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Engineering Document Digitization for
Heavy Industry

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AI assisted, Engineer-validated. Built for industrial AI & digital twins.

The Seven Peaks Document Digitization Accelerator is a delivery accelerator built on the Seven Peaks Product Accelerator and integrated directly into Snowflake Cortex AI. It converts unstructured engineering documents into trusted, structured industrial data, establishing Snowflake as the governed data foundation for industrial AI platforms and digital twins.

This accelerator is designed for production use in asset-intensive environments, where data quality, traceability, and governance are non-negotiable.

Workshops

Discovery and workshops

Conducting research to support requirements and design decisions

Define

Define objectives

Setting clear deliverables based on providing value for customers

Information Architecture

Information architecture

Defining user journeys, structuring and sorting content for optimal UX

Wireframe

Wireframing

Turning low fidelity concepts into production ready interface

Rapid Prototyping

Prototypes

Interactive website and mobile application demos

Design System

Design guidelines

A solid understanding of web, Android and iOS guidelines

User Testing

User testing

Validate design with real users to gain insight into performance

Back Development

Developer guidance

Design is peer reviewed by developers for feasibility and efficient handoff

Capgemini

For nearly 60 years, Capgemini has been a leading strategic partner in business transformation, delivering end-to-end solutions that leverage the full value of technology from strategy to operations.

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The Core Challenge with Engineering Documents

Heavy-industry organizations depend on engineering knowledge locked in:

→ Vendor manuals and datasheets

→ Drawings and schematics

→ EPC handover documentation

→ Scanned and legacy PDFs

While these documents contain critical asset intelligence, they are typically:

→ Unstructured and inconsistent

→ Not machine-usable

→ Difficult to govern, audit, and reuse

→ Isolated from modern data platforms

 

As a result:

× Manual extraction dominates engineering workflows

× The same work is repeated across projects

× AI, analytics, and digital twin initiatives stall at the starting line

 

Before advanced initiatives can succeed, engineering documents must become trusted data.

The Seven Peaks Energy Document Digitization Accelerator

Scalable Document Digitization via Controlled Delivery

This offering industrializes document digitization through a controlled delivery model that combines:

AI-assisted extraction using Snowflake Cortex AI Engineer-led human-in-the-loop validation Industrial data structuring aligned to assets and standards A governed Snowflake enterprise data foundation
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Rapid Delivery via Seven Peaks Accelerator

Delivery is orchestrated through the Seven Peaks Document Digitization Accelerator, which provides:

Coordinated AI and human workflows A customer-branded execution back office for audit and intervention Full traceability from source documents to approved datasets
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The accelerator is a repeatable, production-ready delivery framework designed for sustained industrial use.

High-Level Solution & Workflow

The accelerator follows a controlled, engineering-safe workflow designed to scale without compromising accuracy.

1. Document Ingestion

Engineering source material is ingested across formats, including:

  • → PDFs and scanned legacy documents
  • → Engineering drawings and schematics
  • → Vendor datasheets and manuals
  • → EPC handover documentation

Document types and metadata are classified upfront to guide extraction logic.

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2. AI-Assisted Extraction With Snowflake Cortex AI

AI models embedded in Snowflake Cortex AI extract engineering attributes, tags, and metadata using:

  • → Document-type awareness
  • → Priority-based extraction
    (nameplate → GA → datasheet → calculations)
  • → Confidence scoring on every extracted field

This enables scale and speed without treating AI output as authoritative.

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3.  Human-in-the-Loop Engineering Validation & Audit Control 

AI extraction is treated as an assisted step, not an authority.

All extracted values are routed through the Seven Peaks Document Digitization Accelerator, which provides a governed execution back office for audit, manual review, and controlled intervention when AI confidence is low or engineering judgment is required.

Within this environment, discipline engineers from Draga act as the engineering consultancy and validation authority, reviewing extracted values field-by-field before any data is approved for downstream use.

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Engineering Validation & Audit Control

Validation is supported by built-in control capabilities, including:

      → Confidence scoring and automatic flagging of low-confidence or missing values

      → Structured review queues for human validation and reassignment

      → Side-by-side visibility of source documents and extracted values

     → Manual correction and annotation when AI output is incomplete or ambiguous

     → Persistent audit logs, which capture reviewer identity, changes, and approvals

Engineering validation ensures:

      → Correct interpretation of engineering semantics

      → Alignment with standards such as DEXPI, ISO 15926, and CFIHOS

      → Resolution of ambiguous or conflicting attributes

     → Engineering-usable, safety-grade accuracy suitable for operational systems

Only explicitly validated and approved data progresses into governed Snowflake pipelines. Unapproved values remain in the audit queue, preserving traceability rather than failing silently.

This ensures document digitization operates as a controlled industrial process, not an opaque AI automation.

When AI Confidence Is Low

AI extraction is designed to surface uncertainty, not hide it. When extracted values fall below confidence thresholds, are missing, or conflict with engineering context, they are automatically routed into a controlled review queue within the Seven Peaks Document Digitization Accelerator.

Discipline engineers review the original source document alongside extracted fields, apply corrections or annotations where required, and either approve the value or reject it for reprocessing. Every action is logged — including reviewer identity, timestamps, and change history — ensuring full auditability and traceability.

This mechanism ensures that AI accelerates throughput while engineering authority and accountability are preserved. No data enters Snowflake as trusted industrial data unless it has passed this control point.

4. Structuring into Industrial Data Models

Validated information is structured into:

  • → EDMS and MIMS templates
  • → Equipment and tag hierarchies
  • → Engineering master data models

This replaces manual EPC extraction with a governed, repeatable process aligned to industrial systems.

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5. Snowflake as the Industrial Data Foundation

Structured, validated engineering data is loaded into Snowflake as the single source of truth.

  • → Secure, auditable storage
  • → Governance, lineage, and access control
  • → Queryable access for engineering, IT, and analytics teams

Instead of unmanaged document repositories, Snowflake holds trusted industrial data.

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How the Accelerator Feeds Industrial Platforms

Industrial AI platforms and digital twins require clean, contextualized, structured engineering data. This accelerator establishes that foundation.

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Enabling Industrial AI Platforms: Cognite CDF

Platforms such as Cognite CDF rely on consistent asset hierarchies and high-quality engineering metadata.

The accelerator:

  → Provides clean input data for contextualization

  → Reduces onboarding time

  → Lowers remediation effort

  → Increases trust in AI-driven insights

Without this foundation, CDF initiatives stall in data preparation.

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Enabling Digital Twins: Kongsberg Digital Kognitwin

Digital twin platforms such as Kongsberg Digital Kognitwin depend on:

  → Accurate asset structures

  → Reliable equipment attributes

  → Correct engineering context

The accelerator ensures engineering data is digital-twin ready by grounding asset models in validated documentation and preventing twins from becoming visual shells without operational substance.

The Role of the Accelerator

What it enables

  → Faster rollout of industrial AI platforms

  → Safer digital twin initiatives

  → Reduced risk in data-driven operations

  → Long-term reuse of engineering knowledge

What it does not replace

  → Cognite CDF

  → Kongsberg Digital Kognitwin

  → Operational systems or historians

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Who The Accelerator Is For

Oil & gas operators Refineries and petrochemical plants Heavy-industry asset owners EPC-heavy organizations Engineering-led digital transformation programs


Especially where future AI or digital twin initiatives are planned.

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Typical Engagement Path

Why Seven Peaks

Seven Peaks owns: 

→ End-to-end delivery and accountability

→ AI orchestration through the Seven Peaks AI Accelerator

→ Human-governed validation workflows

→ Industrial data architecture and governance

→ Snowflake implementation and integration readiness programs

→ Alignment with Cognite, Kongsberg Digital, and future platforms

We combine engineering discipline with modern data execution.

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Build your data foundation before you scale AI

Discuss how the Seven Peaks Energy Document Digitization Accelerator supports industrial AI and digital twin roadmaps.
Discuss your use case

Our work and insights in energy

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We push industrial boundaries with innovative data and automation solutions. Through client partnerships, we tailor technology to solve unique challenges and unlock powerful new opportunities for your business.

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Information architecture

Organize & structure content efficiently. Proper planning prevents poor performance! Before any visuals are created it’s critical to define the structure and interaction flow of any digital service. 

  • Grouping similar content and features makes a product more accessible, user friendly and efficient to use.
  • We create sitemaps to communicate how users would navigate around a product and visualise hierarchy of screens.
  • Defining steps users take when engaging with a product helps us identify screens and technical requirements early in a project.
  • We organize user flows to match users mental models (how they expect a digital service to behave).