Launch
Mar 31, 2026
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Platform

Governed autonomy for enterprise decision-making

Umamimind combines agentic planning, constraint verification, and optimization (including quantum-inspired acceleration where it helps) to produce ranked decisions with full evidence trails.

What it does
  • Turns inputs + constraints into ranked plans (not just a chatbot).
  • Enforces policy gates before tools run.
  • Captures evidence: assumptions, constraints, objective scores, approvals, and execution traces.
  • Supports human-in-the-loop control with rollback and rate limiting.
How it works
  1. Ingest signals (ERP/telemetry/market/lab/field).
  2. Multi-agent plan generation with explicit assumptions.
  3. Policy + invariants reject unsafe actions early.
  4. Optimizer ranks feasible actions across cost/SLA/risk.
  5. Approval workflow and audited execution.

Reference architecture, built for approval

umamimind.ai sits between agents and execution infrastructure. Actions are policy-checked, budgeted, observable, and captured as evidence—by design.

Hover any block for details.
umamimind.ai reference architecture
Diagram: interface → orchestration → governance → hybrid execution → evidence.
Control plane
Tenant isolation, RBAC, approvals, and policy bundles (OPA/Rego) for every agent action.
Orchestration
Decision gates, HITL routing, retries, and tool mediation with deterministic audit trails.
Execution plane
SLA-aware routing across classical solvers, workflow runners, and optional quantum backends.
Evidence & telemetry
Run logs, cost traces, evaluations, and export-ready evidence packs for procurement and security.
Vertical use cases

Prebuilt flows and simulations for supply chain, financial services (including DeFi transaction optimization), pharma, manufacturing, government, and research labs.

Use cases by vertical

Explore realistic, constraint-driven flows and simulations. These pages are intentionally practical: inputs, constraints, policy gates, ranking, approvals, and auditable execution.

Interactive use case preview
Pick a vertical and a scenario to preview the workflow flow diagram and a guided run simulation.
Key outcomes
  • Stockout rate / fill rate
  • Inventory turns and holding cost
  • Solve time and cost per run
  • Plan stability (week-over-week variance)
Governance notes
  • OPA allowlist for approved backends/providers
  • Budget ceilings per tenant and per run
  • Region-based data boundary enforcement
Supply Chain: Multi-echelon inventory rebalancing
Continuously rebalance safety stock across DCs and stores using live demand signals, lead times, and service-level constraints.
Loading visuals…
Rendering the flow diagram and simulation.
What you’ll see

Animated flow, a “run explorer” style trace, and a small KPI panel for each scenario.

Flow diagram
A typical Supply Chain workflow run (illustrative).
Animated simulation
A guided walkthrough showing how agents, routing, and OPA governance interact during execution.
Current step
Live eventsOPA decisionBackend routingOTel traceAudit log
Run log
This animation is a UI simulation to communicate flow. In production, these events stream from the runtime and are backed by traces and audit logs.
Note: Visuals are illustrative. In pilots, connectors and constraints map to your systems and policies.
Competitive comparison

Umamimind is designed as decision infrastructure: governable actions, verifiable constraints, and evidence trails.

Piloting quietly with top-tier enterprise teams
CapabilityUmamimind.aiTypical alternatives
Constraint verification before executionPolicy gates + invariants; blocks unsafe actionsOften post-hoc guardrails or manual review
Evidence & audit trails (decision provenance)Run-level evidence packs: inputs, assumptions, scores, approvalsPartial logs; weak provenance for procurement/audit
Human-in-the-loop controlsEscalations, approvals, overrides, rollback hooksBasic approvals or ad-hoc workflows
Optimization (classical + quantum-assisted optional)Objective-based ranking with sensitivity notesHeuristics or single-objective tuning
Multi-tenant governance & isolationTenant boundaries + policy-as-code patternsDepends on implementation; often DIY
Enterprise readinessRate limits, idempotency, trace/log correlation patternsVaries; often requires significant hardening
Note: This matrix is a product-positioning summary. Specific capabilities depend on deployment scope, connectors, and policy requirements.
PilotsDemoTour