AI Mandi Intelligence Engine
A real-time APMC Mandi market intelligence platform that analyzes arrival volumes, historical price trends, and nearby mandis to answer price questions.
The Problem
What difficult challenge is this experiment tackling?Farmers frequently sell produce to local middlemen at a steep discount because they lack real-time price transparency across surrounding district mandis.
The Hypothesis
What do I believe autonomous AI & engineering can accomplish?Aggregating open Agmarknet government data into an automated conversational time-series model helps farmers determine optimal selling timing and target mandi locations.
System Architecture & Workflow
End-to-end design, data pipelines, and component interactionBenchmark & Empirical Metrics
Accuracy • Latency • Cost • Token Usage • Human InterventionAutomated daily ingestion of 500+ mandis within 10 minutes of release; queries answered with historical verification in <1.2s.
Security, Privacy & Ethical Guardrails
Risk mitigation, strict boundaries, and sandbox controlsAnti-speculation safeguard: Clearly presents historical facts and price spreads, avoiding speculative price guarantees.
Lessons Learned & Observations
Empirical findings from prototyping and testingMandi data pipeline schema defined; handling missing data on regional market holidays.
Next Experiment Iteration
Upcoming modifications, scaling factors, or new hypothesesIncorporate diesel transport cost calculations to calculate net farmer profit per mandi.