# AI insights

##### Snapshot (last 30 days)

Tickets analyzed

### 312

AI processed tickets across all regions.

AI‑suggested responses used

### 58%

Of tickets where AI suggestion was shown.

Avg resolution time gain

### ‑0.7d

Faster for tickets with AI help.

Farmers impacted

### 174

Unique farmers whose tickets had AI suggestions.

##### Narrative insight (sample)

"In the last 30 days, most advisory demand has centered around nutrient management and pest outbreaks in rice and corn. Region 2 continues to generate the highest ticket volume, especially after recent heavy rains.

Tickets where agents used AI‑suggested responses closed, on average, 0.7 days faster than those handled manually. Agent comments indicate that AI suggestions are particularly helpful for structuring calls and SMS replies.

There is growing usage of WhatsApp and SMS channels; scripts optimized for these channels could reduce back‑and‑forth messaging and improve farmer satisfaction."

##### Suggested focus

- Refine scripts for high‑volume rice nutrient issues.
- Create KB articles to cover common pest cases after rain.
- Standardize picture‑taking instructions sent via SMS/WhatsApp.

This is a static narrative placeholder. In a real system, the model would consume ticket data and produce updated insights regularly.

##### Patterns by segment (sample)

Placeholder bullets instead of charts.

- New farmers: ask more basic questions; high KB potential.
- Repeat farmers: more complex, field‑visit‑heavy cases.
- High‑adoption regions: heavier use of WhatsApp/SMS; digital scripts matter.

##### Model feedback ideas

What you might feed back into training pipelines later.

- More labeled examples of region‑specific pests.
- Explicit capture of which suggestions agents accept or modify.
- Farmer satisfaction scores linked to tickets handled with AI support.

Again, this page is UI‑only: no analytics or AI models are running in this prototype.