Continuous discovery
100strong 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.
Back to Decision Lab
Flagship decision environment · Release 1.0
Physical AI Product Flight Simulator
Turn a promising prototype into an accountable product decision. Change one assumption and see the consequences move across business value, technology, safety, delivery, field operations and organisation.
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.
01
Define
Set the customer problem, promise, economics, constraints, envelope and accountable owner.
02
Stress
Inject failures and inspect deterministic consequences across the entire product system.
03
Decide
Record a human-owned decision and export a Product Decision Record and Product Bridge Contract.
Live deterministic consequence model
Fictional AMR fleet rollout
Every number below comes from versioned code and the visible assumptions at left. Mistral cannot change the arithmetic, constraints or release status.
Release recommendation
proceed with conditions
0 failed · 2 conditional gates
Lifecycle net value
€12,081,015
4-year value minus TCO
Effective availability
97.21%
Target 97%
Capacity load
36.9%
0 weeks queue delay
Risk screen
48.1/100
elevated · 28% evidence gap
Payback
5 mo
€7 / productive hour
Budget coverage
171.4%
€999,360 headroom
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.
Observed
97.2%
Gate
≥ 97%
Delivery and commissioning capacity
Overload creates queues across roadmap work, commissioning and field response.
Observed
36.9%
Gate
≤ 80% preferred; >95% blocks
Lifecycle customer-value case
A negative or late-return case requires scope, price, reliability or operating-model change.
Observed
€12,081,015 net; 5 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.
Observed
48.1/100 · elevated
Gate
≤35 preferred; >55 blocks
Operating-envelope evidence
Unverified envelope claims cannot support a scaled release commitment.
Observed
72%
Gate
≥80% preferred; <60% blocks
Investment allocation
Unallocated or double-counted investment obscures real trade-offs.
Observed
100%
Gate
100%
Upfront funding coverage
An unfunded hardware, integration, spares or commissioning requirement invalidates the release plan.
Observed
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.
Observed
Illustrative accountable product owner · Product and operations sponsor
Gate
Named owner, role and decision
Operated economics
- Upfront investment
- €1,400,640
- Declared investment budget
- €2,400,000
- Upfront funding gap
- €0
- Annual operating cost
- €437,121
- Lifecycle TCO
- €3,149,124
- Annual productive value
- €3,807,535
- Annual contract revenue
- €432,000
- Productive fleet hours / year
- 111,986.3
- Service hours / year
- 1,873.2
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
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.
Scrum
71conditional 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
79strong 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
100strong 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
70conditional 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%) | €12,330,999 | €12,081,015 | €11,831,031 |
| Productive value/hour (−20% / base / +20%) | €9,034,987 | €12,081,015 | €15,127,043 |
| Availability (−5 pts / base / +5 pts) | €11,297,655 | €12,081,015 | €12,518,076 |
Optional Mistral-only Product Council
Three roles. One provider family. No artificial consensus.
AI disclosure before interaction
If enabled, you interact with Mistral AI through Hyperion’s VPS and the EU regional inference endpoint. Mistral explains and challenges the structured decision; deterministic code owns every number. 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.
AI council gated; deterministic lab fully operational
Hyperion will not send public inputs to Mistral until every provider, privacy, legal and incident-response launch gate has evidence.
- awaiting operator evidenceProduction key and every pinned model verified through the EU endpoint
- awaiting operator evidenceZero Data Retention approved, active and verified for the production workspace and services
- awaiting legal reviewCurrent DPA retained and subprocessor/transfer position reviewed
- awaiting legal reviewPurposes, legal bases, data categories, retention, rights and DPIA screening recorded
- awaiting legal reviewAI Act role/risk classification, human oversight and escalation path reviewed
- awaiting operator evidenceSecurity incident and personal-data-breach procedures owned and exercised
Accountable exports
Turn a simulation into a decision record and a bridge contract.
JSON supports machine use. Markdown supports a human review. Both carry stable SHA-256 input/result hashes, versions, limitations and the named decision owner.
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