Our Analysis Methodology: How Deep Analyst AI Actually Works

Let’s be honest - most “methodology” pages are marketing nonsense. Ours tells you exactly how our AI system works, step by step, because transparency beats buzzwords when your money’s on the line.

The Real Process: 5-Stage Multi-Agent Pipeline

Our analysis isn’t done by one “super AI” - it’s a coordinated effort by specialized AI agents, each doing what they do best. Think of it like a newsroom, but faster and with better fact-checking.

Stage 1: Content Planning (Query Generator + Web Research)

What Actually Happens:

1. Query Generator Agent creates strategic search queries targeting:

   • Breaking news (last 48 hours)
   • Market reactions (last week)
   • Social sentiment (current)
   • Industry context and controversies

2. Web Search API hits these source categories:

   • Financial News: Major financial news outlets and wire services
   • Market Data: Professional trading platforms and financial data providers
   • Social Sentiment: Investment forums, social trading platforms, and financial social media
   • Analysis: Independent research platforms and financial analysis sites

3. Content Planner Agent reviews the research and creates 4-6 article sections with specific angles

Example Query Strategy:

"[TICKER] news OR announcement after:[date]"
"[TICKER] analyst upgrade OR downgrade site:[financial-news-domain]"
"[TICKER] sentiment site:[investment-forum]"
"[TICKER] controversy OR dispute OR challenge"

Stage 2: Research Phase (Research Analyst Agents)

Parallel Processing: Each section gets its own Research Analyst Agent that:

• Generates targeted search queries for that specific section
• Gathers section-specific data and context
• Validates information from multiple sources
• Uses rate limiting (1 call per 2 seconds) to avoid overwhelming APIs

What They Actually Research:

• Core financial data (earnings, metrics, analyst targets)
• Social buzz and sentiment scores
• Contrarian angles and analyst disagreements
• Cultural impact and brand perception
• Comparative industry analysis

Stage 3: Content Writing (Section Writer Agents)

The Writing Process: Each section is written by specialized agents using our defined voice:

• Style: “Investment wisdom meets satirical commentary”
• Structure: 150-200 words per section
• Requirements: Bold company names ([Company] ([TICKER])), proper citations, mobile-friendly formatting

Citation Rules (Actually Enforced):

• Every fact must link to source: [Specific fact text](SourceName.com/subpage)
• Maximum 11 words for link anchor text
• Only URLs from research material - no invented links
• Facts become the clickable text, not separate citations

Stage 4: Fact-Checking Pipeline (Fact Extraction + Verification)

Automated Fact Checking:

1. Fact Extraction Agent identifies three types of verifiable claims:

   • Stock Prices: “[TICKER] closed at $X on [specific date]”
   • Financial Information: “[Company] revenue reached $X in [quarter/year]”
   • General Claims: “[Company] is expected to [specific action] in [timeframe]”

2. Verification Tools (Real APIs we actually use):

   • get_ticker_price: Third-party financial data API for historical stock prices
   • get_ticker_financials: Financial statement data from market data providers
   • get_search: Web search API for claim verification

3. Fact-Checking Agent validates each extracted fact and returns:

   • Truthiness: true/false/partially true
   • Explanation of verification
   • Corrected facts if needed
   • Source URLs for verification

Stage 5: Editorial Review + Final Assembly

Multi-Round Editing:

• Editor Agent reviews each section for accuracy, coherence, and style
• Checks for redundancy between sections
• Provides specific feedback for improvements
• Can trigger up to 3 revision cycles

Final Section Writer creates the complete article:

• Adds compelling title and introduction (100-150 words)
• Ensures logical flow with smooth transitions
• Writes memorable conclusion (150-200 words)
• Verifies all citations are functional

Our Data Sources (What We Actually Use)

Financial Data APIs

• Market Data Providers: Stock prices, historical data, company information
• Real-time Feeds: Live pricing and basic fundamentals
• Multiple Provider Fallbacks: If primary APIs fail

News and Research

• Web Search API: Our primary search engine for financial content
• Authorized Sources: 40+ verified financial news and analysis sites
• Social Sentiment: Investment forums, social trading platforms, financial social media (filtered)

What We DON’T Use

• Unverified blogs or rumor sites
• Paywalled-only content
• Pure technical analysis tools
• Cryptocurrency or forex data (currently)

Quality Control (Real Safeguards)

Automated Validation

• Source Verification: All facts traced to original sources
• URL Validation: Check that cited URLs actually exist
• Date Relevance: Ensure information is current
• Cross-Reference: Multiple sources for important claims

Rate Limiting and Error Handling

• API Limits: 1 call per 2-10 seconds depending on service
• Fallback Systems: Backup data sources for failed API calls
• Retry Logic: Automatic retry with exponential backoff
• Graceful Degradation: Continue operation even with partial failures

Editorial Standards

• Coherence Checks: Logical flow between sections
• Style Consistency: Maintain irreverent but informative tone
• Fact Density: Balance entertainment with substantial analysis
• Mobile Optimization: Readable on all devices

Our Actual Limitations (No BS)

What We’re Good At

• Current Market Events: Real-time news analysis and context
• Large-Cap Stocks: Companies with sufficient public data
• Pattern Recognition: Identifying market ironies and contradictions
• Readability: Making complex topics entertaining

What We’re Not Good At

• Future Predictions: We analyze current conditions, not crystal ball gazing
• Small-Cap Coverage: Limited data availability
• Day Trading: Our analysis is for understanding, not timing trades
• Personal Advice: We don’t know your financial situation

Known Biases

• Data Dependency: Limited by quality of source information
• English-Language Bias: Primary sources are English financial media
• Recency Bias: Focus on recent news may miss longer-term trends
• Large-Cap Bias: Better coverage of major companies

How to Actually Use Our Analysis

What Our Confidence Levels Mean

• High: Multiple sources confirm, clear data trends
• Medium: Good data but some conflicting signals
• Low: Limited data, significant uncertainty

Reading Our Citations

• Click through to verify our sources
• Check publication dates for relevance
• Cross-reference claims that seem surprising
• Remember: even good sources can be wrong

Integration with Your Process

1. Starting Point: Use our analysis to identify interesting angles
2. Additional Research: Dig deeper into areas that interest you
3. Professional Consultation: Discuss with qualified advisors
4. Personal Context: Consider your risk tolerance and timeline

Continuous Improvement

What We Track

• Prediction Accuracy: How our analysis holds up over time
• Source Reliability: Which outlets provide most accurate information
• Reader Engagement: Which analysis styles are most helpful
• Cost Efficiency: Balancing quality with AI usage costs

What We Update

• Model Selection: Switch between AI providers for optimal performance
• Source Lists: Add/remove news sources based on reliability
• Prompt Engineering: Refine instructions for better output
• Quality Gates: Improve validation and fact-checking processes

The Bottom Line

Our methodology isn’t revolutionary - it’s systematic. We use multiple AI agents, real financial APIs, and automated fact-checking to create analysis that’s both informative and readable.

We don’t predict the future, we don’t eliminate risk, and we’re definitely not your financial advisor. We just try to make sense of current market conditions with enough personality to keep you awake.

The real innovation isn’t in having super-smart AI - it’s in having AI agents that specialize in specific tasks and verify each other’s work. Think of it as artificial peer review, but faster and with more sarcasm.

Disclaimer: This methodology produces analysis, not guarantees. Markets are unpredictable, our AI can be wrong, and even perfect analysis can’t save you from your own poor timing. Invest responsibly.