Make Raw Industrial Data Ready for DataOps & AI
T.AI helps industrial companies convert messy, inconsistent, and unstructured plant data into clean, contextualized, platform-ready industrial intelligence.
Industrial Context Engine
Create trusted relationships between assets, historian tags, engineering systems, process boundaries, and operational intelligence.
Source Systems
T.AI Layer
Industrial Data Platforms Need Clean Context
Most industrial source systems remain fragmented, inconsistent, disconnected, and structurally chaotic.
SAP Master Data Problems
SAP master data is incomplete, duplicated, inconsistent, and poorly structured.
Disconnected Hierarchies
Asset, tag, process, and document relationships remain disconnected.
P&ID Intelligence Locked Away
Valuable engineering context remains trapped inside PDFs and drawings.
The Solution
No platform replacement.
No lock-in.
No over-engineered subscription dependency.
What We Deliver
SAP Master Data Clean-Up
Improve SAP hierarchy quality and maintenance readiness.
P&ID Extraction & Tag Mapping
Extract engineering intelligence from drawings and historians.
Engineering Document Extraction
Convert operational documents into structured plant knowledge.
Business Value
Faster Contextualization
Reduce manual mapping and validation effort.
Better Data Quality
Improve SAP and historian data quality.
Stronger AI Foundation
Create trusted industrial context before AI rollout.
Engagement Model
Data Readiness Assessment
Review SAP, historian, P&ID, and engineering data quality.
Contextualization Pilot
Build validated industrial context models.
Enterprise Rollout
Scale contextualization across multiple plants and sites.
Why T.AI
Frequently Asked Questions
Does T.AI replace Cognite or Databricks?
No. T.AI prepares industrial data before platform ingestion.
Can T.AI work with SAP and AVEVA PI?
Yes. T.AI supports SAP PM/EAM, AVEVA PI, historian systems, P&IDs, and engineering documents.
Before Investing More in AI, Fix the Data Foundation
T.AI helps industrial companies identify source data gaps, clean plant information, create asset-tag-document relationships, and prepare platform-ready datasets for analytics, AI, reliability, and enterprise industrial programs.