What we do.
From → To
From a periodic forecast to a continuous, learning, accountable decision system.
We build the decision intelligence layer that sits above your planning and Enterprise Resource Planning (ERP) stack — augmenting it, not replacing it. One coherent policy. One treatment of uncertainty. One objective function.
From periodic to continuous, from deterministic to probabilistic.
Planning systems were designed for a world where demand was predictable, supply was reliable, and decisions could wait for the next cycle. That world is gone. Demand is volatile, supply networks are stressed, and the cost of waiting a week to replan is measured in lost revenue, expedited freight, and customer relationships that quietly erode.
Decision intelligence is the category that replaces periodic planning with continuous, probabilistic, accountable decision-making. The engine sees variability as the signal, not the noise. Plans ship as distributions, not point estimates. Decisions emit continuously as the network state changes, not on monthly cycles. Every decision carries its reasoning. Policy controls what gets auto-committed and what routes to a human. The system learns from realized outcomes and gets smarter quarter over quarter.
VYAN is the platform that operationalizes this. We are not adding AI features to a traditional planning system; we built the platform from the ground up around decision intelligence primitives.
The architecture is the moat. Replicating it requires rebuilding from the database up.
Three operating layers, plus SAGE in the loop.
At the center of VYAN is a decision engine that sees the world as it actually is, decides under one math object, and responds continuously. The engine is composed of three operating layers, with SAGE in the loop across all of them:
- 01
Seeing — the probabilistic substrate calibrates variability from historical data — supplier lead times, demand bias, yield, capacity, customer pull patterns — and consumes those distributions directly in the planning math. Order lines, not buckets. Signals, not surveys.
- 02
Deciding — the one-math-object engine maximizes Resilient-EVA — Economic Value Add adjusted for tail risk — across a balanced scorecard of revenue, margin, working capital, and service, in one solve. The Decision Policy is a first-class governed artifact, not a buried setting.
- 03
Responding & learning — the continuous layer emits decisions continuously as the network state changes, sizes the minimum-impact replan for each disruption, respects policy-driven autonomy thresholds, and learns from realized outcomes so the engine compounds value quarter over quarter.
- 04
SAGE — in the loop across all three the conversational AI colleague that translates between user intent and engine operations, reasons over the system's state, generates explanations and briefings, and mediates cross-functional decisions in group chat.
Same supply chain. Different posture. Defensible numbers.
a planning system that recognizes operational reality — variability is real, disruption is constant, response speed is a margin lever. Plans ship robust by construction. Disruption response stays proportional to the disruption.
working capital becomes a planning input, not a planning surprise. Trade-offs across revenue, margin, capital, and service become visible and optimized rather than negotiated. Every decision is auditable. The engine's value compounds because the platform learns from outcomes.
liberation from repeatable execution work. SAGE absorbs the system-mechanic tasks that consume hours per week — scenario configuration, briefing preparation, exception triage. Planners become strategic advisors; modelers become decision policy architects.
a stable order signal from a plan with built-in churn discipline — designed to absorb the named shocks before they reach the loading dock. Fewer pull-ins and push-outs. Stronger relationships.
Visible in how the platform is built, not in feature lists.
The probabilistic substrate is not an add-on; it is the substrate. The objective function prices tail risk explicitly. Drivers, Key Performance Indicators (KPIs), Decision Policies, and Governance Policies are first-class versioned objects — not configuration buried in master data. SAGE is bounded by the platform's schemas, which is why SAGE can never propose an invalid operation. The four typed source dimensions (Procurement, Production, Transport, Inventory) reflect how supply actually works, not how legacy ERPs modeled it.
See what this looks like inside your enterprise.
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