AI Agents + Quantum‑Enhanced Intelligence & Optimization
Building the intelligence layer for autonomous decision-making.
Design and run multi-agent workflows with live observability, multi-tenant enforcement, and cost/SLA-aware routing across classical and quantum backends.
- Workflow canvas with versioning, rollback, and deterministic replay
- OPA policy-as-code enforcement for tenant boundaries and backend allowlists
- OpenTelemetry traces + Postgres audit logs for audit-ready operations
Product previews (sample)
Workflow canvas, run explorer, ops metrics, and agent graph
Dark mode
Enterprise-grade governance: policy, evidence, and hybrid execution.
Workflow Canvas
Drag-and-drop workflow with routing + governance
Public launch
Countdown to broader availability
Invite-only design partner pilots are underway. We’re hardening the platform ahead of broader availability.
Pilot progress
2 of 5 pilots are in good progress (Supply Chain + Logistics). The remaining 3 are in initial stages (Pharma, Manufacturing, Retail & Commerce).
supply-chain
Supply chain supplier risk radar
Progress68%
Integrations complete. Running shadow-mode alerts with policy gates.
Evidence:Run ledger exportPolicy denials logAlert-to-playbook traceUpdated Jan 20, 2026
logistics
Logistics shipment exception copilot
Progress68%
Automations enabled for low-risk cases; approvals required for high-risk actions.
Evidence:Baseline ROI sheetSLA milestone reportApproval audit trailUpdated Jan 19, 2026
pharma
Pharma batch release assistant
Progress18%
Validation plan and data access controls being finalized with QA.
Evidence:Data handling diagramRisk registerTool allowlistUpdated Jan 18, 2026
manufacturing
Manufacturing plant scheduling optimizer
Progress18%
Constraints and baseline KPIs captured; milestone plan drafted with stakeholders.
Evidence:Success criteria checklistMilestone planSecurity Q&A deltasUpdated Jan 17, 2026
retail
Retail & Commerce merch demand copilot
Progress18%
Stakeholders aligned; measurement plan and data access requests in progress.
Evidence:Data access requestMeasurement planGovernance checklistUpdated Jan 16, 2026
Transparency and trust
Evidence you can verify
Enterprise adoption depends on more than model quality. UmamiMind ships with governance, audit trails, and operational transparency—so stakeholders can validate how decisions are made, how data is handled, and how incidents are managed.
Security overview
How data flows, where controls apply, and what we log for every run.
- Encrypted in transit and at rest (tenant-scoped).
- Least-privilege access + RBAC for ops actions.
- Deterministic audit logs for policy decisions and tool calls.
Control matrix
A practical view of controls mapped to common enterprise requirements.
- Policy enforcement (OPA-style) with decision traces.
- Run ledger exports (JSON/CSV) for audits.
- Retention + deletion controls aligned to DPA.
Trust signals
Live, outcome-aware signals: cost caps, SLA, policy denials, and incident posture.
- Cost and risk caps per mission/run.
- SLA milestones and breach indicators.
- Policy denials and exceptions tracked to approvals.
Status and incidents
Operational transparency: uptime posture, incident history, and response commitments.
- Public status page with component health.
- Incident records with timelines and actions.
- Operational simulation playbooks (tabletop).
Responsible AI
Governance for agentic systems: approvals, human-in-the-loop, and auditability.
- Approval gates for high-risk actions.
- Model/tool allowlists with policy-bound execution.
- Outcome + rationale captured with supporting evidence.
Subprocessors and legal
Clear disclosures: subprocessors, DPA terms, and responsible disclosure policy.
- Subprocessor inventory and purpose.
- DPA and privacy posture for enterprise procurement.
- Coordinated vulnerability reporting workflow.
Pilot transparency
What your stakeholders can review
Each design-partner pilot produces an evidence trail that can be shared with security, legal, procurement, and exec sponsors. The examples below are representative artifacts generated during the pilot lifecycle.
Supply Chain — Supplier risk radar
In progress · evidence captured for integrations, alerts, and mitigation actions.
- Run ledger exports (inputs, tools, cost caps, outcomes)
- Policy denials + exceptions tied to approvals
- Alert-to-playbook traceability (why we escalated)
Logistics — Shipment exception copilot
In progress · workflow automation with measurable throughput improvements.
- Before/after baseline metrics and ROI narrative
- SLA milestones and breach indicators
- Human-in-the-loop approvals for high-risk actions
Pharma — Batch release assistant
Initial stage · validation plan and data access controls documented.
- Data handling diagram + retention policy alignment
- Model/tool allowlist with change approvals
- Risk register and acceptance criteria
Manufacturing + Retail — Pilot intake
Initial stage · scoped success criteria and stakeholders aligned.
- Success criteria checklist + measurement plan
- Pilot milestone plan with owners and dates
- Security questionnaire deltas resolved pre-build
Trust posture
Auditability
Every run produces an exportable ledger of actions, tools, and decisions.
Predictability
Cost caps, policy gates, and SLA checkpoints reduce operational risk.
Accountability
Approvals, exceptions, and incidents are tracked to owners and timelines.
What you can export
Run ledger
Inputs, tools, decisions, costs, and outcomes—per run.
Policy decision logs
Allow/deny traces with reasons and enforcement points.
Evidence packs
Control matrix, questionnaires, and pilot ROI summaries.
Launch readiness
5 pilots in flight
Countdown target: Apr 5, 2026 · Stage driver: initial
18% ready42d to target
Active pilots
Supply chain supplier risk radar
P-SC-2401 · Supply Chain
Progress68%
Case studyShow
Problem: Unplanned supplier disruptions causing expedited freight and stockouts.
Approach: Policy-bound agent orchestration + vendor signals + mitigation playbooks with cost caps.
Outcome: Risk-scored suppliers with recommended mitigations and audit-ready rationale per action.
KPI: Target: 12% fewer expedites
Build/Run phase → integrations, guardrails, monitoring, and evidence capture.
Logistics shipment exception copilot
P-LG-2402 · Logistics
Progress68%
Case studyShow
Problem: Manual exception handling slows recovery and increases customer escalations.
Approach: Exception triage agent with evidence capture, SLA milestones, and HITL approvals.
Outcome: Faster routing of exceptions, fewer missed SLAs, clearer ownership and timelines.
KPI: Target: 25% faster exception resolution
Build/Run phase → integrations, guardrails, monitoring, and evidence capture.
Pharma batch release assistant
P-PH-2403 · Pharma
Progress18%
Case studyShow
Problem: Batch release packets require cross-team compilation and consistent evidence.
Approach: Checklist-driven packet builder + controlled tool allowlist + governance review gates.
Outcome: Cleaner release packets with traceable evidence and fewer back-and-forth cycles.
KPI: Target: 15% shorter release cycle
Intake complete → scope + data access in progress.
Manufacturing plant scheduling optimizer
P-MF-2404 · Manufacturing
Progress18%
Case studyShow
Problem: Frequent schedule churn drives overtime and late orders.
Approach: Constraint-based optimization + policy-bound changes + explainable tradeoffs.
Outcome: More stable schedules and fewer urgent change requests with measurable impact.
KPI: Target: 8% higher throughput
Intake complete → scope + data access in progress.
Retail & Commerce merch demand copilot
P-RT-2405 · Retail
Progress18%
Case studyShow
Problem: Forecast misses create markdowns and lost sales across channels.
Approach: Demand signal fusion + guided actions with spend caps and rollback plans.
Outcome: Improved forecast confidence with explainable drivers and action tracking.
KPI: Target: 6% less markdown waste
Intake complete → scope + data access in progress.
Progress bars and target date are derived from the same stage heuristic used by the launch countdown.
Designed to integrate with modern orchestration, governance, and quantum tooling
QiskitCirqPennyLaneKubernetesOpenTelemetryOPASupabasePostgres
What you get
A production-grade platform for agentic workflows and hybrid compute
Move beyond demos. Operate autonomous systems with deterministic workflows, backend arbitration, and controls required for enterprise adoption.
Agentic Intelligence
Multi-agent planning, execution, and verification—built to move from prototypes to production systems.
- Planner/Executor/Verifier patterns
- Tool registry + schema validation
- Replayable runs
Hybrid Quantum Optimization
Quantum-inspired solvers with optional QPU integration for complex optimization problems, routed by cost and SLA.
- Qiskit/Cirq/PennyLane adapters
- SLA-aware backend arbitration
- Cost ceilings + forecasting hooks
Enterprise Governance
Policy-first control plane with multi-tenant isolation, RBAC, and immutable audit trails.
- OPA/Rego policy enforcement
- Supabase auth + tenant scoping
- Postgres-backed audit logs
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.
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.
A two-minute tour you can reuse
Governed selection → live execution → persisted evidence → audit export.
Auto-advances every ~5s
1) Pick a governed use case
Select a vertical and use case. View constraints, governance gates, and evidence outputs.
Tip: run the same tour in every demo — it builds trust fast.
Trusted by teams exploring production pilots
Piloting quietly with enterprise and institutional teams under governed access.
What this means
Umamimind is evaluated through constrained pilots in live environments. Access is invite-only, enforced with role-based controls, and designed to produce auditable, evidence-backed outcomes.
Typical pilot flow
Ingest → constraints/policy → planning/optimization → simulation → approval → execution → audit export
Common evaluation goals
- • Decision optimization under real operational constraints
- • Agent governance, auditability, and safety boundaries
- • Cost-aware routing across classical and quantum backends
- • Measurable ROI before production rollout
Pilot characteristics
- • Limited-scope, high-impact workflows
- • KPI tracking + evidence artifacts
- • Full audit trails and RBAC by default
- • No public disclosure unless requested
During pilots, logos may be hidden by default unless disclosure is approved.
Representative pilot participants
Examples shown while pilots are under NDA and governed access.
Global Manufacturer (Fortune 1000)
Design partner
Logistics Provider (Regional)
Pilot candidate
Financial Institution (Mid-market)
Evaluation
Pharma R&D Group (Anonymous)
Discovery
Research Lab (University)
Prototype
Government Program Office
Exploration
Disclosure policy
We default to privacy-first pilots. Participants can opt into public listing, anonymous listing, or private-only evaluation. Evidence artifacts and KPI deltas are shareable in redacted form.
Architecture
A clean separation between orchestration and execution
Agents plan and execute work; policies constrain decisions; the router selects the best runtime; observability makes every run explainable and auditable.
Interface
Workflow canvas, templates, APIs, and run explorer
Orchestration
Multi-agent runtime, workflow engine, tool registry
Governance
OPA policy engine, RBAC, tenant boundaries, audit logging
Execution
Classical/GPU compute + quantum providers (Qiskit/Cirq/PennyLane)
Observability
OpenTelemetry traces, metrics dashboards, live event streaming
Storage
Postgres for audit logs, workflow versions, and run metadata
Operational outcomes
Governed autonomy with audit-ready evidence
Your platform team gets repeatable runs, explainable routing, and controls that scale across tenants and backends.
Deterministic operations
Replayable runs
Versioning, rollback, and deterministic replay
Governance
OPA-enforced
Policy decisions logged and attributable
Observability
OpenTelemetry
End-to-end traces across agents/tools/backends
Isolation
Multi-tenant
Tenant boundaries + RBAC (Supabase auth)
Updates
Blog and press
Real-world patterns from enterprise adoption, public-sector readiness, and governance-first agentic execution.
Latest posts
Deep dives
Governance2026-01-10
Agentic AI governance in the enterprise: from policy to execution
How policy-bound orchestration turns LLM capability into auditable, enterprise-safe execution.
Read →
Enterprise AI2026-01-04
From pilot to paid: how AI programs cross the enterprise chasm
Design pilots for conversion with milestones, evidence exports, and procurement-ready artifacts.
Read →
Observability2025-12-22
Observability for LLM agents: tracing cost, policy, and outcomes
Run-level evidence that security, finance, and ops teams can use without guesswork.
Read →
Press
News
Product Announcement2026-01-12
UmamiMind launches agentic orchestration for regulated enterprises
Policy-bound execution, auditability, and pilot-to-production workflows designed for high-trust environments.
Customer Update2025-12-02
Selected for early pilots across finance and supply chain
Initial programs focus on fraud triage, planning optimization, and governed automation with measurable outcomes.
Media inquiries
press@umamimind.ai
Ready to operationalize autonomous decision-making?
See a live workflow run, routing decisions, governance controls, and observability in a technical demo.