Launch
Mar 31, 2026
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Case Studies

Real outcomes, realistic constraints

Enterprise-safe scenarios demonstrating agentic planning, policy enforcement, and optimization in production-grade workflows.

Supply Chain
Reducing Supply Chain Shock Exposure with Autonomous Decision Agents

Scenario planning that is constraint-aware, auditable, and fast enough for real disruption response.

Scenario planning reduced from days to minutesConsistent policy enforcement across procurement and logistics actions
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Supply Chain
Multi‑Echelon Inventory Rebalancing Under Service‑Level Constraints

A governed optimization loop that reallocates inventory across nodes while respecting lead times, capacity, and policy.

Fewer stockouts during volatility windows (bounded, measurable improvements)Reduced expediting through better rebalancing decisions
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Financial Services
Autonomous Portfolio Rebalancing Under Regulatory Constraints

Decision automation that respects risk, liquidity, and policy, with provable audit trails.

Reduced manual rebalancing workload while improving controlsImproved responsiveness to volatility with bounded actions
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Financial Services
AML Investigation Orchestration with Multi‑Agent Evidence Packs

Reduce analyst load by generating structured cases, policy‑checked actions, and complete evidence trails.

Faster triage and case assembly for investigationsFewer false escalations through policy-aware enrichment
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Pharma
Accelerating Trial Prioritization with Multi-Agent Simulation

A pragmatic way to compare trial designs and resource allocations under uncertainty.

Faster prioritization of candidate trial strategiesImproved clarity on constraints and decision rationale
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Pharma
GMP Batch Release: Deviation Triage with Policy‑Checked Agents

Agentic evidence assembly for deviation investigations while respecting regulated workflows and data controls.

Reduced time to assemble deviation context and evidenceMore consistent triage decisions aligned to SOPs
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Manufacturing
Self-Optimizing Production Planning Without Black-Box AI

Explainable, constraint-aware planning that operators can trust.

More stable production schedules under changeImproved operator trust through explainability
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Manufacturing
Predictive Maintenance Planning with Constraint‑Aware Scheduling

Turn maintenance signals into safe, prioritized actions without disrupting throughput and quality.

Reduced unplanned downtime through earlier interventionsFewer schedule shocks by coordinating maintenance windows
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Government
Disaster Resource Allocation with Auditable Decision Agents

Coordinate logistics, shelters, and distribution with policy-aware autonomy during high-pressure incidents.

Faster allocation decisions under changing field conditionsImproved accountability with end-to-end decision evidence
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Research Labs
Experiment Planning for Research Labs: From Ideas to Executable Schedules

Coordinate instruments, compute, budgets, and safety approvals with governed autonomy.

Faster experiment scheduling under resource constraintsReduced conflicts across instruments, staff, and compute
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How Umamimind works

Across verticals, the execution loop is consistent: signals → plans → constraints → ranking → approval → execution.

Signals
1

Operational data and events create decision triggers.

Agents
2

Propose bounded actions with assumptions and rationale.

Constraints
3

Policy and invariants filter unsafe actions.

Optimization
4

Rank feasible plans across trade-offs.

Approval
5

Humans retain control where needed.

Execution
6

Idempotent tool calls plus audit logging.

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