SCANNING THE FUTURE HELPING THE PRESENT

New
early-warning technologyfor near-future critical events.

Q1™ combines a geo-distributed network of physical stations with a proprietary multidimensional analytics engine and AI-assisted interpretation to identify early signal patterns before disruptive events unfold. It is designed to give decision-makers advance warning of emerging risks and more time to assess, prepare and act.

The purpose is to warn before an event happens, not only explain it afterwards.

Explore the technology
Illustrative Network
SELECTED ARCHIVED CASENearly 20 hbefore the event
≈ 19 h between reported timestampsMálaga–Madrid · Retrospective case
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At the edge of the detectable

The Q1™
Early-Warning System.

The Q1™ Early-Warning System integrates a geo-distributed network of on-premises physical stations, site-specific baselines, multidimensional analytics, cross-site correlation, AI-assisted interpretation and automated alerting. Its purpose is to flag emerging risks in advance so decision-makers can assess the situation and prepare before disruption occurs.

A dedicated early-warning layer alongside existing safeguards.

Q1™ adds an independent source of signal-based warning context. It does not replace existing measurement, monitoring, alarm, protection or emergency-response systems.

THE Q1™ EARLY-WARNING ARCHITECTURE

Four connected layers.

Reading the near future

We scan
near-future events.

A station is installed at the site, calibrated and observed over a local reference period. Q1™ then assesses new signals against the site-specific baseline through monitoring, detection, analysis, correlation, signalling and alerting.

How it works

THE Q1™ ANALYTICS ENGINE

Recognise emerging risks
before disruption unfolds.

The Q1™ Analytics Engine uses advanced proprietary computational algorithms for entropy analytics and multidimensional pattern analysis, assessing physical signals over time and space. AI-assisted interpretation helps explain the resulting evidence and supports alert prioritisation.

It looks for subtle structural changes, anomalies and regime shifts that may emerge before a major risk or critical event becomes visible.

Each observation is assessed against the station’s own baseline. The engine examines the structure, persistence and recurrence of a change before applying site-specific warning criteria. A local warning does not require matching anomalies elsewhere; cross-site comparisons add supporting context.

SIGNAL STRUCTURE · BASELINE COMPARISON
Local baseline→Structural change→Local warningNetwork comparisons add supporting context.

System Experience

Explore the Q1™ experience.

Explore how a local warning can develop and how wider observations add context. Follow the same site and the same scenario from Local Q1™ to World Q1™.

Scenario signals · schematic site locations
Explore a scenario

Scenario-based sequence with simulated Q1™ observations, not a record of Q1™ forecasting these incidents. Connections show comparisons between signals, not flight paths or a proven physical propagation.

ESCKEYPE Q1™LOCAL VIEW
Local Entropic Field

Site A · Coastal

Signal structure · animated view
SIGNAL RELATIVE TO LOCAL BASELINEScenario scale · not facility telemetry
EarlierLater
Q1™ Analyst

Applications

Built for environments
where early awareness matters.

Q1™ is designed as an additional early-warning layer for environments where emerging risks can affect people, operations and places. Existing safety systems remain in place.

Before the headline

When signals
meet the world.

RETROSPECTIVE CASE · REPORTED SIGNAL TIMELINE

An archived Q1™ signal episode was timestamped nearly 20 hours before the rail accident near Adamuz. This retrospective case examines the period between that reported observation and the event, with the timing and its limitations explained in the case notes.

ARCHIVED CASE CONTEXT

Málaga–Madrid Rail Accident

Near Adamuz, Córdoba, Spain

18 JAN 2026~19:45 CETREPORTED SIGNAL WINDOW
Sanitized archived signal overview aligned with the Málaga–Madrid rail accident near AdamuzSchematic reconstruction of the reported signal context. Signal episode:17 January23:28 UTC. Event:18 January about18:45 UTC. The approximately19-hour interval is retrospective, not a guaranteed forecast horizon.SIGNAL · 17 JAN 23:28 UTCEVENT · 18 JAN ~18:45 UTCNearly 20 hours before the event · retrospective interval
REPORTED SIGNAL EPISODE17 Jan 2026 · 23:28 UTC
PUBLIC EVENT TIME18 Jan 2026 · ~18:45 UTC
OBSERVATION CONTEXTMálaga-area archived record
REPORTED PATTERNElevated / anomalous episodes
VIEWRetrospective comparison
TIME BEFORE EVENT≈ 19 h · nearly 20 h
CASE TIMELINESignal → Event

Retrospective timestamp comparison. The chart reconstructs the reported context; it is not the original waveform or a probability forecast.

Who we are

Deep-tech startup
born in Europe.

ESCKEYPE Quantum is a European-born deep-tech startup developing Q1™, an early-warning technology for emerging risks and near-future critical events.

Our vision is to recognise early signal patterns before disruptive events occur and turn them into warnings that give decision-makers more time to help protect people, communities and critical infrastructure. We are expanding a geo-distributed station network across Europe and beyond.

FRONTIER R&DEntropy analytics and proprietary computational methods for challenging observation problems.
PHYSICAL OBSERVATIONOn-site stations and local baselines keep each warning grounded in its own observation context.
GLOBAL OUTLOOKEuropean roots, with partnerships and applications across regions and sectors worldwide.
Helping protect people and places through earlier awareness.

PILOTS & PARTNERSHIPS

Bring Q1™
into the conversation.

Discuss a site, its risk priorities and where additional time to assess, prepare and act could make a difference.

  • Explore a site-specific pilot.
  • Support the node network or analysis infrastructure.
  • Discuss a technical or strategic partnership.

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