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AI Analysis

Arlo incorporates Artificial Intelligence to help you understand your data faster. Instead of manually analyzing tables and charts, you can get automatically generated summaries, interpretations, and recommendations.

AI Capabilities in Arlo

Section titled “AI Capabilities in Arlo”

Automatic Summaries (Debriefs)

Section titled “Automatic Summaries (Debriefs)”

After completing a sanitation route, AI generates a summary including:

  • Main findings
  • Values that exceeded thresholds
  • Weather context for the period
  • Comparison with previous routes
  • Action recommendations

Question Analysis

Section titled “Question Analysis”

You can ask questions about your data:

User: "How are pest levels this month compared to last month?"


AI: "Overall pest levels increased 15% compared to December.
    Main changes are:


    • Mites: +35% (from 4.2 to 5.7 average)
    • Thrips: +12% (from 2.1 to 2.4 average)
    • Leaf miner: -8% (from 28% to 26% affected shoots)


    The increase coincides with dry season and higher temperatures.
    Recommend adjusting monitoring frequency and reviewing
    alert thresholds."

Where to Find AI Analysis

Section titled “Where to Find AI Analysis”

In sanitation routes

Section titled “In sanitation routes”

After completing a route:

  1. Go to Sanitation → Route Analysis
  2. Open a completed route
  3. Find the AI Summary or Debrief section

In analytical reports

Section titled “In analytical reports”
  1. Go to Sanitation → Analytical Reports
  2. Select analysis period
  3. Click Generate AI Analysis

Analysis chat (if available)

Section titled “Analysis chat (if available)”
  1. Click on chat or assistant icon
  2. Write your question about the data
  3. Receive response based on your actual data

Types of Analysis

Section titled “Types of Analysis”

Individual route analysis

Section titled “Individual route analysis”

Generated automatically when completing a route.

Period analysis

Section titled “Period analysis”

For time ranges (week, month, quarter):

  • Key indicator trends
  • Lots with recurring problems
  • Effectiveness of previous interventions
  • Projections if patterns continue

Comparative analysis

Section titled “Comparative analysis”

Comparisons between:

  • Lots (which has more problems?)
  • Periods (improving or worsening?)
  • Crops (which requires more attention?)
  • Years (seasonal pattern?)

Limitations

Section titled “Limitations”

What AI does well

Section titled “What AI does well”
  • Summarize large amounts of data
  • Identify patterns and anomalies
  • Compare with historical averages
  • Contextualize with weather data
  • Suggest actions based on best practices

What AI cannot do

Section titled “What AI cannot do”
  • Physically inspect crops
  • Know unrecorded local circumstances
  • Predict the future with certainty
  • Replace expert agronomist judgment
  • Make decisions for you

Next Steps

Section titled “Next Steps”
  1. Data to Action - Convert insights into decisions
  2. Executive Reports - Share analysis with stakeholders
  3. Alert System - Automate problem detection