Most enterprises drown in dashboards and starve for decisions. Decision Engineering is the discipline of structuring hard choices — identifying the decision, framing scenarios, quantifying tradeoffs, and choosing a policy that can be explained and revisited. It draws on decision theory, operations research, simulation, reinforcement learning, and behavioral science. Decision Stack turns the discipline into a stack — a team of agents that propose options, run the math, and show their work. Between Knowledge Stack (what is true) and the action taken, Decision Stack is where judgment happens.
Decision Stack mirrors Knowledge Stack and Analytics Stack's four-layer architecture.
Each capability is delivered as an agent that would otherwise require a strategist, operations researcher, or behavioral scientist.
Enumerates plausible futures — base, upside, downside, stress. Writes each scenario in plain language with its assumptions and probability.
Runs discrete-event or Monte Carlo simulations of each scenario. Reports distributions of outcomes with confidence bounds, not point estimates.
Solves LP, MIP, and convex problems. Finds the allocation, schedule, or portfolio that maximizes objective subject to your constraints.
Trains reinforcement-learning policies (PPO, SAC, behavioral cloning) inside simulated environments. Outputs a deployable decision rule.
Builds the Pareto frontier across competing objectives — cost vs speed, risk vs return, quality vs throughput. Makes the tradeoff visible.
Tornado diagrams and one-at-a-time analysis. Shows which assumptions matter — and which you can stop arguing about.
Discrete-event simulation with event graphs, priority-queue scheduler, and variance reduction. Python-native, reproducible, fast.
Versioned registry of every assumption — expert-elicited, data-calibrated, or scenario-stressed. Every decision traces back to a specific assumption set.
Applies Wendel's CREATE and DECIDE frameworks. When the decision is how humans behave, this is where ethics and design live.
Deterministic math runs local. Claude enhancement is an opt-in layer for open-ended strategy work — scenario writing, rationale, narrative framing.
Runs fully offline. Assumptions stay local.
Opt-in. Metadata + assumptions sent — raw decision data stays local.
Start local. Scale with Claude. Switch anytime.
Every engagement starts with consulting — we frame the decision, elicit assumptions, and design the right stack. Then you choose how to run it.
We host, operate, and optimize it for you.
Your cloud or on-premise. Full sovereignty over assumptions and decisions.
