Automated AI-supported Asset Analytics Reports
01/2026 – 03/2026
Concept and implementation of an MCP-server-based pipeline for automated PDF report generation for investors: difficulty updates per epoch, break-even analysis by device class and plan-vs.-reality tracking.
1. Background
Mining operators and investors regularly need up-to-date assessments of profitability, payback periods and investment performance — dependent on volatile parameters such as network hash rate, asset price and mining difficulty. Manually preparing these analyses was time-consuming and error-prone. The goal was an automated pipeline that generates a complete, data-driven PDF report at every difficulty adjustment (approximately every two weeks) and on demand.
2. Stakeholders
3. Challenges
- Volatile input parameters (hash rate, asset price, difficulty) require calculation logic that correctly reflects changes per epoch
- Different device classes (legacy, standard air-cooled, efficient hydro, next-gen) have distinct efficiency profiles and break-even thresholds
- Plan-vs.-reality comparisons require a persistent baseline forecast against which monthly actuals are tracked
- Reports must be generated consistently, correctly and clearly without manual intervention
4. Analysis
The analysis identified two core report types with different requirements. The Epoch Update Report is triggered at every difficulty adjustment and delivers current hash rate figures, historical comparisons (30d/90d/180d), a predictive model for upcoming adjustments and a device-specific break-even analysis. The Plan-vs.-Reality Report compares a one-time baseline forecast (CoP, ROI, revenue) against actual performance over months — including analysis of deviation drivers (hash rate, price, energy costs). For both types, an MCP-server-based approach was chosen: live data is pulled from APIs (hash rate, difficulty, BTC price), processed through the calculation model and output as a structured PDF.
5. Solution Options
6. Evaluation
- ✓Significant time savings: reports are generated automatically at every difficulty adjustment without manual preparation.
- ✓Consistency and traceability: the same calculation logic applies across all device types and time periods, leaving no room for interpretation.
- ✓Plan-vs.-reality tracking creates transparency around actual investment performance relative to the original forecast.
- Report quality depends directly on the reliability of data sources — outages or latency in API feeds affect output immediately.
- Device-specific parameters (hash rate, power consumption, residual value) must be maintained manually when new models are added.
- Inaccurate forecasts under strongly divergent hash rate or price conditions can lead to poor decisions if users do not account for the model's confidence limits.
- Dependency on external data sources without fallback can interrupt automation when APIs change.
Concept and initial setup: approx. 4–6 weeks; ongoing maintenance and extension: continuous
7. Recommendation
Decision
GoPriority
High – immediate value for operators and investors through automated, data-driven decision support
Next Steps
- Secure data sources and define a fallback strategy for API outages.
- Maintain the device catalogue and integrate new models as they enter the market.
- Collect user feedback on report format and clarity and incorporate iteratively.
8. Artefacts
Ready to collaborate?
I am looking for a permanent position as IT Delivery Lead / IT Project Manager.