From telemetry to decisions people act on.
Streaming data pipelines, time-series platforms, anomaly detection, predictive maintenance and digital-twin applications — engineered so insights reach the person or system that can act on them.
Built for the people accountable for the outcome.
IoT Data, AI & Digital Twins is part of PEXIVA’s Connected Operations & IoT practice, which combines all six PEXIVA core capabilities.
Intended buyers
- Chief Data Officer and analytics leaders
- VP Operations and reliability engineering
- Data and ML engineering teams
- Product leaders building connected services
What this solves.
Data collected, nothing decided
Telemetry lands in a store but never reaches a decision, a workflow or a person who can act.
Models that never leave the notebook
Promising anomaly or failure models lack data pipelines, monitoring and integration to run in production.
No context around the signal
A vibration reading is meaningless without the asset, its maintenance history and its operating state.
Twins that are just 3D pictures
Digital-twin initiatives produce visualizations without the live data model that makes them useful.
IoT Data, AI & Digital Twins — what’s included.
- PEXIVA-deliveredPEXIVA practitioners own and perform the work.
- PEXIVA-ledPEXIVA owns architecture, governance and delivery, with specialist contributors.
- Partner-enabledA qualified specialist delivers a defined work package under PEXIVA program governance.
Streaming data pipelines
Ingest, validate, enrich and route events with defined latency and quality.
Time-series data platforms
Hot, warm and cold storage designed for query patterns and retention cost.
Operational analytics
KPIs such as availability, energy intensity and dwell time, computed consistently.
Anomaly detection
Statistical and machine-learning methods tuned to reduce false alarms.
Predictive maintenance
Failure-risk and remaining-useful-life approaches where data quality supports them.
Computer-vision integration where appropriate
Integrating camera-based inspection or detection outputs into operational workflows.
Digital-twin applications
A live, contextual model of assets, spaces or processes that applications can query.
AI-assisted operational insight
Operational copilots that answer questions over telemetry, manuals and work history.
Dashboards and decision support
Role-specific views for operators, engineers and executives.
What you receive.
- Data-readiness assessment
- Streaming and time-series architecture
- Asset and event data model
- Feature and training pipelines
- Deployed models with monitoring
- Digital-twin data model and APIs
- Dashboards and alert definitions
Evidence before models
Readiness
Check data volume, quality, labels and history before promising a model.
Context
Join telemetry with asset, maintenance and production data.
Model
Start with simple, explainable baselines; add complexity only where it pays.
Operationalize
Deploy, monitor drift and connect outputs to workflows.
Where this is typically applied.
Illustrative use cases — not descriptions of specific client deployments.
Condition-based maintenance
Alerting on abnormal vibration, temperature or current before failure.
Energy-intensity analytics
Energy per unit produced, per room night or per square foot.
Process digital twin
A live model of a line or facility for what-if analysis and monitoring.
Operational copilot
Natural-language questions over telemetry, procedures and maintenance history.
Designed in, not bolted on.
- Data classification and access control on telemetry and derived data.
- Lineage from device to dashboard for auditability.
- Model inputs and outputs logged for review.
- Human review for any automated action with safety or financial consequence.
Engagement models
Common ways to start or continue this work:
- Fixed-scope PoC
- Production Implementation
- Managed IoT Operations
IoT Data, AI & Digital Twins — frequently asked questions
Do we have enough data for predictive maintenance?
What do you mean by a digital twin?
Can AI take automated action on equipment?
Which data platforms do you use?
Turn sensor data into decisions.
Book an IoT Opportunity Assessment to test data readiness and identify the first analytics or AI use case worth building.