Data preparation consumes the effort
Teams spend too much time extracting, joining, transforming, and validating data instead of building intelligence.
iCrunch Sense · Extract intelligence
From prepared data to predictive models and pattern discovery.
iCrunch Sense is the AI/ML intelligence layer for supervised and unsupervised learning: prediction, scoring, clustering, segmentation, anomaly detection, recommendation, forecasting, and pattern discovery.
What Sense enables
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Supervised MLPrediction, scoring and forecasting.
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Unsupervised MLClustering, segmentation and patterns.
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Long history6–12+ months or more when required.
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Lower OPEXLess plumbing, more value delivery.
The AI/ML bottleneck
AI/ML fails when the data foundation is weak.
Most AI/ML initiatives spend the majority of effort before modelling even starts: collecting data, joining sources, cleaning values, building historical datasets, engineering features, and making data usable.
History is missing or too expensive
Prediction and anomaly detection need deep history — often 6 to 12+ months, sometimes with hourly granularity.
Models stay disconnected
If models are built on fragmented or delayed data, results are hard to operationalize across business stakeholders.
What is iCrunch Sense
From prepared data to predictive and structural intelligence.
iCrunch Sense turns prepared, historized, denormalized datasets into predictive and structural intelligence — combining supervised learning, unsupervised learning, anomaly detection, recommendations, and pattern discovery.
Supervised learning
- Prediction & scoring — churn risk, customer value, SLA risk, fraud/risk scoring
- Forecasting — capacity, demand, behaviour, incidents, usage and revenue trends
- Recommendations — retention, up-sell, cross-sell and next-best-action
Unsupervised learning
- Clustering — customers, devices, services, locations and behaviours
- Segmentation — actionable segments for marketing, care and operations
- Pattern discovery — recurring behaviours, anomalies and emerging signals
Why iCrunch Data changes the economics
Prepared and historized data removes the AI/ML plumbing burden.
iCrunch Data provides the foundation Sense needs: real-time ingestion, denormalization, long-term RAS storage, resilience, selective storage policies, and ready-to-use datasets.
Denormalized ML-ready datasets
Join technical and non-technical sources once, create ready-to-use datasets, and avoid repeated costly joins.
Long-term RAS history
Store months or years of time-series history cost-effectively, including hourly or finer-grained values when needed.
Dataset-level resilience
Apply retention, resilience, performance and concurrency policies per dataset instead of overbuilding infrastructure.
Result: iCrunch Data + iCrunch Sense reduce the data-preparation burden so AI/ML teams focus on models, patterns and value creation — not SQL plumbing.
AI/ML use cases
Intelligence for business and operational teams.
Churn prevention
Combine behaviour, usage, care, service quality and value history to detect churn risk early.
Up-sell / Cross-sell
Use clustering and recommendations to identify product affinity and customer growth opportunities.
Anomaly detection
Detect deviations from long-term patterns using historized time-series data from RAS.
Customer segmentation
Create structural segments from behaviour, service, value, location, and interaction patterns.
Operational risk scoring
Score assets, services, locations, or customers based on historical performance and risk indicators.
Pattern discovery
Identify hidden patterns teams would not find manually through dashboards or SQL exploration.