Most enterprises drown in dashboards and starve for decisions. Decision Modeling is the tool that brings structure to the choices in front of you: identifying the decision, framing scenarios, quantifying tradeoffs, and choosing a policy you can explain and revisit. It draws on deep foundations in decision theory, operations research, simulation, reinforcement learning, and behavioral science.
Infozense Decision Modeling works as a team of AI agents that propose options, run the math, and show their work. Between knowing what's true and taking action, Decision Modeling is where judgment happens. And it doesn't replace the solvers, simulators, and models you already trust. Instead, it's the substrate that connects them, so multiple vendors and techniques work together inside one decision you can audit end to end.
A decision isn't made by thinking it through once and being done; it loops until the answer is clear: framing the problem, generating options, simulating and computing the results, seeing what the decision hinges on, then committing or looping back to start again. And Decision Modeling isn't just another modeling tool; it connects the optimizers and simulators you already trust, so many vendors and techniques work together inside one auditable decision. Nothing solves until a human ratifies the frame.
Infozense owns the decision context — the frame, the assumptions, the rationale, the audit trail. The math is borrowed from solvers you choose; the discipline, the gate, and the committee-ready record are ours.
Not every decision uses every agent — they're a toolbox, not a sequence. The Supervisor reads the shape of the problem (a search, an evaluation under uncertainty, a tradeoff) and runs only the agents that fit, then loops until the answer is robust.
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.
Where the agents reach external math, where every assumption is versioned, and where human-behavior decisions are shaped.
Typed connector protocols to external optimizers and simulators — open-source by default, or connect a licensed commercial solver. Every connector reports solver status, optimality gap, and confidence bounds.
A versioned registry of every assumption, whether elicited from experts, calibrated against real data, or stress-tested under crisis scenarios. So every decision traces back to the exact assumption set it used.
Applies Wendel's CREATE and DECIDE frameworks. When the decision is how humans behave, this is where ethics and design live.
Speed without rigor is how you optimize the wrong problem perfectly. Before Decision Modeling solves anything, a human ratifies the frame — and every recommendation it returns carries the evidence to defend it.
A wrong frame can't be solved away — a clean, optimal answer to the wrong question is the most dangerous output there is. So nothing gets solved until a human ratifies the frame: the decision, the alternatives on the table, the values at stake, and what would make the recommendation invalid. The model proposes; it never certifies its own frame.
Every result reports its solver status and optimality gap — no 'optimal' label on a solution that timed out. Each recommendation ships its sensitivity: which assumptions it hinges on, and how wrong they'd have to be to change your mind. Seeded and reproducible, every run.
Every decision traces to a versioned set of assumptions — each tagged with source, owner, confidence, and whether it was measured or merely assumed. The output is a decision record: the action, the rationale, the tradeoff, and the named person accountable. Decision Modeling supports the decision; it never takes it.
It runs where your assumptions and decisions already live. The LLM is your call — local by default, frontier when it matters, your API key and your bill.
Your cloud or on-premise — AWS, GCP, Azure, IBM Cloud, INET, or bare metal. Full sovereignty over your assumptions and decisions, and we train your strategists and analysts to run it.
We host, operate, and optimize it for you — versioned assumptions, a full audit trail, and your first scenarios in days, not quarters.
The substrate is the easy part; a defensible decision is people. Infozense engineers — the ones who build and operate it, not slide-deck consultants — work alongside your team for the first 8–16 weeks: frame the first decisions, wire in your solvers, elicit the assumptions, train your strategists. Knowledge-transfer first: your team owns the decisions at the end.
