Consequence-Aware Infrastructure Intelligence for Regulated Operators
Know where asset risk matters most
KartaSoft helps pipeline integrity, damage-prevention, and operations teams determine which segment, corridor, threat, or investigation deserves action first.
Built on Physics-Informed Artificial Intelligence, the platform governs operational baselines, applies consequences, forms connected Risk Islands™, and explains why each priority ranks where it does.
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The risk is often buried in noise
When every signal competes for attention, scarce engineering time, field capacity, and capital can be spread too broadly.
Threshold SCADA Alerts
Excavation Risk
Inefficient Resource & CapEx Allocation
Spatial Risk Blindness
Physics-Informed Operational Intelligence
KartaSoft's capability is built on Physics-Informed Artificial Intelligence (PIAI).
PIAI combines engineering context, network relationships, operating configurations, asset behaviour, historical records, geospatial observations, and current data.
That foundation supports four operational capabilities.
Baseline Governance
Govern what normal means as operations change.
Maintenance, compressor reconfiguration, demand shifts, instrument changes, pump changes, and other transitions alter expected system behaviour.
Baseline Governance controls when and how that expected behaviour changes. It helps teams distinguish an approved operating transition from an unexplained deviation while preserving a reviewable history of the baseline.
Consequence Intelligence
Determine where potential impact matters most.
Signal magnitude alone does not establish priority.
Consequence Intelligence evaluates each condition in the context of the assets, people, environment, service, throughput, and operations exposed.
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Risk Island
Identify where connected risk forms one operational concern.
Individual high-scoring segments can hide the larger pattern.
KartaSoft connects contiguous segments and spatial cells where elevated conditions, related threats, and consequence converge. The result is a defined Risk Island that teams can compare, assign, investigate, mitigate, and track over time.
Explainability
Show why one priority comes before another.
Every score, Risk Island, and ranking should come with an evidence chain.
KartaSoft shows the governed baseline, contributing signals, consequence factors, likely failure mode, confidence, persistence, reason codes, and ranking history behind each priority.
The operational intelligence layer empowering the backbone systems of society
Oil & Gas
With dispersed assets, changing operating conditions and high-consequence exposure, KartaSoft helps operators focus engineering attention on the risks and flow decisions that matter most.
Water & Wastewater
With aging networks, overflow risk and constrained capital, KartaSoft helps utilities prioritize operational response and renewal where service and ratepayer consequences are greatest.
Electric Utilities
With weather volatility, asset stress and rising reliability expectations, KartaSoft helps utilities combine forecasts and infrastructure context to strengthen grid resilience and planning.
Rail
Across long corridors exposed to encroachment and aging assets, KartaSoft helps rail operators focus safety, inspection and renewal resources where operational consequence is highest.
Large Landholdings
Where vast properties make continuous oversight impractical, KartaSoft improves situational awareness of land activity, access change and encroachment between site visits.
Ecosystem/Partners
As customers demand integrated and defensible infrastructure intelligence, KartaSoft helps partners extend existing services with consequence-aware analysis, evidence and decision support.
Technology alone does not solve operational problems
Critical infrastructure rarely presents clean datasets or simple decisions.
Operators work with aging assets, changing operating conditions, incomplete records, weak signals, limited field resources, regulatory obligations, and consequences that vary dramatically from one location to another.
KartaSoft brings together:
Engineering expertise
Domain knowledge
Advanced analytics
Our forward-deployed engineers and subject matter experts work directly with customer teams to understand the system, integrate the relevant evidence, develop and validate the analytical approach, and put useful intelligence into the hands of the people responsible for the decision.
Engineering and science before algorithms
AI grounded in how infrastructure actually behaves
Critical infrastructure produces difficult analytical problems:
→ rare failures
→ changing operating states
→ incomplete histories
→ noisy sensor data
→ interacting assets
KartaSoft uses physics-informed analytical methods that incorporate engineering relationships, network behaviour, consequence, exposure, asset context, and location sensitivity.
Machine learning can then add pattern recognition and scale without asking operators to treat a statistical correlation as an engineering explanation.
Our approach is supported by validation, subject matter expert review, and evidence that can be examined by the people responsible for the infrastructure.
See more context
Align finite resources with greater confidence
See risk in context
Connect weak signals across operations, assets, environment, and history. Physics-informed analysis helps teams understand changing conditions without treating every deviation as equally important.
Focus finite resources
Compare relative risk across assets, tickets, corridors, and operating states. Give engineering, field, and capital-planning teams a clearer basis for deciding where further review may create the most value.
Make decisions that stand up
Use interpretable outputs, stable analytical governance, and traceable context to build a shared evidence base across technical, executive, and oversight teams.
Add context without the operational disruption
1. Connect the available evidence
Bring together the sources relevant to the chosen use case: operational signals, asset records, GIS, tickets, surveys, imagery, work history, field outcomes, and other customer data.
2. Interpret conditions in context
Apply physics-informed models, governed baselines, and domain context to make noisy or incomplete data more useful. Surface confidence levels and data gaps for review.
3. Characterize relative risk and consequence
Compare assets, activities, or operating conditions using a transparent analytical frame aligned with the customer’s network and decision context.
4. Deliver information into the workflow
Provide customer-configured outputs through GIS layers, ranked information queues, Excel files, reports, or other agreed formats. Qualified personnel review the information and decide the response.
Typical Client
“Using KartaSoft’s risk-informed insights, we gained a clearer view of relative leak risk and asset consequence, which helped inform work-order sequencing and capital planning.”
Senior Asset Manager
Large USA Energy Company
Designed to complement the way your team already works
No. KartaSoft’s physics-informed approach is designed for noisy signals, incomplete histories, rare events, and changing operating conditions. The assessment makes confidence levels and data gaps visible.
No. KartaSoft is designed to provide interpretable context, including the factors, reason codes, confidence, and consequence behind an analytical view. Engineering judgment remains central.
Yes. KartaSoft publishes its security and data-governance position and makes supporting materials available through standard vendor review. Keep the wording “SOC 2-aligned controls” unless the documented status changes.
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Start with one decision
Make your next risk decision with more context
Book a confidential briefing to discuss the operating question, network data, and governance conditions your team is working with.