Transparent AI — Evidence Chain Review
“Not a black box. See exactly WHY the AI recommends what it recommends.”
How It Works
Pak Hendra
Regional Director (T3)
Glass-Box AI: Traceable Evidence
Black-box AI is dangerous in industrial settings. When NayaCore recommends an action, it provides a transparent, auditable evidence chain. Every conclusion can be traced back to the exact sensor reading, WhatsApp message, or SOP clause that generated it.
Multi-Source Ingestion
The AI ingests data from disparate sources simultaneously: a weather station (Cakrawala module), a WhatsApp field report (Plantation module), and a power meter (MillOS module).
The Causal Graph (DAG)
NayaCore constructs a Directed Acyclic Graph (DAG) connecting the dots. It maps the temporal and spatial relationships between the rain, the wet fuel, and the mechanical strain on the press.
Root Cause & Recommendation
Instead of just treating the symptom (the struggling press), the AI identifies the root cause (wet fuel) and recommends a systemic fix based on indexed operational procedures.
Human Validation
The operator reviews the exact causal chain. Because the evidence is fully transparent, they can confidently verify the AI's logic and authorize the recommendation via WebAuthn.
📊 Before vs. After
Traditional Operations vs. NayaCore
| Metric | Traditional | NayaCore |
|---|---|---|
| Reasoning Model | Black-box Neural Net | Transparent Causal DAG |
| Data Sources | Siloed sensors only | Sensors + WhatsApp + SOPs |
| Trust Building | Blind faith required | Verifiable evidence chains |