Readiness before forecasting
Data quality, cadence and authorization checks can stop the process before unreliable output reaches a planner.
Pilot readinessIndustrial decision-intelligence software
Turn uncertainty into decision evidence.
Phaneon builds scientific intelligence for complex operational systems—combining forecasting, simulation and governed evidence to help industrial teams make better planning decisions.
GovernedFail-closed controls
Evidence-ledMeasured, bounded claims
InteroperableVersioned architecture
Forecast envelope
Observed
t−3t−2t−1tt+1t+2
Decision stateEvidence readyBaseline retained · No write path
Scientific intelligence for complex systems.
Current product
Forecasting that knows its limits.
Phaneon Polaris is a governed forecasting system for difficult operational demand. It combines probabilistic forecasts with readiness checks, comparative evaluation and explicit human control.
Controlled shadow-pilot preparation
Probabilistic evidence
Prediction intervals and multiple scoring dimensions keep uncertainty visible instead of reducing it to one point.
Champion discipline
Candidate methods must pass frozen, multi-metric gates. A failed challenger leaves the current champion unchanged.
Planner control
Scenarios and overrides remain explicit, attributable and separate from automatic production decisions.
Rigel · Production simulation
Test operational choices before they touch operations.
Rigel is an offline, non-writing policy simulator designed to connect forecast uncertainty with inventory and service decisions. Its completed synthetic study produced a strong decision-value signal and cleared the gate for a separately controlled shadow pilot.
Development statusSimulator validated · Shadow pilot next
Candidate versus baseline across five preregistered synthetic replicates.
Mean improvement across the same paired synthetic evaluations.
Mean improvement across five independently seeded replicates.
Common-path comparisons designed to isolate the policy difference.
All declared controls, recomputations and artifact checks passed.
No material-flow imbalance detected in the validated synthetic study.
Mathematical core
Uncertainty becomes a constrained decision experiment.
Rigel combines state-transition mathematics, probabilistic demand paths and paired policy evaluation. Each candidate is tested against the same simulated future while capacity, lead time, non-negativity and information-timing constraints remain fixed.
Synthetic evidence boundaryNo real operational data · No production writes · No policy promotion
Inventory evolves through explicit arrivals and demand, with conservation checked on every simulated path.
Probabilistic demand is translated into policy levels through calibrated empirical quantiles.
Both policies face matched stochastic paths so the comparison focuses on the decision rule itself.
Non-negative state, capacity, exact lead time and nonanticipativity are enforced as hard simulation rules.
What the result supportsA controlled, no-write operational shadow pilot.
What remains unprovenProduction reliability and real-world economic superiority.
Polaris interface
Decision evidence, made reviewable.
Polaris is designed to help planners understand the forecast, compare it with a practical baseline and decide where human attention is needed. The examples below use illustrative, non-customer data.
Demand outlookMonthly planning view
Illustrative dataDecision stateReview
Forecast horizon6 months
ConfidenceBounded
See uncertainty before committing. Review the central forecast together with its plausible range and current decision status.
Method comparisonBaseline protection
Lower error is betterSeasonal baselineCurrent champion
RetainPolaris candidateChallenger
ReviewRelease gateChallenger not promoted
The existing method remains when a candidate does not demonstrate a reliable advantage.
Protect what already works. Complexity is not rewarded unless it improves the agreed business-relevant measures.
Planner reviewException workspace
Human controlledHigh
Wide forecast rangeReview supply exposure
OpenMedium
Demand cadence changedConfirm data context
ReviewLow
Baseline remains stableNo intervention proposed
MonitorFocus expertise where it matters. Prioritize uncertain and consequential cases while keeping every override attributable.
Product concept views · Illustrative data · No customer, employer or production information shown
The Phaneon method
Precision is a process, not a promise.
We separate implemented software controls, research evidence and operational readiness. That distinction protects the integrity of every claim—and every decision.
Measure before claiming
Every result belongs to a named dataset, metric, horizon and baseline. Public evidence is never presented as customer proof.
Abstain when evidence is weak
The system is designed to refuse forecasting when data quality, cadence or authorization requirements are not met.
Keep decisions reversible
Champion selection, scenarios and overrides remain governed, visible and subject to explicit release gates.
Build an evidence trail
Versioned interfaces, audit chains and reproducible release artifacts make each operational step reviewable.
Scientific foundation
Forecasts are hypotheses. Evidence decides.
Phaneon treats forecasting as a measurable scientific process: uncertainty stays visible, every candidate faces a simple baseline, and weak evidence leads to abstention rather than automation.
Probabilistic forecasting
Prediction intervals describe a range of plausible outcomes instead of disguising uncertainty as one exact number.
Rolling-origin evaluation
Models are tested across successive historical cutoffs so evaluation better reflects how forecasts are used over time.
Baseline protection
A complex model must earn its place against simple reference methods. If it does not, the baseline remains the safer choice.
Calibration and bias
Accuracy alone is insufficient. Directional bias and interval coverage reveal risks that a single headline metric can hide.
Evidence-aware abstention
Insufficient data quality, cadence or authorization should stop the workflow before unreliable output reaches a decision.
Reproducible claims
Each published result belongs to a named dataset, frozen method, forecast horizon, metric and documented limitation.
Evidence posture
Transparent by design.
The controlled demonstration reached 75.7% weighted forecast accuracy, +7.1 percentage points of selected forecast value added and 83.3% interval coverage. These are promising development signals from synthetic data—not yet customer or production proof.
Review methodology, results and limitationsDevelopment signalPromising
Baseline protectionActive
Industrial validationNext gate
Customer outcomesTo be measured
Product constellation
The Phaneon product portfolio.
Polaris is the current forecasting product. Rigel is in active development. Regulus, Mira, Altair and Sirius define the longer-term portfolio for planning, scenarios, integration and explainability.
Current product✦
Forecasting intelligence
Governed demand ingestion, data-quality readiness, probabilistic forecasting, champion selection and planner-controlled scenarios.
In active development✦
Production simulation
Non-writing policy simulation designed to connect forecast evidence with operational decision testing.
Portfolio direction✦
Planning & scheduling
A future planning layer for constrained, explainable operational coordination.
Portfolio direction✦
Scenario intelligence
A future environment for disciplined scenario exploration and decision comparison.
Portfolio direction✦
Enterprise integration
A future integration layer for versioned, interoperable operational evidence.
Portfolio direction✦
Monitoring & explainability
A future observability layer for model behaviour, lineage and decision accountability.
Design-partner pathway
Prove value before production.
Phaneon is preparing a narrow, no-write shadow evaluation for a qualified industrial design partner. The protocol begins with a frozen baseline, agreed metrics, anonymized historical data and an explicit no-value exit.
contact@phaneonscience.com01
Define baseline, scope and success criteria.
02
Evaluate historical data without operational writes.
03
Review evidence and decide whether to proceed.
Pilot intakeOpening after readiness gates