Rank S&P 500 companies by quality, value, growth, and risk. Build sector-aware DCF scenarios, compare intrinsic value, and let deterministic AI agents challenge your thesis before you commit.
Most research tools help you collect evidence for the thesis you already like. Divergia keeps the math deterministic, then forces the thesis through disagreement: valuation assumptions, factor rank, sector model, risk context, and agent critique.
The goal is not a louder buy signal. It is a clearer map of where your thesis can break.
A structured loop for moving from candidate discovery to valuation, critique, and portfolio context without hiding the assumptions.
We don’t use fixed thresholds. The full S&P 500 screening universe (~300+ eligible names post sector filter) is evaluated to return the top snapshot across Quality, Value, Growth, and Risk factors.
The system detects business nature. It applies Gordon Growth for financials, FFO for REITs, and dynamic WACC for the rest. Immediate results in 3 sensitivities (Bull, Base, Bear).
The AI here is not a chat. They are personas (Risk, Macro, Devil's Advocate, Quant) deterministically analyzing the ticker and forcing a solid contradictory thesis for your review.
The Portfolio Engine consolidates the top assets by score and adjusts portfolio weights using inverse volatility optimization against the current market regime.
AI Committee Review · TSLA
Valuation Scenarios · MSFT
The product separates deterministic calculations from AI critique, so the story never outruns the evidence.
Ranked Evidence
Compare companies mathematically instead of using static filters like P/E. See where a business sits across quality, value, growth, and risk factors.
Sector Modeling
Run interactive valuation models with institutional rigor. Adjust parameters to test Bull, Base, and Bear cases with dynamic WACC and automated cash flow margins.
Adversarial Review
Get a structured second opinion from autonomous AI agents. They stress-test your thesis against macro trends, balance sheet risks, and news without reinforcing your biases.
Divergia is not a generic bot fed with superficial prompts. The math is computed first; AI critique comes after.
Critical answers before you trust your time to the engine.
No. Divergia is SaaS (Software as a Service) analytical infrastructure. The software does not recommend a 'Buy' to an individual without knowing their risk aversion. It provides the same structured research you would see on an institutional desk so you can make the final decision.
Every historical data point is locked out for 90 days after the corporate accounting period closes. Under the official 2026-08-10 re-pin (production-parity base plus a determinism contract: strict freeze, lake-pinned regime inputs, and a historized risk-free rate), DE_ALPHA records +6.70pp daily-reconstructed, annualized point-in-time alpha versus SPY. That number is published with provenance: it originates in a measurement-base change built on coherence repairs, not a new engine-improvement claim.
The engine is fed by primary SEC EDGAR 10-K and 10-Q filings, normalized fundamental data via SimFin, pricing via market APIs, and macro environment via the Federal Reserve (FRED).
We have strictly separated our Deterministic Domain Layer from AI. All math, DCF, margins, and scores are purely quantitative. The AI is fed these static numbers and restricts its output to qualitative critique. The AI audits the math, it never generates it.
Yes — analyze individual tickers free, no card required. Pro starts at $29/mo for the full S&P 500 screener, Portfolio Engine, and 2019–2025 T-90 backtests. Cancel anytime.
Run the math, expose the assumptions, and see where opposing evidence can weaken your thesis before the market does it for you.
Start Free AnalysisNo card required. Free to analyze.