Skip to main content

Technical Risks

Technical risks relate to the AI platform architecture, sensor integration, and processing capabilities.

T1: AI Model Accuracy in Field Conditions​

Level: High

Description​

Achieving ≥95% detection accuracy across all 17 use cases in Vaca Muerta's challenging environment presents significant uncertainty:

  • Environmental factors: Dust, extreme temperatures, high winds
  • Lighting variation: Desert glare, night operations
  • Asset diversity: Wide range of equipment types and conditions
  • Novel use cases: Some detections not proven in production elsewhere

Mitigation​

  1. Phased accuracy targets: Progressive improvement across phases
  2. Rail yard baseline: Transfer proven patterns from production deployment
  3. Operator feedback loop: Continuous model refinement
  4. Multi-sensor correlation: Improve accuracy through signal fusion
  5. Conservative claims: Only claim accuracy after validation

Contingency​

  • Extend accuracy optimization phases if needed
  • Prioritize highest-value use cases
  • Implement human-in-loop for low-confidence detections
  • Adjust SLA expectations through contract discussion

T2: Multi-Sensor Data Quality​

Level: Medium

Description​

Multi-sensor correlation depends on consistent, quality data from all sensor types:

  • Sensor calibration: Methane, thermal, LiDAR require regular calibration
  • Data synchronization: Temporal alignment across sensors
  • Metadata completeness: GPS, timestamps, flight parameters
  • Edge cases: Sensor failures, partial data

Mitigation​

  1. Data validation pipeline: Automatic quality checks
  2. Calibration protocols: Regular sensor verification
  3. Graceful degradation: System operates with partial data
  4. Quality dashboards: Visibility into data health

Contingency​

  • Single-sensor fallback for low-quality scenarios
  • Manual review queues for quality issues
  • Sensor replacement protocols

T3: 20-Minute SLA Achievement​

Level: Medium

Description​

Analytics availability within 20 minutes of data upload requires optimized processing pipeline:

  • Current baseline: ~30 minutes in rail yard deployment
  • Additional processing: Multi-sensor correlation adds latency
  • Scale factors: Higher data volumes than current experience

Mitigation​

  1. Architecture optimization: Parallel processing, micro-batching
  2. Infrastructure scaling: Sufficient compute resources
  3. Priority queuing: High-urgency events processed first
  4. SLA monitoring: Real-time latency tracking

Contingency​

  • Tiered SLA by event severity
  • Additional infrastructure investment
  • SLA renegotiation with justification

T4: GPR Interpretation Complexity​

Level: High

Description​

Ground Penetrating Radar (GPR) data interpretation for underground interference detection is technically complex:

  • Data interpretation: Requires specialized expertise
  • Ground truth: Validation challenging
  • False positives: Subsurface anomalies often benign
  • Limited AI training data: Few labeled GPR datasets

Mitigation​

  1. Deferred priority: GPR as Phase 4+ capability
  2. Assisted interpretation: AI highlights anomalies for expert review
  3. Training data collection: Build labeled dataset during operations
  4. Expert partnership: Engage GPR interpretation specialists

Contingency​

  • Visualization-only approach (no autonomous detection)
  • External service for GPR interpretation
  • Scope reduction if technically infeasible

T5: Edge Processing Latency​

Level: Medium

Description​

Some scenarios may require edge processing for immediate response:

  • Emergency alerts: Faster than cloud round-trip
  • Connectivity gaps: Processing during network outages
  • Data volume: Reduce upload bandwidth

Mitigation​

  1. Cloud-first design: Edge processing as enhancement
  2. Selective edge: Only time-critical processing at edge
  3. Architecture flexibility: Support edge deployment if needed

Contingency​

  • Cloud-only MVP; edge as post-MVP enhancement
  • Accept slightly higher latency for remote sites