AutoSim · An AlphaIQ product

Diverse use cases, one engine.

AutoSim's versatility opens doors to impactful applications across numerous sectors, turning complex data into strategies you can defend.

Urban planning & smart cities

Model traffic flow, public transport, energy consumption and disaster response to build resilient, efficient urban environments.

Supply chain & logistics

Optimise inventory, predict disruptions, test new distribution strategies and improve resilience against market volatility.

Healthcare & epidemiology

Simulate disease spread, test intervention strategies, optimise hospital resource allocation and model patient-flow dynamics.

Environmental science & climate

Model ecological systems, the impact of climate policies, resource management and species interactions to inform conservation.

Economics & finance

Model market behaviour, assess financial risk, optimise portfolios, test trading algorithms and analyse policy impact across stakeholders.

Sports & games

Analyse player performance, optimise team strategy, simulate tournaments, test game mechanics for balance and model fan engagement.

What AutoSim simulates

Six families of model, one description away.

AutoSim is not a template gallery. It picks the modelling paradigm the system actually needs, writes the simulator, and calibrates it against your data.

Agent-based models

Heterogeneous agents with behavioural rules, interacting on a grid, a network or a market. Emergent outcomes rather than assumed ones.

Traders, households, drivers, patients, firms

Compartmental & system dynamics

Stocks, flows and feedback loops. Continuous or discrete time, fitted to observed trajectories rather than hand-tuned.

SIR epidemics, adoption curves, inventory loops

Discrete-event & queueing

Arrivals, servers, capacity and waiting. Utilisation and tail latency come out of the run, not out of an average.

Triage, warehouse picking, call centres, ports

Network diffusion & contagion

Spread over an explicit graph, with topology treated as a parameter you can stress rather than a fixed assumption.

Information cascades, default contagion, outages

Monte-Carlo risk ensembles

Thousands of stochastic paths from the calibrated model, returning full distributions, probability cones and tail measures.

VaR and ES, ruin probability, delay distributions

Policy & intervention search

Sweeps and optimisation on top of the simulator, so you compare interventions under the same uncertainty instead of one point forecast.

Pricing, staffing, lockdowns, rebalancing rules

Every family ships as readable Python, calibrated with ABC-SMC and scored out-of-sample — the paradigm changes, the evidence standard does not.

The inherent value of simulation

Why a simulation beats a spreadsheet.

Strategic foresight

Anticipate trends, identify challenges and explore what-if scenarios before committing resources.

Optimisation & efficiency

Identify bottlenecks, improve processes and allocate resources for better performance and lower cost.

Risk reduction

Test strategies and policies in a virtual environment to understand impact before real-world implementation.

Enhanced understanding

Gain deeper insight into the dynamics and interdependencies of systems that are otherwise hard to grasp.

Improved collaboration

Give diverse stakeholders a common visual language for discussing complex problems and solutions.

Illustrative scenarios

What the work looks like in three sectors.

Financial services

Risk-modelling revolution

Traditional Monte-Carlo simulations took six months or more to develop and validate.

An AI-generated, calibrated agent-based risk model produced and validated in two weeks.

90% reduction in development time
40% improvement in prediction accuracy
Consulting spend cut by an order of magnitude
Real-time scenario testing

GenAI code generation · 30-run ensembles · iterative refinement

Healthcare network

Pandemic response planning

Needed to model disease spread and hospital resource allocation in days, not quarters.

An epidemiological agent-based model with mobility patterns and intervention testing.

ICU capacity optimised by 35%
50+ vaccination distribution scenarios tested
24-hour model deployment
93% accuracy on case-peak timing

WebAssembly Python · AI data calibration · visual validation

Global manufacturing

Disruption resilience

Recurring supply-chain disruptions with no way to test mitigation before committing to it.

A multi-agent supply-chain simulation stress-tested against disruption scenarios.

23 critical vulnerability points identified
Recovery time reduced by 60%
Optimisation savings across the network
Proactive risk-mitigation strategies

AI-suggested improvements · ensemble metrics · iteration history

Illustrative scenarios written to show the shape of the work. They do not describe named clients, and the figures are indicative rather than audited results.

Nine simulations already built on AutoSim — open and run any of them

Core technology

Next step

Bring a system you need to defend.

Describe it in plain language and AutoSim will build, calibrate and score a simulation of it while you watch. If the question needs more than the product alone, AlphaIQ will build it with you.

Talk to AlphaIQ