Industrial Data Readiness for Cognite, Databricks, AVEVA, Palantir, Microsoft Fabric & AWS

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.

Request a Data Readiness Assessment View Product Modules

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

Challenge

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.

🏭

Industrial Data

Manufacturing Process Plants in AI

🗄️

SAP PM / EAM

⚙️

P&IDs

📈

AVEVA PI

📄

Engineering Docs

📋

Equipment Datasheets

📑

Line Lists

🎛️

Instrument Indexes

🛠️

Maintenance Docs

🛡️

Reliability Docs

📘

Operating Manuals

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.

SAP

SAP Master Data Clean-up

Validate functional locations, equipment hierarchy, duplicates, equipment attributes, status, criticality, and naming standards.

Hierarchy quality Data scoring Exception list

P&ID

P&ID Extraction and Tag Mapping

Extract equipment, instruments, valves, lines, and control loops; map tags to assets, systems, and process boundaries.

Tag register Process model Asset mapping

DOC

Engineering Document Extraction

Convert manuals, datasheets, line lists, inspection reports, reliability documents, and specifications into searchable knowledge.

Metadata Attributes Asset-doc map

Ontology and Knowledge Graph

Create site-area-unit-system-equipment-tag-document relationships for AI, analytics, digital twins, and platform ingestion.

Ontology Relationships Context model

Optional PlantGPT

Add a role-based AI interface over contextualized plant knowledge for document Q&A, equipment search, and troubleshooting.

Q&A Search Troubleshooting

VAL

SME Validation Workflow

Review exceptions, approve mappings, validate asset relationships, and improve trust before full-scale rollout.

SME review Governance Scale-ready

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.

Faster Platform Adoption

Accelerate Cognite, Databricks, AVEVA, Palantir, Microsoft, AWS, and other data platform programs.

Lower Implementation Risk

Identify source data gaps early and fix them before large-scale data platform rollout.

Reusable Templates

Create reusable data extraction, validation, mapping, and ontology templates that can be scaled across units, sites, and assets.p>

Engagement Model

T.AI engagement can start with a focused assessment or pilot before scaling across plants, systems, and geographies.

4–6 Weeks

Data Readiness Assessment

Review SAP master data
Analyze historian tag structure
Review P&ID samples
Identify data quality gaps
Assess contextualization readiness
Create implementation roadmap

6–8 Weeks

Contextualization Pilot

SAP hierarchy clean-up
P&ID extraction
Tag mapping
Engineering document extraction
Asset-document relationship mapping
Business validation workflow

Phased

Site-Level Scale-Up

Extend templates across units
Standardize mapping rules
Improve data governance
Connect additional systems
Scale validation workflows
Prepare ingestion packages

Multi-Site

Enterprise Rollout

Template standardization
Multi-site SAP harmonization
Ontology governance
Site-by-site rollout
Platform integrations
Enterprise readiness framework

AI Add-On

PlantGPT Add-On

Connect structured knowledge base
Configure AI assistant
Enable document search
Build Q&A workflows
Configure role-based access
Enable troubleshooting support

Differentiate

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

Data standards, if available

Business SMEs for validation

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

Document-to-asset mapping

Knowledge graph starter

Tag-to-equipment mapping

Tag-to-system mapping

Frequently Asked Questions

Does T.AI replace Cognite, Databricks, AVEVA, Palantir, Microsoft, or AWS?

No. T.AI does not replace enterprise data platforms. T.AI prepares the upstream industrial data so those platforms can ingest cleaner, structured, and contextualized data faster.

What is the best first use case?

The best starting point is usually a high-value operating unit where SAP master data, historian tags, P&IDs, and engineering documents need to be connected for a data platform, analytics, reliability, or AI use case.

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 data structures, P&IDs, and engineering documents to create a connected asset and tag context model.

Is PlantGPT required?

No. PlantGPT is optional. The first priority is to create clean, structured, validated, and contextualized plant data. PlantGPT can be added after the data foundation is created.

What does the customer need to provide?

Customers typically provide sample SAP master data, historian tag lists, P&IDs, engineering documents, data standards, target platform requirements, and business SMEs for validation.

What platforms can consume the output?

Outputs can be prepared for Cognite, Databricks, AVEVA CONNECT, Palantir, Microsoft Fabric, Azure Data Lake, AWS industrial data platforms, APM platforms, and enterprise data engineering environments.

How long does a pilot take?

A focused assessment can typically run for 4–6 weeks. A contextualization pilot for one unit or plant area can typically run for 6–8 weeks, depending on data availability, validation needs, and source complexity.