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Turn Engineering Documents into Business Data You Can Trust

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, with seamless integration into Snowflake Cortex AI. It converts unstructured engineering documents into trusted, structured industrial data, establishing Snowflake as the governed data foundation that feeds industrial AI platforms and digital twins.

This is not a document processing tool. It is a production-ready execution accelerator for asset-intensive industries.



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Production-Ready

Built to operate at scale in active, asset-intensive environments

Engineer-Governed

AI accelerates extraction; engineers approve what the business relies on

Audit-Safe

Full lineage and approval history for compliance and accountability

When Siloed or Untrusted Data Puts Programs at Risk

Most industrial transformation programs don’t stall because of technology. They stall because engineering knowledge is still locked inside vendor manuals, schematics, EPC handovers, and scanned legacy PDFs. These documents contain critical asset intelligence, but they remain unstructured, inconsistently interpreted, and difficult to govern or reuse across teams. When programs try to scale, this fragmentation surfaces as delays, rework, and disagreement about what is correct.

 

The result is slow decisions, rising risk, and stalled value.
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Manual extraction dominates engineering workflows
Manual extraction dominates engineering workflows
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Each project repeats the same documentation effort
Each project repeats the same documentation effort
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Digital twins fail to support operational decisions
Digital twins fail to support operational decisions
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Predictive maintenance insights aren’t trusted
Predictive maintenance insights aren’t trusted
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Conflicting asset data persists across systems
Conflicting asset data persists across systems
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Engineering teams are overloaded with rework
Engineering teams are overloaded with rework

Before advanced solutions are possible, engineering documents must become trusted data.

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The Seven Peaks Document Digitization Accelerator: A Controlled Path from Documents to Data

Providing a governed, production-ready path from fragmented engineering documents to a single, trusted industrial data foundation, the Seven Peaks Document Digitization Accelerator converts unstructured documentation into validated data that can support operational decisions, industrial AI, and digital twins.

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Engineer-Governed by Design

AI accelerates extraction, but engineering teams remain the validation authority. Data only becomes usable once it is reviewed, approved, and aligned to engineering standards.

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Fully Auditable from Day One

Every extracted value is traceable back to its source document, with confidence scoring, review history, and full audit logs to support compliance and accountability.

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Built to Enable Business Outcomes

Validated engineering data is structured around assets and systems, ready to power industrial AI platforms, predictive maintenance, and digital twin initiatives.

This is not a standalone document tool. It is a controlled, repeatable execution accelerator designed for long-term industrial use.

How the Document Digitization Accelerator Works

The Accelerator is a proven delivery framework that ensures only trusted, validated engineering data enters Snowflake—ready for use across the business.

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Document Ingestion & Classification

Engineering source material is ingested, including PDFs, drawings, scanned documents, vendor datasheets and manuals, and EPC deliverables. Document types and metadata are classified upfront to guide extraction logic.

  • Multi-format document ingestion
  • Engineering document classification
  • Asset and system context applied early
  • Structured preparation for AI processing
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AI-Assisted Extraction (Snowflake Cortex AI)

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

  • Document-type awareness
  • Priority-based extraction by document type
  • Confidence scoring on every extracted field

This enables scale and speed — but does not finalize the data.

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

AI extraction is treated as an assisted step, not an authority. All extracted values flow through the Seven Peaks Document Digitization Accelerator, providing a governed back office for audit, manual review, and controlled intervention when confidence is low or engineering judgment is required.

Discipline engineers from Draga act as the validation authority, reviewing AI output field by field before any data is approved for downstream use. Validation is supported by built-in audit and control capabilities, including:

  • Mandatory human validation
  • Discipline-specific engineering review
  • Full audit and approval logging
  • No silent acceptance of AI output
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Validation is supported by built-in audit and control capabilities, including:

  • Confidence scoring and automatic flagging of low-confidence or missing values
  • Structured review queues for human validation and reassignment
  • Full visibility into source documents alongside extracted values
  • Manual correction and annotation when AI output is incomplete or ambiguous
  • Persistent audit logs capturing who reviewed, changed, and approved each value

 

Engineering validation ensures:

  • Correct interpretation of engineering semantics
  • Alignment with standards such as DEXPI, ISO 15926, and CFIHOS
  • Resolution of ambiguous, conflicting, or low-confidence attribute
  • Engineering-usable, safety-grade accuracy suitable for operational systems

 

Controlled Data. Trusted Outcomes.

Only validated and explicitly approved data is allowed to progress into governed Snowflake pipelines. Unapproved or unresolved values are retained in the audit queue, ensuring traceability rather than silent failure.

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

 

Structuring into Industrial Data Models

Only validated data is loaded into Snowflake as the single source of truth. Governance, lineage, and access controls ensure consistent use across teams and structured into:

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

This replaces manual EPC extraction with a governed, repeatable process.

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Snowflake as the Industrial Data Foundation

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

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

Snowflake holds trusted industrial data — not raw documents.

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When AI Confidence Is Low

AI extraction is designed to surface uncertainty, not hide it. 
 

Controlled Review Queue

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. In this state:

  • Data cannot flow downstream
  • AI output is explicitly marked as unapproved
  • Human review becomes mandatory, not optional

 

Discipline Engineer Validation

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.

How This Feeds Future Industrial Platforms

Industrial AI platforms and digital twins do not work with documents.
They require clean, contextualized, structured engineering data. This accelerator creates that prerequisite.
 

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Enabling Industrial AI Platforms — Cognite CDF

Platforms such as Cognite CDF rely on high-quality asset metadata, consistent equipment hierarchies, and contextualized engineering attributes.
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 cleanup.

 

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
  • Asset models are grounded in validated documentation
  • Operational data can be contextualized correctly

This prevents digital twins from becoming visual shells without substance.

 

Seven Peaks is a System Integration partner of Cognite/ Kongsberg

What the Accelerator Delivers

The Document Digitization Accelerator combines AI, engineering governance, and delivery discipline into a repeatable framework designed to reduce risk and increase confidence.

AI + Human Orchestration

AI is used to accelerate throughput, while humans remain accountable for correctness.

Scalable without loss of control
Faster extraction with confidence
No black-box automation

Engineering-Grade Audit

Structured review workflows and audit trails ensure data can be trusted in operational contexts.

Clear accountability
Full traceability
Audit-ready data

Industrial Data Structuring

Data is prepared for reuse across plants, assets, and initiatives—not locked into one project.

Consistent asset views
Reusable models
Standards alignment

Platform Readiness

The foundation required to reliably support digital twins, predictive maintenance, and industrial AI.

Decision-grade data
Cross-platform enablement
Long-term foundation

Trusted for Industrial-Grade Execution

Seven Peaks delivers senior-led, governed execution for complex, high-risk industrial initiatives, where data quality directly impacts safety, cost, and performance. We own end-to-end delivery and accountability, combining AI-assisted extraction with engineer-governed validation workflows to ensure industrial data is trusted before it is used.

400+ projects delivered across asset-intensive and regulated enterprise environments.

Build the data foundation before you scale AI.

Discuss how the Seven Peaks Energy Document Digitization Accelerator can support your industrial AI and digital twin roadmap. Discuss your use case and we will review the high-level architecture.

Contact Us for Free consultancy

A Controlled Path to Scale

Engagements are structured to validate early, reduce risk, and scale only when confidence is established.

1

 

Focused Pilot

Validate document scope, asset coverage, and business-critical attributes.

2

 

Scaled Digitization

Expand across assets and sites using proven, governed workflows.

3

 

Data Foundation

Establish Snowflake as the governed industrial engineering data backbone.

4

 

Platform Enablement

Integrate with EDMS, MIMS, SAP, historians, and SCADA.

5

 

AI & Digital Twins

Enable advanced use cases once data trust is in place.

Delivery at Industrial Scale

Explore examples of complex, large-scale programs delivered in environments where data trust and execution discipline are non-negotiable.

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Accelerating Drilling Operations and Ensuring Safety Through Next-Generation Design

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Start with Data the Business Can Stand Behind

Digital twins and predictive maintenance depend on trusted engineering data. Build the foundation first. Then scale advanced platforms with confidence.

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