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Two Worlds. One Risk Engine.

Satellite + ground + operational data fused into ranked decisions.

Protect Infrastructure & Supply Chains from Climate Risk.

We unify satellite data, IoT, and operations into a single risk engine to predict failures and secure supply lines.

Book a Demo See How It Works

Renewal priorities · Vulnerability scores · Suitability comparisons · Inspection targeting

Predict Outages Secure Sourcing Audit Compliance

Our approach

Two Worlds. One Risk Intelligence.

From city grids to sourcing regions—one platform for climate-physical risk.

Satellite Imagery
→
IoT & Ground Truth
→
Internal Ops Data
→
Fusion Core
→
Renewal Priorities Sourcing Risk
Infrastructure Dashboard Supply Chain Dashboard
Asset Condition Index
Retrofit Priority
Suitability Analysis

The challenge

Three gaps between data and decisions.

Why traditional monitoring fails to protect modern assets.

EARLY Signal LOST TIME TOO LATE Action

Gap 01: Timing Mismatch

Real-Time Signals. Annual Budgets.

Risk is visible early, but maintenance cycles wait for failure.

Result: Funding arrives after the outage
2024 (SAFE) 2030 (RISK) !

Gap 02: The Blind Spot

Static Plans in a Shifting World.

Your assets were designed for yesterday’s climate. Relying on historical performance data now actively hides emerging suitability risks.

Result: Investing heavily in doomed locations
DATA X BROKEN ? AUDIT?

Gap 03: The Audit Black Hole

Decisions Without Proof.

Siloed data breaks the link between warnings and actions.

Result: Indefensible compliance failures

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01 / 03 Lead Time
Blind spot: Lead time Example output: Early warning
Signals Timeline
EARLY LATE threshold baseline noise weak signal breach failure

Lead time arrives before budgets do

Lead time +21 days
  • Weak signals precede field confirmations
  • Escalation happens after thresholds are crossed

What teams miss

Lead time Asset context Confidence
02 / 03 Suitability Analysis
Blind spot: suitability drift Example output: Scenario shift
Suitability Trend
TODAY SHIFT 2035 Optimal Baseline Critical

Static maps, moving realities

Suitability -18% by 2035
  • Year-on-year drift often goes unmeasured
  • Scenario differences surface too late for change

What teams miss

Trend view Scenario comparison Site ranking
03 / 03 Evidence
Blind spot: evidence Example output: Audit trail
Evidence Graph
SAT IOT OPS FUSION CORE Alert Score

Decisions without defensible evidence

Traceability 3 sources → 1 trail
  • No shared provenance across sources
  • Decisions can’t be defended in audits or budgets

What teams miss

Provenance Traceability Decision trail

How it works

Inputs aligned before decisions are made.

Each layer is standardized, synchronized, and quality-scored inside the fusion engine.

DATA INPUTS

Satellite

Optical, SAR & thermal imagery

Ground Sensors

IoT & weather stations

Operations

SCADA, logs & assets

Fusion Engine
FUSION ENGINE
DECISION OUTPUTS

Risk Score

Real-time alerts

Dashboard

Visual analytics

Audit Trail

Full traceability

Decision Outputs

Outputs delivered in enterprise format.

Shared delivery layer, plus specialized outputs for each track.

Risk Signals & Alerts

Confidence-weighted signals with thresholds aligned to your operations.

Dashboards & APIs

Operational views plus integration into GIS, SCADA, and analytics stacks.

Evidence & Audit Trail

Traceable outputs for planning, budgeting, and reporting reviews.

Utilities & Energy (Intra-urban)
Asset-Level Vulnerability

Heat, drought, flood, and subsidence proxies across critical buildings and networks.

Renewal & Inspection Prioritization

Target maintenance and retrofit budgets to the highest-risk segments.

Producers & Supply Chain
Suitability & Exposure (Over Time)

Track viability shifts across sites and sourcing regions.

Suitability Analysiss

Decision support for sourcing, site selection, and adaptation planning.

Trusted by forward-thinking teams

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From asset registers to sourcing decisions.

Structured outputs for renewal planning, site selection, and climate adaptation—grounded in validated data.

Building-level intelligence

Every building profiled—age, height, materials, roof geometry, and energy class.

Energy demand & potential

Heat demand, insulation status, and PV potential derived from building geometry and orientation.

Renewal prioritization

Actionable recommendations for insulation, heating systems, and renewable integration.

Building Profile ID: #BLD-2026-1847
Last updated: 2h ago

Residential Multi-Family Musterstraße 42, 80331 München

EPC: D
PV Potential: High

Building Attributes

Construction Year 1967
Height 18.4 m
Floors 6
Roof Area 420 m²
Facade Material Concrete
Insulation Partial
Heat Demand 142 kWh/m²
PV Yield Est. 38 MWh/yr
Optimal roof orientation (SW)

Renewal Priority

Facade insulation recommended. Combined with rooftop PV, estimated 65% reduction in primary energy demand.

Move from reactive to planned.

See how evidence-ready risk intelligence fits into your planning and budgeting cycle.

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