NUMA Closure Evidence Assembly

Assembles regulator-ready closure evidence for NUMA compliance submissions by collecting, organizing, and validating artifacts from engineering, environmental, and closure teams.

The Problem

“NUMA closure evidence assembly for regulator-ready compliance submissions”

Organizations face these key challenges:

1

Artifacts are spread across shared drives, email, document management systems, GIS exports, and engineering repositories

2

Evidence requirements must be mapped manually to NUMA checklist items and submission templates

3

Documents contain unstructured text, tables, figures, appendices, and scanned content that are slow to review manually

4

Teams use inconsistent naming, versioning, and metadata conventions

Impact When Solved

Reduce evidence assembly time from weeks to days for standard submissionsIncrease checklist completeness and traceability across engineering, environmental, and closure evidenceLower risk of missing, outdated, or contradictory artifacts in regulator submissionsCreate reusable structured evidence records for future closure updates and audits

The Shift

Before AI~85% Manual

Human Does

  • •Request closure artifacts from engineering, environmental, and closure teams across shared repositories and email
  • •Review reports, drawings, GIS exports, and scanned files to find facts relevant to each NUMA checklist item
  • •Copy evidence details into spreadsheets and submission templates and manually link supporting documents
  • •Chase subject matter experts to resolve missing, outdated, or conflicting artifacts

Automation

    With AI~75% Automated

    Human Does

    • •Set submission scope, confirm checklist interpretation, and decide evidence standards for regulator readiness
    • •Review AI-drafted checklist responses and approve which artifacts and claims will be submitted
    • •Resolve exceptions such as contradictory evidence, missing approvals, or unsupported closure claims

    AI Handles

    • •Collect, OCR, organize, and classify closure artifacts from available document sources by site, domain, and submission section
    • •Extract required facts, metadata, and evidence snippets and map them to NUMA checklist items with citations
    • •Score evidence sufficiency, detect missing, stale, duplicate, or conflicting support, and create remediation queues
    • •Draft regulator-ready checklist responses, assemble submission packages, and maintain traceable evidence lineage and readiness status

    Operating Intelligence

    How it works

    Humans set constraints. AI generates options.

    Humans choose what moves forward.

    Selections improve future generation quality.

    Confidence87%
    ArchetypeGenerate & Evaluate
    Shape6-step branching
    Human gates2
    Autonomy
    50%AI controls 3 of 6 steps

    Who is in control at each step

    Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

    Loop shapebranching

    Step 1

    Define Constraints

    Step 2

    Generate

    Step 3

    Evaluate

    Step 4

    Select & Refine

    Step 5

    Deliver

    Step 6

    Feedback

    AI lead

    Autonomous execution

    2AI
    3AI
    5AI
    gate
    gate

    Human lead

    Approval, override, feedback

    1Human
    4Human
    6↺ Loop
    AI-led step
    Human-controlled step
    Feedback loop
    TL;DR

    Humans define the constraints. AI generates and evaluates options. Humans select what ships. Outcomes train the next generation cycle.

    The Loop

    6 steps

    1 operating angles mapped

    Operational Depth

    Free access to this report