AI Finance Advisor: From Market Noise to Portfolio Insights

Click to Play 3:00 Demonstration (with Voiceover)

Scene 1 • 0:00 – 0:25 (25s) Act I: The Problem

The Portfolio Decision Dilemma

Data everywhere, but what does it mean for your holdings?

Fragmented Market Noise

• BREAKING: Fed pauses rate hikes; tech valuations reassessed

• Q3 Earnings: IT margins compress under wage headwinds

• Retail Portfolios: Over-concentration skew undetected

The Real Investor Question

"What does this news actually mean for my specific portfolio, what risks am I exposed to, and what trade-offs should I consider next?"

✓ Goal: Contextual Portfolio Grounding

AI Finance Advisor Demonstration Target Runtime: 3:00

Scene 2 • 0:25 – 0:55 (30s) Act II: What It Does

Unifying Holdings, Risk & Intelligence

Moving beyond generic stock chatbots to true portfolio-aware reasoning.

Portfolio Ingestion

Relational SQLite holding memory loaded by Customer ID.

Concentration Alerts

Deterministic >25% single-stock risk detection in Java code.

Deterministic Math

Exact PnL, cost basis and SMA calculations offloaded from LLM.

Search Grounding

Live web retrieval for quarterly filings and earnings surprises.

Scenario Modeling

3-case forward projections (Baseline, Upside, Stress).

Guardrail Shield

Blocks transactional orders (execute_trade); non-advice safety.

Core Principle: LLM Reasons & Synthesizes • Java Code Computes & Enforces

Scene 3 • 0:55 – 1:50 (55s) Act III: Live Demonstration

Live Execution: Customer 1002 (Infosys Analysis)

Multi-turn session state, concentration alert, and 3-case scenario stress test.

> user: "Review Customer 1002 portfolio, identify key risks, and stress-test tech budget headwinds."

Holdings: 10x INFY @ 1,500 INR (15,000 INR Invested) ⚠️ RISK ALERT: 100% Single-Stock Concentration (>25% limit)

🔹 Baseline Case
Target: 1,650.00 INR (+8.5%)
Assumptions: 6% constant-currency revenue growth; North America deal ramp.

🟢 Upside Catalyst
Target: 1,820.00 INR (+19.7%)
Assumptions: Generative AI enterprise deal acceleration.

🔴 Downside Stress
Target: 1,380.00 INR (-9.2%)
Assumptions: Enterprise discretionary IT freeze, tier-1 wage inflation.

DCF Intrinsic Fair Value: 1,585.20 INR (Two-Stage Model) Evidence: SEC Filing & Q3 Earnings Conference Call

Supporting Evidence Cited • Zero Arithmetic Hallucination • Human Oversight Required

Scene 4 • 1:50 – 2:30 (40s) Act IV: What Makes It Different

Engineering Rigor: Why It's Built in Java

Specialist multi-agent division of labor with Google ADK lifecycle callbacks.

Google ADK Java

Native Core

Built on official ADK 1.4.0 with Gemini 2.5 Flash and session lifecycle management.

Java Math

Deterministic Tool

Zero LLM math drift. PnL, allocation weights, and DCF terminal value computed in Java.

5 Callbacks

Safety Perimeter

BeforeAgent (injection guard), BeforeModel (PII scrub), and BeforeTool (trade blocker).

Structured JSON

Contract Validation

FinanceAnalysisResponse GenAI schema contract with self-healing repair engine.

Observability: Traced with Execution IDs • Cloud Run Container Port 8089 • Firestore Sync

Scene 5 • 2:30 – 3:00 (30s) Act V: Who It Helps & Close

Transforming Financial Decision Support

Empowering individual investors, RIA practices, and wealth enterprises.

Individual Investors

Turn fragmented holdings into a coherent, stress-tested view of portfolio health with grounded news.

Advisers & Wealth Teams

Automate quarterly review packets across hundreds of accounts with audit-ready citations and disclaimers.

Fintech Platforms

Embed autonomous MCP tools and REST/SSE gateways into existing wealth management applications.

Educational decision-support platform. Not investment advice. No automatic trading.

Narrator: “Imagine you have an investment portfolio. Markets are moving, new financial news is coming out...”

00:00 / 03:00

Voiceover Narration (Web Speech)

Video Script Storyboard

5 Scenes • 3:00

1. The Problem 0:00 – 0:25 (25s)

“Imagine you have an investment portfolio. Markets are moving, new financial news is coming out, and some of your holdings are becoming a bigger part of your portfolio...”

2. What It Does 0:25 – 0:55 (30s)

“The idea is to bring portfolio information, risk analysis and market intelligence together in one experience. Instead of simply asking a chatbot about a stock...”

3. The Live Demonstration 0:55 – 1:50 (55s)

“Let me demonstrate a typical scenario. I ask the Finance Advisor to review a portfolio and identify the key risks, recent market developments, and holdings...”

4. What Makes It Different 1:50 – 2:30 (40s)

“The important part is the engineering behind the experience. The Finance Advisor is designed around specialist analysis, financial data tools, deterministic calculations...”

5. Who It Helps & The Close 2:30 – 3:00 (30s)

“For individual investors, this can help turn fragmented information into a more coherent view. For wealth-management teams, it supports portfolio reviews...”