Prima Investment Intelligence Platform
See the whole investment. Not just the familiar ratios.
A cross-asset research system that brings established financial models and Prima’s proprietary intelligence into one transparent, comparable decision framework.
For professional research and decision support. Not investment advice.
PRIMA / CROSS-ASSET ANALYSISMODEL STACK 01.6
AnalyseFund A + Listed Co. + Startup X3 assets
PRAR1.42Risk-adjusted return
PIQ78Investment quality / 100
PFR68%P(return > 0)
One analytical spine
Different assets. The same economic questions.
The system respects the mathematics of each asset class, then normalises the outputs that matter to an investment committee: return, loss, liquidity, time, uncertainty and portfolio contribution.
Ingest
Market data, returns, models, filings, decks, cap tables and investment theses.
Calculate
100+ conventional metrics through the correct asset-specific engine.
Normalise
Cross-asset comparison without pretending fundamentally different investments are identical.
Challenge
Prima metrics, probabilistic scenarios and deliberate thesis-breaking analysis.
Transparent by design
Industry-standard mathematics below. Prima intelligence above.
Every conclusion remains traceable to the underlying inputs, formulae and assumptions. Analysts can move from a clear signal to the calculation beneath it.
DCF · IRR · MOIC · Sharpe · Sortino · Omega · Calmar · Beta · VaR · CVaR · Monte Carlo
Valuation, performance, downside, factor exposure and scenario models selected for the asset under review.
PRAR
(Expected return − risk-free rate) / weighted downside × persistenceExtends beyond volatility to drawdown, tail loss, liquidity, concentration and model uncertainty.
PIQ 0–100
A calibrated composite of return, downside, tail risk, quality, valuation, liquidity, correlation, persistence and uncertainty.
PFR distribution
Bear, base and bull outcomes with explicit probabilities—not a falsely precise single target price.
Asset-specific engines
The model changes when the investment changes.
A startup without price history should not be forced through a listed-equity model. Each engine uses the available evidence, then maps its result into the shared decision framework.
Public companies
Fundamental · relative valuation · factor models · macro · filings and calls
Startups and private companies
Unit economics · burn · retention · exit scenarios · probability-weighted MOIC
Hedge funds and managers
Performance · drawdowns · factors · liquidity · persistence · strategy drift
VC, PE and portfolios
Cash flows · concentration · vintage · scenarios · look-through diversification
A model that must earn trust
Every forecast creates its own future test.
Proprietary outputs are designed to be timestamped and assessed against realised outcomes. Calibration, Brier scores, forecast error, hit rate and model decay make performance measurable—and weak signals replaceable.
- At forecast
- Inputs, model version and probability distribution locked
- At horizon
- Outcome measured against the original distribution
- Over time
- Weights recalibrated with out-of-sample evidence
Investment decisions, made legible
Move from fragmented analysis to a system of record for investment conviction.
The platform is an investment-research concept and decision-support environment. Proprietary metrics shown here are experimental and require ongoing validation. Nothing on this website is investment, legal, tax or accounting advice, an offer of securities, or a recommendation to transact.