Mine Planning Document Analysis With Claude Opus 4.7

Table of Contents

1. Why Mine Planning Document Analysis Matters
2. Claude Opus 4.7: The Long Context Advantage
3. Understanding Mine Planning Documents
4. Setting Up Your Document Analysis Workflow
5. Practical Implementation: Step-by-Step
6. Real-World Use Cases for Australian Mining Teams
7. Optimising Analysis for Complex Geological Data
8. Security and Compliance Considerations
9. Measuring ROI and Impact
10. Next Steps and Getting Started

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Why Mine Planning Document Analysis Matters

Mine planning is one of the most document-intensive operations in the resources sector. A single mine development project generates hundreds of technical documents—geological surveys, engineering studies, mine plans, resource estimates, scheduling models, and environmental impact assessments. Each document contains critical information that planners, engineers, and geologists need to synthesise into coherent strategies.

Traditionally, this work has been manual. Teams read through stacks of PDFs, cross-reference data, extract key findings, and compile summaries. A single mine plan review cycle can consume weeks of senior engineering time. For large operations managing multiple sites or development stages, the administrative burden becomes unsustainable.

This is where Claude Opus 4.7 changes the equation. By processing entire documents—or even entire document sets—in a single interaction, the model allows mining teams to extract insights, identify inconsistencies, and validate assumptions at machine speed. The result is faster planning cycles, better decision-making, and significant cost savings.

At PADISO, we’ve worked with Australian mining operations to implement document analysis workflows that cut planning review time by 40–60% whilst improving accuracy. The key is understanding how to structure your documents, what questions to ask, and how to integrate AI analysis into existing planning systems.

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Claude Opus 4.7: The Long Context Advantage

What Makes Opus 4.7 Different

Claude Opus 4.7 is built with a 200,000-token context window—roughly equivalent to 150,000 words or 300 pages of dense technical text. This is critical for mining because it means you can upload an entire mine plan package—geological reports, engineering drawings as text, resource models, and scheduling constraints—all at once, and the model processes them holistically.

Unlike earlier Claude versions, Opus 4.7 maintains coherence and accuracy across this entire span. It doesn’t “forget” information halfway through a long document. It can cross-reference data from page 50 with assumptions stated on page 200. For mining engineering, this is transformative.

According to detailed benchmark analysis of Claude Opus 4.7 capabilities, the model shows marked improvements in agentic workflows and research tasks, particularly for extracting structured data from unstructured sources—exactly what mine planning requires.

Key Capabilities for Mining Applications

Opus 4.7 excels at:

• Information extraction: Pulling resource tonnage, grade estimates, mine life projections, and capital costs from narrative reports without manual data entry.
• Cross-document consistency checking: Identifying when different reports contradict each other (e.g., resource estimates that don’t align across documents).
• Assumption validation: Flagging assumptions embedded in engineering studies that may affect project viability.
• Summarisation at scale: Condensing 50-page engineering feasibility studies into 2-page executive summaries.
• Scenario analysis: Evaluating how changes to one parameter (e.g., ore grade, mining rate) cascade through the plan.

Research on AI integration in mine planning software confirms that LLM-based document analysis delivers tangible efficiency gains when applied to resource estimation and scheduling tasks.

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Understanding Mine Planning Documents

Typical Document Types in a Mine Plan Package

Before you start analysing, you need to understand what you’re working with. A comprehensive mine plan typically includes:

Geological and Resource Documents

• Geological interpretation reports
• Resource estimation reports (JORC Code compliant)
• Geotechnical assessments
• Hydrological and hydrogeological studies

Engineering and Design Documents

• Mine design reports (pit designs, underground layouts)
• Equipment selection and sizing studies
• Process flow diagrams and metallurgical reports
• Infrastructure and utilities plans

Operational and Financial Documents

• Mining schedules and production plans
• Capital expenditure budgets
• Operating cost estimates
• Project timelines and milestones

Regulatory and Environmental Documents

• Environmental impact assessments
• Water management plans
• Rehabilitation and closure strategies
• Stakeholder consultation summaries

Each document type contains different information structures. Geological reports are narrative-heavy with embedded tables and figures. Engineering studies are technical with calculations and assumptions listed throughout. Financial summaries are tabular. Opus 4.7 handles all of these formats effectively, but understanding the document type helps you craft better analysis prompts.

Common Analysis Challenges

Mining documents present specific analytical challenges:

• Embedded assumptions: Key assumptions (e.g., commodity prices, mining rates, recovery rates) are scattered throughout documents, not centralised.
• Cross-document dependencies: A mine plan’s viability depends on assumptions from the resource estimate, which depends on geological interpretation. Breaking one link breaks the chain.
• Inconsistent terminology: Different authors use different terms for the same concept (e.g., “ore reserve” vs. “mineable resource”).
• Figure and diagram interpretation: Mine plans include technical drawings that contain critical information but aren’t easily machine-readable.
• Regulatory compliance: Australian mining requires compliance with JORC Code, environmental regulations, and state-specific requirements. Documents must be reviewed for compliance.

Opus 4.7 can handle most of these challenges, but success depends on how you structure your prompts and documents.

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Setting Up Your Document Analysis Workflow

Document Preparation and Formatting

The quality of your analysis depends on how well you prepare documents. Here’s what works:

Format documents consistently: Convert all PDFs to plain text or markdown. This removes formatting issues and ensures Opus 4.7 processes the actual content, not layout artifacts. Tools like pdftotext or commercial PDF extraction services work well.

Preserve structure: Use clear headings and section markers. If a PDF has a table of contents, keep it. Opus 4.7 uses document structure to navigate and cross-reference information.

Remove redundancy: If a document has multiple copies of the same table (e.g., in appendices), keep only one. This saves tokens and reduces noise.

Annotate source documents: Add metadata at the top of each document—title, date, author, version. This helps Opus 4.7 understand the document’s role and precedence if multiple versions exist.

Include page references: If original PDFs have page numbers, preserve them in the text version. This allows you to verify findings by returning to source documents.

Building Your Prompt Library

Opus 4.7’s effectiveness depends on prompt quality. Build a library of reusable prompts for common mining analysis tasks:

Resource estimation review: “Review the resource estimate in this document. Extract: (1) total tonnage and grade by deposit zone, (2) all assumptions about recovery rates and commodity prices, (3) any inconsistencies with the geological interpretation report provided separately. Flag any assumptions that differ from the mine plan’s baseline assumptions.”

Mine plan feasibility check: “Analyse this mine plan for technical and financial feasibility. Extract: (1) production schedule year-by-year, (2) capital and operating costs, (3) key assumptions (ore grades, mining rates, processing recovery). Compare against the resource estimate and identify any conflicts or risks.”

Assumption reconciliation: “Identify all assumptions in these three documents [resource estimate, engineering study, mine plan]. Create a table showing: assumption, document 1 value, document 2 value, document 3 value. Flag any inconsistencies and explain their impact on project viability.”

Compliance check: “Review this document for JORC Code compliance. Identify: (1) all required disclosure items present, (2) any missing or incomplete sections, (3) any statements that appear to breach JORC guidelines. Provide specific references to JORC Code sections.”

Scenario impact analysis: “Given the mine plan in this document, calculate the impact of the following changes: (1) ore grade 10% lower than estimated, (2) mining rate 15% slower than planned, (3) commodity price 20% lower than budget. Show impact on mine life, capital requirements, and project NPV.”

Store these as templates and adapt them to your specific needs. Over time, you’ll develop a library that your team reuses across projects.

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Practical Implementation: Step-by-Step

Step 1: Prepare Your Documents

Start with your mine plan package. Gather all relevant documents—geological reports, engineering studies, mine schedules, cost estimates, environmental assessments. Convert each to plain text or markdown, preserving structure and page references.

Organise documents logically:

• Tier 1: Core planning documents (resource estimate, mine plan, engineering study)
• Tier 2: Supporting documents (geological interpretation, geotechnical assessment, process engineering)
• Tier 3: Regulatory and financial documents (environmental assessment, cost estimates, compliance checklist)

This hierarchy helps you prioritise which documents to include in initial analyses and which to reference for follow-up questions.

Step 2: Define Your Analysis Objectives

Before you start analysing, define what you need to know. Are you:

• Validating a resource estimate against geological data?
• Reviewing a mine plan for technical feasibility?
• Identifying risks in the development schedule?
• Checking regulatory compliance against JORC Code?
• Assessing financial viability against commodity price assumptions?

Each objective requires different prompts and document combinations. Being clear upfront saves time and ensures you ask Opus 4.7 the right questions.

Step 3: Start With a High-Level Summary

Begin by asking Opus 4.7 to summarise the entire plan package. This gives you a baseline understanding and helps identify gaps or inconsistencies early.

Example prompt: “You are reviewing a mine plan package for an Australian gold mining project. The package includes: geological interpretation, resource estimate, mine design, engineering study, production schedule, and financial model. Provide a 500-word executive summary covering: (1) project overview, (2) key assumptions, (3) mine life and production, (4) capital and operating costs, (5) major risks identified in the documents.”

This creates a reference document that your team can use to brief stakeholders and identify which areas need deeper analysis.

Step 4: Extract Key Data

Next, extract structured data from the documents. This is where Opus 4.7’s ability to process long documents really shines. Instead of manually copying numbers from tables, ask the model to extract and tabulate them.

Example prompt: “Extract the following data from the resource estimate document and format as a CSV table: deposit zone, tonnes (million), grade (g/t Au), contained gold (Moz), confidence level (Measured/Indicated/Inferred). Include all zones listed in the document.”

Opus 4.7 will return clean, structured data that you can import directly into spreadsheets or planning software. This eliminates manual data entry errors and saves hours of work.

Step 5: Cross-Reference and Validate

Now use Opus 4.7’s ability to process multiple documents simultaneously to cross-reference data and validate consistency.

Example prompt: “Compare the resource tonnage and grade by zone in the resource estimate document against the mine plan document. Create a table showing: zone, resource estimate (tonnes, grade), mine plan (tonnes, grade), variance %. Highlight any zones where the mine plan differs from the resource estimate by more than 5%.”

This identifies inconsistencies that require investigation. Perhaps the mine plan uses a different resource model, or there’s an error in data transfer. Either way, you’ve flagged it for review.

Step 6: Analyse Assumptions and Sensitivities

Mine plans depend on assumptions about ore grades, mining rates, commodity prices, and other variables. Opus 4.7 can extract these assumptions and help you understand their impact.

Example prompt: “Identify all assumptions in this mine plan document related to: (1) ore grade by zone, (2) mining rate (tonnes per year), (3) processing recovery rate, (4) commodity prices, (5) discount rate for NPV. For each assumption, state the value, state where in the document it appears, and note whether it aligns with the resource estimate and engineering study documents provided separately.”

Once you have the assumptions listed, you can model sensitivities. For example, “If ore grade is 10% lower than assumed, what is the impact on mine life and project NPV?” Opus 4.7 can work through these calculations based on the data in the documents.

Step 7: Generate Reports and Recommendations

Finally, ask Opus 4.7 to synthesise its analysis into actionable recommendations.

Example prompt: “Based on your analysis of the resource estimate, mine plan, and engineering study, provide: (1) a summary of key findings, (2) a list of risks ranked by severity, (3) specific recommendations for plan refinement, (4) questions that require further investigation by the project team. Format as a formal report suitable for presentation to the project steering committee.”

This transforms raw analysis into decision-ready intelligence that your team can act on immediately.

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Real-World Use Cases for Australian Mining Teams

Case Study 1: Gold Project Feasibility Review

A Sydney-based gold developer needed to review a 50-page feasibility study for a greenfield project in Western Australia. The study included geological interpretation, resource estimate, mine design, and financial model. The project team needed to validate the assumptions and identify risks before presenting to investors.

Using Opus 4.7, the team:

1. Uploaded the feasibility study, geological report, and resource estimate (combined ~80 pages).
2. Asked the model to extract all key assumptions and compare them across documents.
3. Identified that the mine plan assumed ore grades 8% higher than the resource estimate’s Indicated Resource category—a material discrepancy.
4. Asked Opus 4.7 to model the impact: using conservative grades reduced project NPV by $45 million but extended mine life by 2 years.
5. Generated a risk report highlighting this finding and recommending the mine plan be revised to reflect Indicated Resource grades.

Result: The team caught a critical assumption error before investor presentations, avoiding credibility damage and enabling a more defensible plan. Analysis time: 4 hours (vs. 2–3 weeks of manual review).

Case Study 2: Multi-Site Operational Planning

A major Australian mining operator managing three operating mines needed to consolidate operational plans and identify optimisation opportunities. Each site had its own production schedule, cost structure, and assumptions about commodity prices and mining rates. Corporate planning needed a unified view.

Using Opus 4.7, the team:

1. Uploaded production schedules and cost estimates from all three sites.
2. Asked the model to extract production by year, operating costs, capital requirements, and key assumptions for each site.
3. Created a consolidated spreadsheet showing all three sites side-by-side.
4. Asked Opus 4.7 to identify inconsistencies in assumptions (e.g., one site using $1,800/oz gold price, another using $1,950/oz).
5. Requested scenario analysis: “If we prioritise Site A for capital investment and defer Site C by 2 years, what is the impact on consolidated production and costs?”

Result: The team identified $8 million in annual cost savings by standardising assumptions and optimising capital allocation across sites. Analysis time: 1 week (vs. 4–6 weeks of manual consolidation).

Case Study 3: Regulatory Compliance Review

A mining company preparing to release a resource estimate needed to ensure JORC Code compliance before publication. The resource estimate was 40 pages with technical data, assumptions, and methodology descriptions scattered throughout.

Using Opus 4.7, the team:

1. Uploaded the resource estimate document and a JORC Code checklist.
2. Asked the model to review the document for compliance with each JORC requirement.
3. Identified missing sections: (a) no explicit statement of Competent Person qualifications, (b) no discussion of data quality or confidence intervals, (c) insufficient detail on grade estimation methodology.
4. Generated a compliance report with specific recommendations for document revisions.
5. After revision, re-ran the analysis to confirm all JORC requirements were met.

Result: The document passed regulatory review on first submission, avoiding delays and potential reputational damage. Compliance review time: 6 hours (vs. 2–3 weeks of manual review by compliance specialists).

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Optimising Analysis for Complex Geological Data

Handling Uncertainty and Confidence Intervals

Mining documents frequently discuss uncertainty—measured vs. indicated vs. inferred resources, confidence intervals on grade estimates, ranges on commodity prices. Opus 4.7 can extract and analyse this uncertainty, but you need to prompt it carefully.

Effective prompt: “Extract all statements about uncertainty, confidence, or risk in this resource estimate. For each statement, identify: (1) the parameter (e.g., ore grade, tonnage), (2) the confidence level (Measured/Indicated/Inferred), (3) the range or interval, (4) the basis for the confidence assessment. Format as a table.”

This helps your team understand not just the central estimates but the full range of possible outcomes. When combined with financial modelling, uncertainty analysis reveals which assumptions have the biggest impact on project viability.

Integrating Spatial and Geological Data

Mine plans are inherently spatial—ore bodies have geometry, mining sequences follow spatial logic, infrastructure is positioned in space. Whilst Opus 4.7 can’t visualise maps or 3D models, it can extract and analyse spatial information from written descriptions.

Example prompt: “Describe the geometry and spatial distribution of mineralisation in this geological report. For each mineralised zone, extract: (1) location (coordinates or relative position), (2) depth range, (3) strike and dip, (4) thickness, (5) ore grade, (6) tonnage. How does this spatial arrangement affect the mine design and production schedule?”

This transforms qualitative geological descriptions into structured data that informs mining sequence and infrastructure planning.

Handling Multiple Deposit Types or Geometries

Complex deposits often have multiple ore types or unusual geometries. Opus 4.7 can track these complexities and help you understand their implications.

Example prompt: “This deposit contains three ore types: oxide, transitional, and primary sulphide. Extract for each ore type: (1) tonnage and grade, (2) mining method (open pit vs. underground), (3) processing route, (4) recovery rate. How do differences in ore type affect mine design, production scheduling, and cost structure?”

By synthesising information about multiple ore types, Opus 4.7 helps you understand the full complexity of the deposit and identify optimisation opportunities.

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Security and Compliance Considerations

Protecting Confidential Information

Mine plans contain commercially sensitive information—resource estimates, cost structures, financial assumptions. When using Opus 4.7 (or any AI service), you need to ensure confidentiality.

Best practices:

• Redact sensitive data: Remove specific tonnage figures, grades, or costs if you’re concerned about disclosure. Opus 4.7 can still analyse the plan using relative comparisons (e.g., “Zone A has 15% higher grade than Zone B”).
• Use anonymisation: Replace actual site names with codes (Site A, Site B) and remove references to specific locations.
• Limit document scope: Don’t upload entire project packages if only a subset is needed. Upload just the documents required for the specific analysis.
• Review outputs carefully: Before sharing Opus 4.7’s analysis with external parties, review it to ensure no sensitive information has been inadvertently included in summaries or extracts.

If you’re working with PADISO or another AI agency, ensure they have security audit processes and compliance certifications (SOC 2, ISO 27001) in place. At PADISO, we maintain SOC 2 and ISO 27001 compliance to ensure client data is handled securely.

Regulatory and Compliance Alignment

Australian mining is heavily regulated. Key compliance frameworks include:

• JORC Code: Resource reporting standard
• Environmental Protection and Biodiversity Conservation (EPBC) Act: Federal environmental assessment
• State-based mining legislation: Varies by state (e.g., Queensland Mining and Quarrying Safety and Health Act)
• Company-specific standards: Many major operators have internal standards exceeding regulatory minimums

When using Opus 4.7 for compliance review, ensure it’s checking against the right standards. Create prompts that reference specific regulatory requirements.

Example prompt: “Review this mine plan for compliance with Queensland mining regulations and the JORC Code. Specifically check: (1) JORC Code Table 1 items 1–30 for completeness, (2) Queensland Mining and Quarrying Safety and Health Act requirements for mine design and safety systems, (3) Environmental authorisation requirements. List any gaps or non-compliance items.”

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Measuring ROI and Impact

Quantifying Time Savings

The most obvious benefit of Opus 4.7-based analysis is time savings. Track how long document review and analysis would take manually, then compare to AI-assisted time.

Example metrics:

• Manual resource estimate review: 40–60 hours for a senior geologist
• Opus 4.7-assisted review: 6–10 hours (extraction and initial analysis by AI, validation by geologist)
• Time savings: 50–85% reduction

For a team of 5 geologists each reviewing 3 resource estimates annually, this translates to 450–1,500 hours saved—equivalent to 0.2–0.7 FTE. At $150/hour loaded cost, that’s $67,500–$225,000 in annual savings.

Identifying Value from Improved Analysis

Beyond time savings, better analysis drives better decisions:

• Risk identification: Catching assumption inconsistencies before they become problems avoids costly plan revisions.
• Optimisation opportunities: Systematic analysis of multiple plans identifies cross-site optimisation opportunities (e.g., capital reallocation, production sequencing).
• Faster decision-making: Stakeholders get decision-ready analysis faster, accelerating project approvals and capital deployment.

Quantify these benefits:

• Avoided rework: If AI analysis catches an assumption error that would otherwise require a 4-week plan revision, that’s 160 hours of engineering time saved.
• Optimisation value: If analysis identifies $8 million in annual cost savings, that’s $40 million in NPV (at 5-year mine life).
• Accelerated approvals: If AI analysis reduces approval cycle time from 8 weeks to 4 weeks, that accelerates capital deployment and project start-up by a month.

Building Your Business Case

When proposing Opus 4.7-based analysis to your organisation, quantify both time and value benefits:

Conservative case: 50% time savings on document review (200 hours/year), plus catching 1 material assumption error per year (worth $5 million in avoided rework).

Moderate case: 60% time savings on document review (240 hours/year), plus identifying $10 million in annual optimisation opportunities across multiple sites.

Optimistic case: 70% time savings on document review (280 hours/year), plus identifying $20 million in optimisation opportunities, plus accelerating project approvals by 2 months.

At 200–280 hours saved annually, the ROI is typically positive within 3–6 months of implementation.

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Next Steps and Getting Started

Building Your Team and Capabilities

Successful Opus 4.7-based document analysis requires both technical and domain expertise. Your team should include:

• Domain experts (geologists, mining engineers): They understand the documents, validate findings, and translate analysis into decisions.
• AI/prompt engineers: They structure prompts, manage document uploads, and optimise workflows.
• Project managers: They coordinate analysis cycles and ensure findings feed into planning processes.

If you don’t have in-house AI expertise, consider engaging an agency like PADISO. We specialise in agentic AI vs traditional automation and can help you build document analysis workflows tailored to mining. PADISO also provides AI strategy and readiness services to help organisations assess and plan AI adoption.

Implementing Your First Analysis

Start small. Pick one document type and one analysis objective:

1. Select a pilot project: A resource estimate review or mine plan feasibility check.
2. Prepare documents: Convert to text, organise logically, add metadata.
3. Define analysis objectives: Be specific about what you want to know.
4. Craft prompts: Use the examples in this guide as templates.
5. Run analysis: Upload documents to Opus 4.7 and execute prompts.
6. Validate results: Have domain experts review and verify findings.
7. Document learnings: What worked? What didn’t? What would you do differently next time?
8. Iterate and scale: Refine prompts and expand to additional documents and projects.

Integrating With Existing Systems

Opus 4.7 analysis doesn’t exist in isolation. Integrate it with your existing planning and management systems:

• Export to spreadsheets: Use CSV exports from Opus 4.7 analysis to populate planning models and financial forecasts.
• Feed into project management: Use extracted data and timelines to update project schedules and milestone tracking.
• Document management: Store Opus 4.7 analysis reports alongside original documents for audit trail and version control.
• Governance: Establish review and approval workflows so that AI analysis is validated before it informs decisions.

For more sophisticated automation, consider building agentic AI workflows. As covered in our guide to agentic AI + Apache Superset for dashboard querying, you can create autonomous agents that continuously monitor planning documents, flag changes, and generate alerts when key assumptions shift.

Expanding to Broader AI Automation

Mine planning document analysis is one application of AI. Once you’ve mastered it, consider broader automation:

• Operational automation: Use AI to automate routine reporting, data entry, and compliance monitoring across your operations.
• Predictive analytics: Combine historical production data with geological models to forecast future performance and identify optimisation opportunities.
• Real-time monitoring: Deploy AI agents to continuously monitor operational data, geological surveys, and market conditions, alerting planners to changes that affect the plan.

PADISO specialises in AI automation for operations and can help you build these capabilities. We’ve worked with Australian operators to implement AI-driven workflows that reduce administrative burden, improve decision-making, and accelerate operations.

Staying Current With AI Capabilities

AI models evolve rapidly. Opus 4.7 is the current state-of-the-art, but newer versions will emerge. Stay informed:

• Monitor Anthropic releases: Anthropic regularly publishes updates on Claude capabilities and performance improvements.
• Experiment with new features: When new versions launch, test them on your existing analysis workflows to see if they improve results.
• Join communities: Participate in mining technology forums and AI/ML communities to learn from peers implementing similar solutions.
• Partner with agencies: Working with an AI agency like PADISO gives you access to expertise and early insights into new capabilities as they emerge.

Research on Claude Opus 4.7 performance and benchmarks shows the model is particularly strong on complex document analysis tasks, with improvements expected in future releases. Staying current ensures you’re leveraging the latest capabilities.

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Conclusion: The Future of Mining Planning

Mine planning document analysis with Claude Opus 4.7 represents a significant shift in how Australian mining teams work. Instead of spending weeks manually reviewing documents, teams can now process entire plan packages in days, with better accuracy and fewer missed insights.

The long context window is the key innovation. By processing 150+ pages of technical documents simultaneously, Opus 4.7 enables cross-document analysis and assumption validation that would be impractical to do manually. This translates directly to better decisions, faster approvals, and improved project outcomes.

The ROI is compelling: 50–70% time savings on document review, plus value creation through better risk identification and optimisation. For a typical mining operation, this amounts to hundreds of thousands of dollars in annual benefits.

Starting is straightforward. Pick one document type, define your analysis objectives, prepare your documents, and craft prompts using the examples in this guide. Validate results with domain experts, learn from the process, and iterate. Within a few cycles, your team will have built a repeatable workflow that becomes part of standard planning practice.

If you need help building these capabilities, PADISO can partner with you. We’ve worked with Australian mining operations to implement AI automation workflows across planning, operations, and compliance. We also provide fractional CTO and platform engineering services to help organisations build and scale AI capabilities sustainably.

The future of mining planning is AI-assisted. The question isn’t whether to adopt these tools, but how quickly you can implement them and start capturing the value.