Most decisions are made on spreadsheets that can't answer "what happens if the market shifts while we're executing?"
AI³ Observatory is a scenario and evidence machine — not a dashboard, not a forecast tool, not an oracle. It builds a living causal model of your business and the world around it, runs tens of thousands of simulated futures, and lets you compare any two scenarios side by side: same evidence, different decision.
ENTER MISSION CONTROLA quarterly model can't tell you how morale erosion compounds with pipeline drop, or when cash pressure will force a hiring freeze. Every assumption is silent. Every interaction invisible.
A business has physics. Revenue has inertia. People have morale. Cash has pressure. AI3 Observatory instruments these dynamics into three coupled models — and runs 10,000 scenario paths to show what could happen under these assumptions.
"We made possible futures visible, comparable, and auditable." — The core premise of the Observatory engine.
Departments (Ops, Sales, Product, Finance) · People (capacity, morale, burnout) · Cash, Revenue, Burn · Decision energy. Measured from internal systems. Higher confidence than world signals. Color: cyan.
Market conditions, trends · Regulation changes · Competitor moves · Macro shocks (interest rates, demand shifts) · Technology disruptions. Partially observed — amber tier evidence. Color: amber.
How world changes propagate into business state (shock transmission) · How business decisions respond to world signals. Coupling gain: 1.0× baseline → 1.6×⚑ demo at shock event. This is WHERE scenarios diverge. Color: violet.
From raw world signals and internal data to probabilistic scenario space — every step is instrumented, every confidence level encoded. Click any node to inspect its metrics.
World signals enter amber / dashed — visually distinct from measured internal evidence. The machine tracks them separately and flags when assumption load is high relative to confidence threshold 0.60.
Interactive 3D scene of the VPhysics Engine Core. Orbit, zoom, inspect — the machine is alive. Every node, every causal link, every state transition is rendered in real time.
— drag to orbit · scroll to zoom · click nodes to interact
This is not a static diagram. The Engine Core visualises BusinessPhysicsEngine.step(), AI Impact Pack, Monte Carlo paths, and Falsification gates — all as an explorable 3D topology. Drag to orbit, scroll to zoom, click to inspect.
Uncertainty is not a limitation of the Observatory — it is a first-class output. Every number the machine produces carries a data tier and a confidence score. When an assumption is wrong, you see it. When evidence is thin, the fan is wide. This is by design.
Line encoding rules: Thickness = impact strength. Opacity = confidence level. Dashed = assumption or world signal. Fan width = uncertainty at that percentile band. Wide fan = honest machine, not broken machine.
Inputs are not simply accepted or rejected. The gate marks, weights, or blocks each signal depending on its evidence tier and gate rules. High-confidence signals enter the core directly. Lower-confidence signals are downweighted, routed to the Assumption layer, or flagged — tracked and surfaced separately, never silently discarded. Currently: PASS (threshold 0.60, intake confidence 0.82⚑ demo).
Guards engine readiness for scenario deployment. Calibration error must be below 0.10. Currently at 0.14⚑ demo — the Replay Circuit is iterating to reduce it.
The machine doesn't hide its uncertainty — it renders it. Every line width, every opacity level, every dashed segment carries epistemic weight. You always know which numbers to trust, and which ones to question.
В master-документе — первичные данные: KeyBanc (N>500), Benchmarkit (N>1000 SaaS), SaaS Capital (N>1000), MIT Sloan (N=34M профилей), KPMG (N>3000 сделок). Это уровень, который PitchBook и CB Insights продают как premium benchmarks — просто без их брендированной обёртки. Ниже встроен оригинальный Antigravity semantic graph: клик по узлу центрирует граф и открывает правую карточку с источниками, диапазонами, confidence и связями.
At period 14⚑ demo, an external shock enters through the Coupler. The machine fans into two scenario trees: baseline drift vs. a decision lever. Same world signals. Same evidence. Different decision. The machine shows what changes — and what your assumptions had to be for that change to occur.
The Coupler routes external shocks into the Business Twin. At a shock event, coupling gain amplifies from 1.0× baseline to 1.6×⚑ demo, widening the scenario uncertainty fan. Same world signals — different coupling state = different possible futures.
A scenario and evidence machine. Not an oracle — a structured way to reason under uncertainty. Three zoom levels surface simulation outputs at every altitude — from board-level runway to engine-level calibration error.
Request access to the AI3 Observatory demo. Bring your data — we'll build the twin in a live session and replace every demo value with your real ai3.run_bundle.v1 artifact.