We build Context — how the firm actually runs. We surface Options worth taking, then Resolve them: the best answer under your constraints — or a clear report that none exists. Every decision we Encode back into memory. That loop is CORE.
Not a recommendation. Not a guess. A decision you can defend.
Build how the firm actually runs. Surface the decisions worth taking. Resolve them to optimality within your constraints — or show exactly why none exists. Encode every answer back so the next one starts smarter. No modeling team, no solver license, no six-week engagement.
Instantiate a decision-ontology from your systems of record. Every rule, precedent, and prior decision compounds in over time.
Read that context forward — into demand, rates, and risk — and surface the decisions actually worth taking.
Return the optimal action under your real constraints, with binding rules named in business language — or show exactly why none exists.
Every resolved decision becomes a node in the graph. Searchable precedent, replayable reasoning, audit-ready for compliance.
Encode → Context Each pass starts from a richer position than the last. That is how the decision layer compounds.
Wherever the work already happens.
For the analyst who wants to sit inside the problem, verify the formulation, and walk the audit trail.
The decision layer as a native capability inside the AI assistant the enterprise already runs — deterministic, structured outputs.
An autonomous agent calls for an optimal decision the same way it calls any other service — and gets an answer with the optimality gap shown, not a guess.
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.
A wealth manager rebalancing a household against an IPS mandate and wash-sale rules. A private credit fund structuring a waterfall across LPs.
A staffing firm matching a bench to open requisitions across skills and geographies. A hospital covering demand under specialization and capacity limits.
Eighty nurses across three shifts under union and rest rules. A fleet across two hundred stops with time windows and capacity limits.
Each decision stands on its own math. Together they build a record no competitor can copy: how your firm actually decides.
Other tools let a language model invent the equations and hope. We don't. Each decision is composed from a library of frozen, established optimization primitives, the AI chooses which to compose; it never authors the math itself.
Every result carries an optimality gap. “Optimal within constraints” means no better feasible answer exists for the model you approved.
Same data in, same optimal 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. Your decision memory maps how your firm actually works: roles, dependencies, obligations, and the precedents you've set. And every decision reasons from it before it solves. Each decision then writes a decision trace: the constraints it weighed, which ones bound, the rules and exceptions it applied, the precedents it drew on, and the reasoning for why. Those traces stitch together into your decision memory, a living, searchable record of how your firm actually decides, that only your firm can build.
The solver doesn't start from a blank sheet. It reads your context first: who's accountable, what's upstream, which rule is binding, so the answer fits how your firm actually operates, not a generic template.
Every decision adds context and precedent the next one reuses; the tenth decision starts from the first nine. A rival can copy the software, not years of how your firm decides.
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, where decisions are made, not just recorded. Because every node is optimal within its constraints, your decision memory isn't a log of opinions; it's a record of decisions that were optimal within their constraints — ones your firm can reason from. The audit trail is just its compliance readout. Fiduciary and governance pressure — SEC Regulation Best Interest, the EU AI Act among them — is raising the bar for traceability that decisions were made under real constraints with reasoning preserved.
Upload a real dataset and watch a decision get made, explained, and reproduced — optimal within your constraints, in a single session.