Insights trapped
Different tools and teams produce different truths, slowing decisions and execution.
iCrunch Data · Prepare the truth
Data Management built for real-time analytics, AI, and GenAI.
A high-performance data foundation that unifies ingestion, denormalization, storage policies, and instant analytics — so the business can move from raw data to decisions in seconds.
What it enables
1
Multiple pipelinesSame inputs, many outputs.
2
DenormalizationTime-critical ready datasets.
3
Selective storageDataset-level policies.
4
Instant analyticsSub-second analytics at scale.
The problem with generic stacks
Removing data silos is not enough.
Many platforms remove data silos — but create new analytics and AI silos, where insights stay trapped within specific teams or tooling layers. Meanwhile, high data volumes drive high compute cost and operational complexity.
Compute explosion
Growing volumes and complex joins increase cost and operational burden.
Too much complexity
More moving parts, more pipelines, more fragility — hard to sustain at scale.
What is iCrunch Data
A Data Fabric + Real-Time Analytics Storage foundation.
iCrunch Data is a real-time streaming contextual analytics platform combining Data Fabric and RAS — Real-Time Analytics Storage. It supports end-to-end workflows and extreme performance with a minimal IT footprint.
End-to-end workflows
Ingest → data preparation / denormalization → analytics / visualization → action orchestration.
Cross-correlation
Correlate technical and non-technical data with denormalization for consistent insight.
Lean, extreme performance
Extreme performance with a minimal IT footprint and no heavy dependency stack.
Mandatory capabilities
Three capabilities for high-performance data management.
These capabilities are essential to deliver real-time analytics at scale without runaway cost and complexity.
Multiple pipelines
Create different pipelines from the same inputs to serve different use cases, domains, or ML/AI needs — customers, geography, products, devices, sensors and more.
Real-time denormalization
Avoid costly joins and build time-critical ready datasets by joining sources and metadata, filtering and computing fields in real time at scale.
Selective storage policies
Assign retention, resilience, performance and concurrency rules per dataset to build a cost-effective Real-Time Analytics Storage layer.