The future of a system depends on the path it took.
We build working models of real systems — markets, consumer demand, industrial operations — and simulate where they can go next. Forecasts arrive with their uncertainty attached, validated against held-out reality before anyone is asked to trust them.
Start a conversation →Why we exist
Language models don't do math.
The current wave of AI is superb at narration and weak at numbers. When the question is quantitative — what will demand do next quarter, how does a shock propagate through an operation, which scenario deserves capital — the answer requires real machinery: feature engineering, system identification, neural forecasting, stochastic simulation.
That machinery is what we build.
What we build
Models that earn their keep.
Forecasting engines
Neural time-series forecasting at production scale. Honest baselines, walk-forward validation, and uncertainty cones instead of point promises.
Simulation & digital twins
System-dynamics and agent-based models of operations, markets, and consumers. Ask what-if and watch the distribution of outcomes, not a single guess.
Decision interfaces
Scientific visualization that makes a model legible to the people who bet on it. Executive dashboards without the decoration.
Method
Built one-of-one.
Every engagement is a model designed from scratch for one client and one system — never a re-skinned product — and it ships only after it beats honest baselines on data it has never seen. The person who scopes the model is the person who builds it.
Work
A production track record.
The practice behind the firm has shipped forecasting and simulation that production operations depended on. Demonstrations run only on data you can audit — open sources or fully disclosed synthetic systems — with pipelines, baselines, and metrics in the open.
Contact
Start a conversation.
Write to contact@pathdependenceresearch.ai, or see what a useful first note includes.