Built for your operation, not a template
PlantIntel doesn't apply a one-size-fits-all checklist. It learns the structure of your specific operation. When you upload, the system detects your entities, identifies your grain, and validates against what your data actually contains.
Not what a textbook says it should contain.
We understand your environment
Different manufacturing processes produce different data structures. PlantIntel recognizes the patterns specific to your production type and validates accordingly.
Batch Process
Pharma, food, chemical
Signals we detect
Lot traceability, batch yield, recipe conformance, expiry and hold logic.
Key validation
Lot-to-batch rollup integrity, recipe step completeness.
Discrete Manufacturing
Automotive, electronics, assembly
Signals we detect
Work order flow, station-level cycle times, BOM consumption, routing steps.
Key validation
Routing completeness, component traceability across stations.
Continuous Process
Paper, steel, refining
Signals we detect
Run-rate metrics, downtime categorization, quality sampling intervals.
Key validation
Time-series continuity, measurement drift across sensors.
Mixed-Mode / Hybrid
Multiple production types per site
Signals we detect
Cross-process entity overlap, shared resource allocation, mixed granularity.
Key validation
Entity consistency across process types, unified reporting grain.
Every audit makes the next one sharper
PlantIntel tracks structural patterns across uploads and across similar manufacturing environments. When a batch processor uploads OEE data with the same entity structure as other batch environments, the system draws on that context to improve detection confidence.
It is not a static rule engine. It adapts to your plant, your variables, and your industry.
Entity recognition
7 types
Equipment, product, operator, location, material, process, temporal
Pattern matching
Cross-environment
Similar structures across plants improve signal quality automatically
Validation context
Domain-aware
Understands manufacturing ontology, not just data types
What we validate
28 structural checks grouped into six categories. Every check runs against your actual data, not a predefined schema.
Structural Integrity
Key uniqueness, duplication, grain consistency, column completeness.
Entity Recognition
Detects 7 entity types: equipment, product, operator, location, material, process, and temporal.
Time Coverage
Gaps, overlaps, boundary alignment, frequency consistency.
Rollup Logic
Whether aggregations hold, parent-child relationships are intact, and hierarchies resolve.
Drift Detection
Has the structure changed from what your KPIs assume? New values, missing categories, shifted distributions.
Operational Signals
Early warnings worth inspecting: outlier concentrations, variance spikes, coverage anomalies.
See what your data actually contains
Upload a dataset and get a forensic readiness report in under a minute. No templates. No configuration. Just your data, validated.