Built for the Wedge. Architected for Scale.
DcisionAI starts with high-intensity ops like dispatch and field scheduling — then scales across the enterprise through a modular agentic architecture.
Start with Operational Complexity
Our first wedge: fleet dispatch. A real-world challenge where live signals, constraints, and tradeoffs collide. DcisionAI integrates with your systems to orchestrate real-time, explainable decisions.
- Live signals: GPS, traffic, weather, incidents
- Constraint handling: SLAs, driver fatigue, cost factors
- Agentic decisions: Optimal plans based on real-world tradeoffs
- Human-in-the-loop: Managers can audit, approve, or override decisions
MCP: Model, Context, Protocol
At the core of DcisionAI is MCP — a modular framework that separates decision logic (Model), real-world inputs (Context), and governance constraints (Protocol). This structure powers agentic workflows that are explainable, controllable, and enterprise-ready.
- Model: Optimization logic or generative agent
- Context: Structured real-time signals and constraints
- Protocol: Guardrails for override, audit, and improvement
Composable by Design — Powered by Plugins
Every decision module in DcisionAI is a plugin. From model logic to override triggers, each component is swappable and composable — enabling fast deployment into one wedge, and seamless expansion to others.
- Model Plugins: Route scoring, field dispatch, demand forecasting
- Policy Plugins: SLA rules, escalation logic, compliance triggers
- Override Plugins: Manual reviews, audit trails, safety protocols
Scale from Wedge to Platform
Once live in one domain, DcisionAI can be extended across any decision flow — from workforce scheduling to inventory, pricing, and finance — using the same agentic engine.
- Same engine. New domain.
- Same protocol. New inputs.
- From fleet to finance — one platform.