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Turn Engineering Documents into Business Data You Can Trust

The Seven Peaks Document Digitization Accelerator turns unstructured engineering documents into validated, governed industrial data so digital twin, predictive maintenance, and AI programs can move from promise to measurable results. It establishes Snowflake as a trusted data foundation for industrial initiatives where delivery certainty matters.

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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 data is fragmented, inconsistently interpreted, or not trusted across teams. Engineering knowledge lives in documents owned by different vendors, projects, and departments. 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.

 

Common patterns we see:

Delayed programs with unclear recovery paths
Delayed programs with unclear recovery paths
Rising costs driven by manual effort and rework
Rising costs driven by manual effort and rework
Digital twins that fail to support decisions
Digital twins that fail to support decisions
Predictive maintenance results that aren’t trusted
Predictive maintenance results that aren’t trusted
Conflicting asset data across systems
Conflicting asset data across systems
Engineering teams overloaded with rework
Engineering teams overloaded with rework
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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 is designed for organizations that need trusted data to support operational decisions.

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Engineer-Governed by Design

AI accelerates extraction, but engineering teams retain authority. Data only becomes usable once it is reviewed, validated, and approved.

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Fully Auditable from Day One

Every value is traceable back to its source document, supporting internal assurance, regulatory needs, and long-term reuse.

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Built to Enable Business Outcomes

Data is structured around assets and systems so it can reliably support digital twins, predictive maintenance, and industrial AI.

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.

Document Ingestion & Classification

Engineering documents from vendors, EPCs, and internal teams are ingested and classified upfront. This establishes clarity on document type, asset scope, and validation requirements before extraction begins.

  • 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

AI models, embedded via Snowflake Cortex AI, extract engineering attributes using document context and confidence scoring. AI accelerates scale—but never operates unchecked.

  • Context-aware extraction logic
  • Attribute prioritization
  • Field-level confidence scoring
  • AI used as an assisted step
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Engineering Validation & Audit Control

All extracted data passes through a governed validation workflow. Discipline engineers review, correct, and explicitly approve values before they are released.

  • Mandatory human validation
  • Discipline-specific engineering review
  • Full audit and approval logging
  • No silent acceptance of AI output
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Structuring into Industrial Data Models

Validated information is structured into engineering-ready data models aligned to assets, equipment hierarchies, and standards such as DEXPI, ISO 15926, and CFIHOS so it is usable across systems and lifecycle stages.

  • Asset-aligned data structures
  • Import directly into EDMS
  • Replacement of manual EPC extraction
  • Reduced rework across projects
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Snowflake as the Governed Industrial Data Foundation

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

  • Governed data pipelines
  • End-to-end traceability
  • Secure enterprise access
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Feeding Industrial AI Platforms & Digital Twins

Once validated and governed in Snowflake, engineering data is passed to industrial AI and digital twin platforms that depend on trusted asset context to deliver value. The accelerator ensures these platforms receive clean, structured input—so they can focus on insight and decisions, not data remediation.

  • Clean and structured data for industrial platforms
  • Faster onboarding into Cognite CDF and Kognitwin
  • Reduced remediation and rework effort
  • Increased trust in AI-driven insights and digital twins
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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.

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

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.

Accelerating Drilling Operations and Ensuring Safety Through Next-Generation Design
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Accelerating Drilling Operations and Ensuring Safety Through Next-Generation Design

Increased win rate and improved returns with an AI-Driven Quotation Engine

Increased win rate and improved returns with an AI-Driven Quotation Engine

Building a Cloud-Native Data Monitoring System for 200+ Oil Rigs

Building a Cloud-Native Data Monitoring System for 200+ Oil Rigs

See more case studies
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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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