Strategic collaboration
Paxon, Epik Solutions, and Sequantix
Paxon Energy and Infrastructure’s intelligent field engineering capability is powered by a strategic
collaboration with Epik Solutions and Sequantix combining Paxon’s deep field execution expertise
with best-in-class digital platforms to deliver predictive intelligence for energy and utility operators.
Paxon Energy and Infrastructure
Field execution domain owner
Paxon owns the field execution domain integrity management, construction inspection, vegetation management, gas recompression, and PMO services. Every structured field signal, observation, and measurement that feeds the prediction engine originates from Paxon’s crews and operational workflows.
Epik Solutions
Digital execution platform
Epik Solutions provides the digital execution layer mobile field applications, real-time work tracking, and structured data capture that transforms Paxon’s field operations into high-fidelity, asset-linked digital signals ready for analytics and predictive modeling.
Sequantix
Predictive intelligence and analytics
Sequantix delivers the predictive intelligence and analytics engine risk modeling, anomaly detection, compliance forecasting, and AI-driven decision support that turns Paxon’s field data into actionable predictions operators can trust.
How it works together: Paxon generates the field signals through its service lines. Epik digitizes those signals into structured, asset-linked data. Sequantix transforms that data into predictive intelligence. The result is a closed-loop system where field execution continuously improves through data-driven insights and every prediction is grounded in real operational reality.
The problem
Reactive operations are expensive operations
Most utilities discover problems after they’ve already become failures a leak, an outage, a compliance violation, a failed audit. By then, the cost is exponentially higher: emergency mobilization, regulatory penalties, unplanned capital, and public safety risk.
The data to predict these failures already exists inside your operations in inspection records, ILI runs, field observations, work orders, and condition assessments. The problem isn’t missing data. It’s that the data sits in disconnected systems, unlinked to assets, and invisible to the people making decisions.
Paxon changes the equation:
Every field signal links to an asset. Every asset accumulates a risk history. Every risk history feeds a prediction model. And every prediction surfaces in an operator’s workflow before the failure, not after.
Predictive intelligence in practice
What operators actually see
Paxon doesn’t just score risk it tells operators what’s trending toward failure, why, and
what to do about it.
Integrity management
See corrosion failures 18 months before they happen
Paxon combines ILI run data, soil conditions, pressure cycling history, and historical dig results to compute failure probability for every pipeline segment then surfaces the highest-risk assets in a prioritized remediation queue before a leak or rupture occurs.
- Metal loss growth rate trending
- Pressure cycle fatigue correlation
- HCA proximity risk escalation
- Dig-or-defer recommendations with cost tradeoffs
Vegetation management
Predict encroachment and prevent outages before storm season
Growth rate models, historical trim cycles, terrain slope, and fire-risk zone data converge to forecast which spans will breach clearance thresholds weeks or months before a tree contacts a conductor.
- Species-specific growth forecasting
- Wildfire exposure scoring by corridor
- Clearance breach probability by span
- Storm-season prioritization queues
Inspections and QA/QC
Catch quality issues in the field not in the audit
Automated evidence validation flags incomplete photo sets, missed checkpoints, and anomalous readings the moment they’re captured. QC exceptions surface in real time so rework happens the same day.
- Same-day documentation completeness scoring
- Photo evidence validation and geo/time verification
- Inspector credential and qualification checks
- NCR pattern detection across projects
Compliance and audit
Know which obligations are at risk before the deadline passes
Paxon continuously monitors open work, pending evidence, and approaching regulatory deadlines predicting which compliance obligations are trending toward a miss and quantifying financial exposure.
- Obligation deadline risk forecasting
- Evidence gap detection across programs
- Audit-readiness scoring by regulatory body
- Violation probability with financial exposure modeling
From prediction to prevention
Sense → Analyze → Act → Verify
Prediction alone isn’t enough you need a closed loop that turns early warnings into
preventive action and proves the action was taken.
01
Sense
Field crews capture structured observations, measurements, and evidence linked to physical assets. Every data point becomes a signal that feeds risk models.
Raw signals accumulate
02
Analyze
Risk models compute failure probabilities, detect anomalies, and forecast trends surfacing the assets and programs most likely to cause problems next.
Risks predicted early
03
Act
Prioritized risk queues drive work orders, remediation dispatch, and field routing directing crews to the right assets before failures occur.
Failures prevented
04
Verify
Automated compliance checks and evidence validation confirm the preventive action was completed, documented, and audit-ready closing the loop.
Compliance proven
Every completed loop makes the next prediction more accurate. Field outcomes become training data. Overrides become model corrections. The system gets smarter with every cycle your teams execute.
Why you can trust the predictions
Built for operators, not data scientists
Infrastructure AI has to be trustworthy, explainable, and accountable. These aren’t
features they’re non-negotiable commitments baked into every prediction Paxon makes.
Asset-centric, not document-centric
Every risk score, prediction, and recommendation traces back to a physical asset not a PDF. See the full risk history of any pipeline segment, pole, or corridor in one view.
Tenant-local by default
Predictions and risk models run inside your infrastructure boundary. Your operational data never leaves. Intelligence comes to the data not the other way around.
Compliance by construction
Audit trails, evidence chains, and regulatory mappings are built into every prediction and action. Compliance evidence is generated automatically.
Explainability over black boxes
Every risk score includes an explanation: which signals drove the prediction, confidence level, and what happens if the operator overrides it.
Under the hood
Three-layer intelligence architecture
Predictions are only as good as the data and models behind them. Paxon’s architecture
ensures field signals flow cleanly, models run locally, and intelligence improves across
every deployment.
Field signal layer
Proprietary signal generation
Every field interaction generates structured, asset-linked signals that feed the prediction engine.
Tenant-local intelligence runtime
Your data stays in your boundary
Risk models, anomaly detection, and predictive analytics run locally within your infrastructure boundary.
Global intelligence layer
Patterns, not raw data
Prediction models improve through governed model registries sharing patterns, never raw customer data.
Data sovereignty
Raw operational data stays inside your boundary. Model packages and policy updates flow through
governed registries. Your predictions get smarter without your data ever leaving.
Deployment architecture
One platform. Three runtime patterns.
The same logical modules deploy across any runtime environment. Only placement
changes, never the product.
Shared platform layer identical across all three modes
On-prem only
Utilities, regulated infrastructure, air-gapped environments
All services, queues, object storage, MQTT, models, and data run inside the customer network.
Example fit
Hybrid controlled
Customers with partial cloud allowance
Operational data, scoring, and workflows stay local; optional cloud for backup, central monitoring, or approved LLM calls.
Example fit
Cloud / managed
SaaS-style pilots, non-regulated deployments
All services deploy in managed environments with strict tenant isolation.
Example fit
Same platform, same APIs, same operator experience only the deployment boundary changes.
AI approach
The right model for the right job
Predicting infrastructure failures isn’t a job for a single AI model. Paxon uses a purpose-
built stack where each layer handles what it does best with human oversight where it
matters most.
Classical ML / CV / time-series
Risk scoring, anomaly detection, encroachment prediction, emissions estimation, failure probability
Most operational prediction lives here labels are clearer, explainability matters, and operators need to trust the output.
Narrow domain models
Image triage, report normalization, evidence completeness, classification
Small, specialized models for repetitive, bounded tasks where high accuracy is non-negotiable.
Foundation LLM
Summaries, QandA, report drafting, copilot interactions
Governed behind RAG and policy checks. The language layer supports operators it doesn’t replace their judgment.
Rules and policy engine
Utility-specific thresholds, compliance mappings, required approvals
Hard rules still matter. Regulatory thresholds and safety gates are enforced, not learned.
Human-in-the-loop review
Override, signoff, escalation, closeout
Every critical decision has a human accountable. Overrides are captured and improve future predictions.
Why this matters for predictions: The moat is not a generic AI model it’s field signals + asset-linked history +
operational workflows + local model execution. That combination creates predictions grounded in your real
operational reality, not generic industry benchmarks.
See Paxon in action
Whether you’re looking to predict pipeline failures, prevent vegetation encroachment, or
prove audit readiness request a demo to see how Paxon turns your operational data
into predictive intelligence.