Notice the disclaimer at the bottom of responses with financial figures: 'All figures shown are based on your actual portfolio data. This is informational only and not financial advice.' This is a domain-specific verification — the agent only cites numbers from real tool results and appends a financial disclaimer automatically."

[MVP-7] At least one domain-specific verification check — visible: financial disclaimer on data-bearing responses.

PART 2: Evaluation Suite in Terminal (~1 min)

Scene 8 — Run Eval (3:00)

Switch to terminal (or split screen).
Say: "Now I'll run the evaluation suite — 10 test cases that verify tool selection, response quality, safety, and non-hallucination."
Run:
```
AUTH_TOKEN="<your bearer token>" npx tsx apps/api/src/app/endpoints/ai/eval/eval.ts
```
(To get a token: curl -s https://ghostfolio-production-f9fe.up.railway.app/api/v1/info | python3 -c "import sys,json; print(json.load(sys.stdin)['demoAuthToken'])" )

Or if running against localhost:
```
AUTH_TOKEN="<token>" npx tsx apps/api/src/app/endpoints/ai/eval/eval.ts
```
Wait for all 10 tests to complete. The output shows each test with PASSED/FAILED, tools called, and individual checks.
Say: "All 10 test cases pass. The suite checks correct tool selection, non-empty responses, safety refusals, content validation, and non-hallucination."

[MVP-8] Simple evaluation: 5+ test cases with expected outcomes — visible: 10 tests, pass/fail status, 100% pass rate.

Wrap-Up (3:45)

Say: "To recap — this is a fully functional AI financial agent built on Ghostfolio. It responds to natural language, invokes 9 tools backed by real portfolio services, maintains multi-turn conversation, handles errors gracefully, includes financial verification checks, passes a 10-case evaluation suite, and is deployed publicly on Railway. Thanks for watching."

Quick Reference: All 9 MVP Requirements

#	Requirement	Demonstrated In
1	Natural language queries	Scene 3
2	3+ functional tools	Scenes 3, 4, 5
3	Tool calls return structured results	Scene 3
4	Coherent synthesized responses	Scene 3
5	Conversation history across turns	Scene 4
6	Graceful error handling	Scene 6
7	Domain-specific verification	Scene 7
8	5+ eval test cases	Scene 8
9	Deployed and accessible	Scene 1

Before Recording Checklist

Browser open, no other tabs visible
Terminal ready with eval command prepared
QuickTime set to record full screen
Deployed site is up (visit URL to confirm)
You know the demo URL: https://ghostfolio-production-f9fe.up.railway.app/demo

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