01
SAP Master Data Clean-up
Validate functional locations, equipment hierarchy, duplicates, equipment attributes, status, criticality, and naming standards.
Hierarchy qualityData scoringException list
Industrial Data Readiness Platform
Turn messy plant data into platform-ready industrial context.
T.AI creates the pre-contextualization layer between SAP, P&IDs, historian tags, and engineering documents — so Cognite, Databricks, AVEVA, Palantir, Microsoft Fabric, AWS, and AI programs can move faster.
SAP + P&ID + Historian + Docs
Platform-neutral output
Optional PlantGPT layer
T.AI Data Readiness Console
Live Context Model
Industrial Context Readiness
Automated extraction, validation, mapping, and ontology preparation.
82%
Readiness Score
Contextualization Pipeline
SAP hierarchy quality
76%
Tag-to-asset mapping
68%
Document extraction
84%
P&ID relationship map
72%
Ontology readiness
64%
Asset Relationship Graph
Equipment
SAP FLOC
PI Tags
P&ID
Docs
1,284Tags classified
412Assets mapped
96Data gaps found
Designed for data programs using
Cognite
Databricks
AVEVA CONNECT / PI
Palantir Foundry
Microsoft Fabric
AWS
Azure Data Lake
APM Platforms
The Bottleneck
Your platform is ready. Your plant data usually is not.
Industrial data platform programs often stall because SAP hierarchies, historian tags, P&IDs, and engineering documents are not connected into usable context.
01
Incomplete SAP master data
Duplicated equipment, missing attributes, poor naming, and unreliable functional locations.
02
Unmapped historian tags
Tags are not consistently linked to assets, systems, units, or operating boundaries.
03
Engineering context locked in files
P&IDs, manuals, datasheets, and reports remain unstructured and difficult to search.
04
Weak AI foundation
AI and analytics outputs become unreliable when the underlying industrial context is incomplete.
Product Modules
Everything needed before industrial data enters your platform.
T.AI prepares the upstream industrial data layer: extraction, quality checks, mapping, ontology creation, and platform-ready outputs.
P&ID
02
P&ID Extraction and Tag Mapping
Extract equipment, instruments, valves, lines, and control loops; map tags to assets, systems, and process boundaries.
Tag registerProcess modelAsset mapping
DOC
03
Engineering Document Extraction
Convert manuals, datasheets, line lists, inspection reports, reliability documents, and specifications into searchable knowledge.
MetadataAttributesAsset-doc map
KG
04
Ontology and Knowledge Graph
Create site-area-unit-system-equipment-tag-document relationships for AI, analytics, digital twins, and platform ingestion.
OntologyRelationshipsContext model
AI
05
Optional PlantGPT
Add a role-based AI interface over contextualized plant knowledge for document Q&A, equipment search, and troubleshooting.
Q&ASearchTroubleshooting
VAL
06
SME Validation Workflow
Review exceptions, approve mappings, validate asset relationships, and improve trust before full-scale rollout.
SME reviewGovernanceScale-ready
How It Works
From raw plant data to platform-ready context.
The workflow is built for practical implementation: source review, extraction, validation, contextualization, and export.
1
Ingest
Collect SAP extracts, historian tag lists, P&IDs, line lists, manuals, datasheets, and maintenance records.
2
Extract
Identify equipment, instruments, attributes, tables, documents, tags, and process relationships.
3
Validate
Detect missing data, duplicates, weak hierarchy links, unmapped tags, and inconsistent naming.
4
Contextualize
Create asset-tag-document-process relationships and export platform-ready context models.
Business Value
Accelerate data platform and industrial AI programs.
The output is not a report alone. It is a reusable industrial context foundation for analytics, APM, reliability, operations, AI, and data engineering teams.
↯
Faster Contextualization
Reduce manual cleaning, mapping, and validation before platform ingestion.
✓
Better Data Quality
Improve SAP, historian, engineering, and document quality before it affects AI outputs.
→
Faster Platform Adoption
Accelerate Cognite, Databricks, AVEVA, Palantir, Microsoft, and AWS programs.
AI
Stronger AI Foundation
Build trusted industrial context before copilots, PlantGPT, and decision support apps.
!
Lower Rollout Risk
Find source data gaps early before large-scale platform deployment.
↻
Reusable Templates
Scale extraction, validation, mapping, and ontology patterns across plants and regions.
Platform Fit
Platform-neutral by design.
T.AI does not replace your enterprise data platform. It prepares upstream industrial data so those platforms can create value faster.
Cognite Data Fusion →
Databricks →
AVEVA CONNECT / PI →
Palantir Foundry →
Microsoft Fabric / Azure →
AWS Industrial Data Fabric →
APM / Reliability Platforms →
Industrial AI / Digital Twins →
Engagement Model
Start small. Prove value. Scale across sites.
Use a focused assessment or pilot before scaling across plants, systems, and geographies.
Data Readiness Assessment
4–6 weeksBest for initial discovery across one site, one unit, or selected data sources.
- Data readiness score
- Source data gap report
- Sample extraction output
- Contextualization roadmap
Contextualization Pilot
6–8 weeksBest for one high-value operating unit or plant area.
- SAP hierarchy clean-up
- P&ID extraction and tag mapping
- Document-to-asset mapping
- Base ontology and knowledge graph
Site and Enterprise Scale-Up
Phased programBest for multiple units, full sites, business units, or regions.
- Reusable extraction templates
- Standard mapping rules
- Ontology governance
- Platform ingestion-ready datasets
Differentiation
Not generic ETL. Industrial context engineering.
Generic Approach
T.AI Approach
Moves data from one system to another
Makes industrial data meaningful before ingestion
Focuses on ETL pipelines
Focuses on assets, tags, systems, documents, and process context
Treats documents as files
Extracts engineering knowledge from documents
Cleans SAP data in isolation
Connects SAP to P&IDs, tags, documents, and ontology
Deploys AI on weak data
Builds trusted context before AI
Customer Inputs
What we need to start
Initial assessment or pilot can begin with representative source data samples and SME validation access.
SAP PM/EAM extract
Functional location hierarchy
Equipment master list
Criticality data
Historian tag list
Sample P&IDs
Engineering documents
Target platform requirements
Pilot Outputs
What you get back
Outputs are designed to be reviewable by engineering teams and usable by data/platform teams.
Data quality report
Cleaned SAP sample
Hierarchy corrections
Equipment mapping report
P&ID extraction output
Tag-to-asset mapping
Base ontology / KG
Scale-up roadmap
Before investing more in analytics or AI, make sure your industrial data is ready.
T.AI helps identify source data gaps, clean and structure plant data, create asset-tag-document relationships, and prepare platform-ready data for DataOps, data fabric, and AI programs.
FAQ
Questions buyers will ask before a pilot.
Does T.AI replace Cognite, Databricks, AVEVA, Palantir, Microsoft, or AWS?
No. T.AI prepares upstream industrial data so those platforms can ingest cleaner, structured, and contextualized data faster.
What is the best first use case?
A high-value operating unit where SAP data, historian tags, P&IDs, and engineering documents need to be connected for analytics, reliability, or AI use cases.
Can T.AI work with existing SAP and AVEVA PI data?
Yes. T.AI can work with SAP PM/EAM exports, AVEVA PI tag lists, historian structures, P&IDs, and engineering documents.
Is PlantGPT required?
No. PlantGPT is optional. The first priority is to create clean, structured, validated, and contextualized plant data.
How long does a pilot take?
A focused assessment typically runs 4–6 weeks. A contextualization pilot for one unit or plant area typically runs 6–8 weeks, depending on data availability and validation needs.