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From Demo to Operated Product · Simulator 1.1

Physical AI Product Flight Simulator

Explore a fictional product decision through five Product System gates and six decision lenses. Change one assumption and inspect completeness, recovery, operator burden, economics and the evidence still missing. Every diagnostic and export remains illustrative and experiment-only.

The Hyperion difference

Business intent becomes a technology contract. Technology evidence returns as a business decision.

Mohammed Cherifi and Hyperion bridge customer value, product management, hardware, software, AI, data, safety, operations and organizational delivery—through the full product lifecycle.

Business

Problem, buyer, economics, investment and customer commitment

Product

Discovery, method fit, roadmap, evidence and release gates

Technology

Hardware, software, AI, data, reliability and operating envelope

Authority

A named human owns every consequential decision

Four clearly fictional presetsDeterministic calculationsFailure propagationVersioned decision exports

How to use it

Fly the decision before you fly the product.

Define

Set the customer problem, promise, economics, constraints, envelope and accountable owner.

Stress

Inject failures and inspect deterministic consequences across the entire product system.

Decide

Record the illustrative decision, then export its Product Decision Record, Product Bridge Contract, Product System record and provenance.

Explore the decision without JavaScript

Without JavaScript, the displayed calculations describe the initial fictional preset only. Interactive edits, recalculation, downloads and the optional AI council require JavaScript. No result authorises a product release. Read the six-stage protocol and Product System reference to work through your decision manually.

Read the Decision Lab protocol Read the Product System reference Run a Product Decision Review

Scenario control

Change an assumption. See every consequence.

Fictional reference scenario

Live deterministic consequence model

Fictional AMR fleet rollout

Calculated simulator fields come from versioned code and the visible assumptions at left. The optional council cannot change these calculation results, constraints or release status.

Modeled experiment signal

hold

6 failed · 2 conditional gates

Lifecycle net value

€11,070,366

4-year value minus TCO

Effective availability

92.01%

Target 97%

Capacity load

38.3%

0 weeks queue delay

Risk screen

60.5/100

high · 28% evidence gap

Payback

5.4 mo

€8 / productive hour

Budget coverage

171.4%

€999,360 headroom

From Demo to Operated Product

Integrated Product Release Case: experiment only

This public simulator evaluates fictional, visitor-supplied assumptions. It cannot establish release-grade evidence, specialist acceptance or authority-matched field performance.

An Integrated Product Release Case is the accountable product recommendation for one configuration, use, operating envelope and commitment. A release-ready result binds current evidence and product completeness to that configuration, envelope, claim set, intended Human–Machine Authority and operating supervision. It is not a safety case, certification, conformity assessment or legal opinion.

Gate: Productization · Decision: Demo to operated product

Customer, operator and product value · Economics and business viability · AI, data, evaluation and HMI · Complete system architecture · Safety, cybersecurity, compliance and governance · Deployment, operations, service and ecosystem

Reliability and exposure

Declared gap. Ratios use declared fictional counts, independently of the failure multipliers.

Exposure unit: fictional pallet-transfer attempts per fleet-year
Successful outcomes / attempts (%): 95
Interventions / 1,000 attempts: 20
Successful recoveries / recovery attempts (%): 80
Longest continuous duty (hours): 8
Operator minutes / duty hour: 6
Lifecycle cost / declared successful outcome (EUR): 3.67

Product Completeness

Capability · Requires evidence
Fictional capability assumption; configuration-bound evidence is absent.
Reliability · Declared gap
Shift-length obstruction and recovery evidence is missing.
Authority · Requires evidence
Fictional authority assumption; configuration-bound evidence is absent.
Evaluation · Requires evidence
Fictional evaluation assumption; configuration-bound evidence is absent.
Operations · Requires evidence
Fictional operations assumption; configuration-bound evidence is absent.
Service · Declared gap
Recovery staffing and spares response have not been tested.
Commercial · Requires evidence
Fictional commercial assumption; configuration-bound evidence is absent.
Evidence · Requires evidence
Fictional evidence assumption; configuration-bound evidence is absent.
Scale · Declared gap
A second site's maps and workflows have not been evaluated.

Human–Machine Authority

Declared gap

Evaluated authority: Bounded actuation
Intended authority: Bounded actuation
Evaluated supervision: Continuous operator
Intended supervision: Monitoring only
Authority gap: Declared no
Supervision gap: Declared yes
Intervention available: Declared yes
Deterministic stop: Declared yes
Recovery owner: Illustrative warehouse shift supervisor

Agent–simulator–critic diagnostic

Declared gap. Separately prompted roles may share dependencies and blind spots.

Critic present: Declared yes
Independence declared: Declared no
Shared dependencies disclosed: Declared no
Calibration declared: Declared no
Deterministic checks: Declared yes
Human escalation: Declared yes

Evidence gaps

Public visitor inputs are fictional assumptions, not release evidence; independent validation remains required.
Evaluated and intended authority, supervision, intervention and stop controls are not aligned.
Critic independence or shared-dependency disclosure is unresolved.
reliability completeness is declared gap.
service completeness is declared gap.
scale completeness is declared gap.

Requirement gaps

Define and evidence the human baseline for useful work, burden, quality and cost.
Bound the product claim to one configuration, intended use and operating envelope.
Translate the claim into verifiable product and system requirements.
Resolve data provenance, IP and learning rights for the intended lifecycle.
Specify memory, long-horizon task state and stale-state behaviour.
Specify dead-end detection and accountable recovery escalation.
Define generalization slices and the evidence required for each slice.

Technology Curve

Fictional warehouse task policy with deterministic motion limits · Declared gap · Plateau risk: unknown

Required product bar is conditional.
Expected ceiling is unknown.
Future cost is unknown.
Migration path is gap.
Vendor exit is gap.

Capability and Complexity Delta

Capability: Requires evidence · Experiment signal: hold

Edge/cloud boundary is gap.
Data and learning rights are gap.
The declared critic is not independent from the system it evaluates.
manufacturing readiness is conditional.
supply readiness is conditional.
deployment readiness is conditional.
service readiness is gap.
regulatory readiness is unknown.
cybersecurity readiness is unknown.
commercialCommitment readiness is gap.
rollback readiness is gap.
retirement readiness is unknown.

Configuration impact

Fictional AMR chassis, sensors, task policy, map and warehouse workflow; rollback rehearsal is missing.
Validate the declared model-system set: task_specific_policy, physics_simulator, deterministic_controller.
Revalidate world-model roles: scenario_generator.
Rollback is not supported by evidence for the declared configuration.

Reopening conditions

Reopen after any policy, model, hardware, data, tooling or site configuration change.
Reopen if sustained duty, intervention demand or recovery exceeds the declared assumptions.
Reopen when sensing degradation changes the validated operating envelope or fallback behaviour.
Reopen when supplier lead time changes the release configuration or customer commitment.
Reopen when recovery effectiveness falls below the declared exposure tolerance.
Reopen when service demand exceeds the support model or customer commitment.
Reopen before any change to intended use, configuration, operating envelope, authority or commercial commitment.
Reopen when evidence expires, is corrected or no longer matches the release configuration.

Proposed Product Decision Review scope

Resolve the demo to operated product decision at the productization gate.
Public visitor inputs are fictional assumptions, not release evidence; independent validation remains required.
Evaluated and intended authority, supervision, intervention and stop controls are not aligned.
Critic independence or shared-dependency disclosure is unresolved.
reliability completeness is declared gap.
Define and evidence the human baseline for useful work, burden, quality and cost.
Bound the product claim to one configuration, intended use and operating envelope.
Translate the claim into verifiable product and system requirements.
Required product bar is conditional.
Expected ceiling is unknown.

Release-condition ledger

A decision is only as strong as its weakest unowned gate.

Fleet availability

A missed availability gate changes customer value, service load and the release case.

Modeled value

92%

Gate

≥ 97%

Delivery and commissioning capacity

Overload creates queues across roadmap work, commissioning and field response.

Modeled value

38.3%

Gate

≤ 80% preferred; >95% blocks

Lifecycle customer-value case

A negative or late-return case requires scope, price, reliability or operating-model change.

Modeled value

€11,070,366 net; 5.4 months

Gate

Positive lifecycle net value inside contract term

Combined safety and evidence risk

Risk is a screening signal only; a named safety authority owns acceptance.

Modeled value

60.5/100 · high

Gate

≤35 preferred; >55 blocks

Operating-envelope evidence

Unverified envelope claims cannot support a scaled release commitment.

Modeled value

72%

Gate

≥80% preferred; <60% blocks

Investment allocation

Unallocated or double-counted investment obscures real trade-offs.

Modeled value

100%

Gate

100%

Upfront funding coverage

An unfunded hardware, integration, spares or commissioning requirement invalidates the release plan.

Modeled value

171.4% · €999,360 headroom

Gate

≥100% of modeled upfront investment; 90–99.9% conditional

Accountable human decision

No model or simulator can own a safety, release, investment or customer commitment.

Modeled value

Illustrative accountable product owner · Product and operations sponsor

Gate

Named owner, role and decision

Reliability and exposure profile

Architecture and release decisions cannot precede an explicit reliability and exposure profile.

Modeled value

declared gap

Gate

Declared denominator, sustained-duty, intervention, recovery, fallback and severity-tolerance basis

Product completeness

Capability alone cannot waive reliability, authority, evaluation, operations, service, commercial, evidence or scale gaps.

Modeled value

3 of 9 dimensions unresolved

Gate

All nine dimensions assessed; declarations still require evidence

Authority-matched operation

Evidence collected under lower authority or stronger supervision cannot silently justify intended operation.

Modeled value

declared gap

Gate

Evaluated and intended authority/supervision aligned with intervention, recovery and stop controls

Agent–simulator–critic independence

An evaluator with unresolved common-mode dependencies cannot establish product readiness.

Modeled value

declared gap

Gate

Versioned critic, disclosed dependencies, calibration, deterministic checks and human escalation

Integrated Product Release Case

This public simulator can recommend a bounded next experiment, never release readiness.

Modeled value

experiment only

Gate

Release-grade, configuration-bound evidence and named specialist decisions

Operated economics

Upfront investment: €1,400,640
Declared investment budget: €2,400,000
Upfront funding gap: €0
Annual operating cost: €486,110
Lifecycle TCO: €3,345,079
Annual productive value: €3,603,861
Annual contract revenue: €432,000
Productive fleet hours / year: 105,995.9
Service hours / year: 2,339.7

Failure propagation

Sensor degradation · 35%

— 2.1 availability points removed
— 8.4% service-load increase
— 6.3 risk points added

Supplier delay · 55%

— 6.6 weeks added to supplier lead time
— 4.4% coordination-load increase

Recovery failure · 40%

— 4 availability points removed
— 12% service-load increase
— Recovery evidence and sustained-duty claims require revalidation

Service overload · 30%

— 15% service-load increase
— 1.2 availability points removed
— Support capacity and customer commitments require revalidation

Method-Fit Lab

Continuous discovery + systems engineering evidence spine + Kanban for constrained flow.

Method choice is contextual. Safety, compliance, investment, release and customer commitments remain owned human decisions regardless of agile method.

Continuous discovery

100

strong fit

Keeps customer evidence, field learning and product assumptions connected to delivery decisions.

Use evidence cadence and decision records; do not treat interviews as release authority.

Scrum

conditional fit

Supports bounded product increments where uncertainty can be reduced inside a stable team cadence.

Keep hardware, safety and commissioning gates outside the fiction of a purely software Definition of Done.

Kanban / flow

strong fit

Makes integration, field defects, evidence work and service queues visible as constrained flow.

Set explicit classes of service and WIP limits for safety, field and supplier work.

Systems engineering

100

strong fit

Controls interfaces, operating-envelope evidence and verification across hardware, software, AI and operations.

Use as a product evidence spine, not as a substitute for customer discovery or incremental learning.

SAFe

conditional fit

May coordinate multiple teams and portfolio dependencies when scale is real and independently evidenced.

Do not introduce enterprise-scale ceremony for a small team; Scrum, Kanban and systems interfaces are sufficient.

Sensitivity range

Lifecycle net value when one assumption moves and every other input remains fixed.

Variable	Low case	Base	High case
Hardware cost (−20% / base / +20%)	€11,320,350	€11,070,366	€10,820,382
Productive value/hour (−20% / base / +20%)	€8,187,277	€11,070,366	€13,953,455
Availability (−5 pts / base / +5 pts)	€10,287,006	€11,070,366	€11,853,726

Optional Mistral-only Product Council

Three roles. One provider family. No artificial consensus.

AI disclosure before interaction

When you submit a council request, you interact with Mistral AI through Hyperion’s VPS and the EU regional inference endpoint. Mistral explains and challenges the structured decision. Versioned code calculates the simulator fields; council commentary is generated by AI. Outputs can be wrong and are not safety certification, legal advice or an autonomous release decision. The named human owner remains responsible.

Only the fictional scenario and your question are sent. This lab does not persist their content; routine logs retain content-free operational metadata. Do not submit personal, confidential, special-category, export-controlled or safety-critical operational data. Privacy and deletion rights are described in the site privacy notice.

This is a multi-model Mistral council, not independent cross-provider verification. Shared provider and model-family failures remain possible.

Decision question sent to the Product Council

I confirm this scenario is fictional and contains no personal, confidential, regulated, export-controlled or safety-critical operational data.I understand that this scenario and question will be sent from Hyperion’s VPS to Mistral AI for model-derived commentary.

Accountable exports

Carry the Product System into a reviewable decision record.

JSON records include the scenario, calculated result and validated Product System record. Markdown supports human review. Provenance binds the inputs and result to stable SHA-256 hashes. Every artifact remains illustrative and experiment-only.

Input SHA-256
calculating…

Result SHA-256
calculating…

Inspect formulas, assumptions and limitations

Formula register

Upfront funding coverage
total investment budget ÷ modeled upfront investment
Units: % funded
Theoretical availability
MTBF ÷ (MTBF + MTTR), then explicit failure penalties
Units: % available time
Upfront investment
units × (hardware + integration) + spares + commissioning days × day rate
Units: EUR
Annual operating cost
platform + data/AI ops + inference + energy + service labour + replacements
Units: EUR/year
Annual productive value
units × productive hours × effective availability × value/hour × injected modifiers
Units: EUR/year
Capacity utilization
(planned work + commissioning load) ÷ (team hours × teams)
Units: % monthly capacity
Risk screening score
likelihood × impact + evidence gap + explicit failure increments
Units: 0–100 screening index

Assumptions

— All scenarios are fictional and illustrative; figures are visitor-controlled assumptions, not Hyperion client evidence.
— Currency is EUR and values are undiscounted; tax, financing and inflation are excluded from this thin slice.
— MTBF/MTTR availability is a planning approximation and does not establish functional-safety performance.
— Commissioning capacity assumes eight engineering hours per day and forty site-level setup hours per deployment.
— Failure injections are transparent multipliers, not probability forecasts or field observations.
— The investment budget is compared with modeled upfront hardware, integration, spares and commissioning cost; operating reserve and financing are excluded.

Limitations

— This result is not safety certification, legal advice, conformity assessment or an autonomous release decision.
— Queueing is a deterministic capacity approximation; it is not a discrete-event simulation of a specific operation.
— No real customer, personal, confidential, export-controlled or safety-critical operational data should be entered.
— A qualified human owner must validate evidence, operating envelope, compliance duties and consequential commitments.

From illustration to your product decision

Bring the real decision. Keep confidential evidence out of the public lab.

Run a Product Decision Review