80+ AI Financial Analysis Tools, One Platform
Multi-agent AI automates every step of investment research from financials, consensus, news, technical analysis, to screening.
Alpha Chat (AI Agent)
AI financial analysis chatbot powered by multi-agent Orchestrator-Worker architecture. Six specialized workers identify query intent and automatically collect and analyze financial statements, consensus, news, technical analysis, screening, and price/flow data. Multi-turn conversation memory preserves context, with automatic chart and table visualization.
- ✓6 specialized workers with auto-routing
- ✓Multi-turn conversation memory
- ✓Auto chart/table visualization
- ✓RAG + structured data hybrid
Included
Chart & Technical Analysis
AI automatically draws support/resistance lines, trendlines, and patterns on TradingView-based high-performance multi-charts. Professional-grade pattern analysis including VCP, CANSLIM, and divergence. Chat with AI in real-time while viewing charts through Chart Chat.
- ✓AI auto support/resistance & trendlines
- ✓VCP, CANSLIM, divergence patterns
- ✓Real-time AI chat on charts
- ✓Multi-timeframe analysis
Included
Screener & Stock Selection
Screen stocks with custom criteria like ROE, PER, revenue growth, and instantly apply proven preset strategies such as VCP, CANSLIM, and PEAD. Bull/Base/Bear scenario analysis provides comprehensive target price and risk evaluation.
- ✓Custom screening criteria
- ✓VCP, CANSLIM, PEAD presets
- ✓Bull/Base/Bear scenario analysis
- ✓Comprehensive valuation
Included
Market Intelligence
Start your day with the daily dashboard and AI Morning Brief. Assess current market regime and risk with Market Health indicators. Monitor every market signal from FOMC, CPI, NFP economic calendars to institutional flow, theme tracker, and bubble detector.
- ✓AI Morning Brief
- ✓Market Health (regime & risk)
- ✓Economic/Earnings calendar
- ✓Institutional flow, themes, bubble detector
Included
Research & Data
AI automatically searches and analyzes SEC/DART filings. Compare target prices and investment opinions by brokerage at a glance with vector DB-powered consensus data. Quickly grasp key information with real-time news aggregation and AI summaries.
- ✓SEC/DART filing AI analysis
- ✓Vector DB-powered consensus
- ✓Real-time news AI summary
- ✓Brokerage target price comparison
Included
Quantitative Tools
Design quant models in Model Lab and validate with backtesting. Calculate optimal position sizing and explore pair trade opportunities. Diagnose overall market health with sector rotation and breadth analysis.
- ✓Quant model design & backtest
- ✓Position sizer
- ✓Pair trade
- ✓Sector/Breadth analysis
Included
Why AlphaLenz
Multi-Agent Architecture
Six specialized workers auto-distribute by query intent for parallel processing. Superior accuracy and speed versus general-purpose LLMs.
Institutional-Grade Data Pipeline
Automated parsing of primary filings, multi-stage cross-validation, and real-time consensus updates via vector DB.
All-in-One Investment Platform
Charts, screening, research, news, and quant models in one platform. Complete analysis without switching tools.
Fin-RATE Financial Analysis Benchmark
Finance Benchmark — Normalized Score (0-1) · LLM-as-Judge (GPT-based, 0-5)
* LLM-as-Judge methodology: GPT evaluates each response on accuracy, freshness, precision, and cross-source validation on a 0-5 scale, normalized to 0-1. Same prompt · same data · same evaluation criteria applied.
Evaluation framework reference: "Fin-RATE: A Real-world Financial Analytics and Tracking Evaluation Benchmark for LLMs on SEC Filings" (Jiang et al., 2026) — Joint research by Yale University & Goldman Sachs. A financial LLM benchmark based on SEC filings, evaluating three analytical pathways: single-document reasoning, cross-entity comparison, and longitudinal tracking. arXiv:2602.07294
Agent Architecture
Unlike general-purpose LLMs, Treasurer A7Z operates as a multi-agent pipeline specialized for financial analysis.
- •Auto-routing to specialized workers by query intent (financials, consensus, news, technical analysis)
- •Orchestrator-Worker pattern handles complex queries step-by-step
- •RAG + structured data hybrid — minimizes hallucination
Data Quality
Sourced directly from primary filings and validated through a multi-stage pipeline to ensure institutional-grade accuracy.
- •Primary filings → automated parsing → cross-validation pipeline
- •Brokerage reports in vector DB (Qdrant) — real-time consensus updates
- •Daily automated collection and normalization of price, flow, and macro data
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