SEE OUR WORK
P_success
P_success replaces gut feel with a quantified, auditable probability-of-success.
WHAT IS P_SUCCESS?
P_success is a decision-grade, probability-based algorithm that sits atop LLM’s and rolls many drivers of success into one number.
Because it’s a composite indicator (inputs are normalised, weighted, and combined), companies and decision makers can tweak assumptions and immediately see how different choices move the likelihood of success, perfect for scenario testing & portfolio triage.
-
Simulate various business or policy scenarios (e.g., new market entry, product launch, policy implementation, benchmark against competitors) to forecast potential outcomes.
-
Use our proprietary model to predict the probability of success for projects, investments, initiatives, business directions, industries and almost ANYTHING…
-
Develop tailored simulations that incorporate client-specific factors such as regulatory changes, market volatility, and competition dynamics.
P_SUCCESS
Use Cases To-Date
Use-cases marked with an asterisk (*) are confidential/on behalf of clients.
Startup: Medical - exercise oncology*
Gold Price dynamics*
Startup: Space - reusable debris remover*
Dairy - HPAI H5N1 risk strategy post US outbreak*
Startup: AI-powered retention engine for gyms*
Startup: Solar powered pool pumps*
Startup: 100% bio-based, BPA-free epoxy system*
Housing Funding Policy Scenarios
Election and political scenarios
Copper & Lithium mine stock investment
CCI Risk Alpha framework*
Mock Defence scenarios
Real-estate listing portals*
High dividend low debt ASX
Company investment / exit strategy*
WHAT WE DO
Solving Key Challenges
Faster, more consistent decisions
Standardised intake + automated scoring shrink cycle time (real-world agentic deployments cut resolution times dramatically)
Lower risk of costly mis-bets
Delve into InfraplanX.io's government policy offerings encompassing complex policy simulation models and real-time scenario analysis for better policy-making.
Evidence-backed transparency for boards/regulators
Every recommendation carries model/run IDs and aligns with how public agencies already appraise investments
Decisions grounded in real “what-if”s, not prose
Integrates simulation/digital-twin outputs where available so choices are tested against scenarios, not just text prediction.
Far fewer hallucinations in critical workflows
Gating outputs behind structured drivers, sources, and uncertainty directly addresses known LLM reliability issues.
We’d love to work with you.
Click below to get in touch with our team, or to request a demo.