Foresight projects counterfactual scenarios and simulates future decision paths against your existing rules. Instead of waiting for outcomes to reveal whether your rules are adequate, Foresight lets you ask "what would happen if?" — and get deterministic, traceable answers before anything executes in production.
This pattern applies when
The Problem
New rules go live and you discover their effects on real decisions with real consequences. There's no way to simulate "what would this rule have done across last month's traffic?" before activation.
Strategic teams build scenarios in slide decks and spreadsheets. But these scenarios don't connect to the actual decision logic that will execute when conditions change. The gap between "our plan says X" and "our system does Y" is invisible until it's too late.
"If we had changed this policy three months ago, what would have happened?" is a question no one can answer — because decisions aren't stored in a format that allows replay under modified conditions.
Risk teams describe scenarios in words. But the actual system behavior under those scenarios is unknown. You know what you want to happen. You don't know what would happen.
Predictive Analysis
Foresight uses Memrail's deterministic decision model as a simulation engine. Because every rule is a pure function of documented inputs, and because decision replay is exact, Foresight can project what would happen under conditions that haven't occurred yet — and give you traceable, reproducible answers.
This isn't probabilistic forecasting. When Foresight replays historical decisions against modified rule sets, the outputs are deterministic. When it feeds hypothetical contexts through your governance model, it observes exactly which rules fire, which gaps appear, and what actions execute. The simulation runs on the same engine that will govern the real decisions.
"If we change rule X, how would the last 5,000 decisions have been different?" Re-run historical decision contexts against modified rule sets and compare outcomes deterministically.
"If market conditions shift to this scenario, which rules fire, which gaps appear, and what actions execute?" Feed hypothetical contexts through the model and observe behavior before it matters.
"What's the worst-case decision path under extreme conditions?" Identify rule combinations that produce undesirable outcomes before they happen in production.
"Based on trajectory analysis, this condition is likely within 30 days. Here are the adjustments to prepare." Combine observed patterns with forward projection to act before problems materialize.
Example: Deal scenario simulation
A deal intelligence team at a private equity firm uses Memrail to govern their sourcing and evaluation agents. Foresight lets them simulate deal scenarios before committing capital: "If interest rates rise 150bps and the target's revenue declines 20%, how do our evaluation rules respond? Which deals in our current pipeline would be flagged, downgraded, or killed?" Instead of running these scenarios manually across spreadsheets, Foresight computes the answers deterministically — using the same rules that will actually execute when conditions change.
The same pattern applies to legal teams simulating litigation outcomes under different judicial assignment scenarios, compliance teams stress-testing regulatory change impact, and operations teams projecting the effect of policy modifications on autonomous behavior at scale.
Foresight pairs with Hindsight — simulations are most powerful when grounded in observed decision patterns. What Hindsight learns from history, Foresight projects forward.
See HindsightHindsight + Foresight together
The most powerful application combines both. Hindsight analyzes historical decision patterns to identify what has happened and why. Foresight projects those patterns forward to predict what will happen next — and recommends changes to intervene before negative outcomes materialize.
Example: Hindsight identifies that a judge has shifted ruling patterns over the last 18 months on a specific procedural issue. Foresight simulates how this shift affects the outcome probability of pending cases — informing whether to settle, litigate, or modify strategy now rather than after an unfavorable ruling.
Platform
Deterministic replay is the foundation — same inputs plus same rules equals same output. This property makes simulation possible and results trustworthy.
Learn moreShadow mode lets Foresight-generated rules run against real traffic without executing. Validate projected interventions before they go live.
Learn moreVersion pinning enables counterfactual comparison across rule versions. See exactly how decisions would differ under different policy configurations.
Learn moreEnsures simulated rules can actually fire if promoted to production. No gap between what Foresight projects and what the system can execute.
Learn moreIndustries
Foresight applies wherever the cost of a wrong decision exceeds the cost of simulating it first. In high-stakes, low-reversibility domains, the ability to test decisions before they execute is not a luxury — it's risk management.