Ask in plain English — or let DcisionAI read your data and surface what’s worth optimizing. Either way: the single best action, what it’s worth in dollars, and a certificate that proves it.
Not a recommendation. Not a confidence score. A proof.
Analytics is a ladder. Most tools stop one or two rungs short — they tell you what happened, maybe what's next, and leave the hardest part to you. DcisionAI climbs the last rung: the decision itself, solved to optimality.
Upload a messy spreadsheet and it reads the columns cold — profiles every field, flags the anomalies, tells the story. No schema, no setup.
DescriptiveWhere there's history, it forecasts forward — with an honest band that widens with distance. Where there isn't, it maps the drivers instead of faking a trend.
PredictiveThe rung nobody else climbs. It formulates the decision as real math, solves it, and returns the best possible action — with a certificate proving it's optimal.
Prescriptive · the moatEveryone sells rungs one and two. The value — and the defensibility — lives at the top.
They recur wherever capital, people, and time are scarce — deploying capital, allocating resources, scheduling against the clock. High-constraint, audit-heavy, and exactly where today's tools run out.
Put a finite pool to work under hard limits.
Match resources to competing demands.
Cover demand and break no rules.
Four steps. No modeling team, no solver license, no six-week engagement.
Drop in a CSV or connect a source. It profiles the shape and finds the decision worth making.
“Rebalance these accounts.” “Staff these people to these jobs.” No math notation required.
The decision is compiled to exact math and solved to optimality. The answer, and the proof it's the best one.
Every decision writes a trace to your Context Graph: searchable precedent, replayable reasoning, audit-ready for compliance.
Other tools let a language model invent the equations and hope. We don't. Each decision is composed from a library of frozen, proven optimization primitives — the AI chooses which to compose; it never authors the math itself.
Every result carries a proven optimality gap. “Certified optimal” means mathematically no better answer exists.
Same data in, same certified answer out — down to the fingerprint. The math path has no LLM in it.
When a goal is impossible, it isolates the exact conflicting constraints and the smallest change that restores feasibility.
The model was never the moat — models commoditize. What compounds is the reasoning underneath. Every certified decision writes a decision trace: the constraints it weighed, which ones bound, the rules and exceptions it applied, the precedents it drew on, and the proof of why. Those traces stitch together into your Context Graph — a living, searchable record of how your firm actually decides, that only your firm can build.
A warehouse sees your data after the fact; a dashboard records the outcome. DcisionAI sits in the decision itself — so the reasoning becomes a first-class record, not tribal knowledge lost in a thread.
Every decision adds a trace and precedent becomes searchable — the tenth decision is faster than the first. A rival can copy the software, not years of your certified decisions.
Role-scoped and replayable: how a decision was reached, which constraint bound, what precedent it set — grounded in the graph, not a model guessing.
The last generation of software owned the data layer; this is the layer above it. And because every node is certified optimal, your Context Graph isn't a log of opinions; it's a record of provably-best decisions. The audit trail is just its compliance readout.
Upload a real dataset and watch a decision get made — certified, explained, and reproducible — in a single session.