Agri, Food & Grains
Harvest to Market Logistics

Transform grain spoilage concerns and cold chain inefficiencies into intelligent, automated agricultural logistics operations

Pain Points

Agricultural logistics operations face critical challenges that directly impact margins and food quality throughout the supply chain

Grain Spoilage

Poor storage conditions cause significant grain losses through mold, pests, and quality deterioration, impacting farmer profits and food security.

Cold Chain Breakdowns

Refrigeration failures disrupt the cold chain, causing rapid quality losses and spoilage in temperature-sensitive shipments.

Misaligned Dispatch Planning

Poorly coordinated dispatch schedules increase delays, inflate operational costs, and erode overall profit margins.

Unpredictable Weather Disruptions

Unpredictable weather affecting transportation schedules

Manual Reconciliation Delays

Manual documentation processes slow down reconciliation, increasing errors, operational bottlenecks, and staff workload.

Delivery Status Blindness

Lack of real-time visibility in delivery status causes uncertainty, missed deadlines, and erodes customer trust.

Theft & Pilferage During Transit

Goods are stolen or tampered with while in transit, causing inventory loss, delays, and higher costs.

Inefficient Return Load Planning & Execution

Poor planning for return trips leaves trucks running empty, wasting fuel, time, and money.

AI Driven Solutions

Transform your agricultural logistics with intelligent automation that monitors, predicts, and optimizes every step from harvest to market

JARVIS + IOT

Integrated harvest mapping and dynamic truck allocation.

Smart Sensors

Live humidity, temperature, and spoilage pattern alerts.

Linqhaul AI

Learns procurement cycles and optimizes storage-to-dispatch match.

JARVIS

Learns from production trends and dynamically generates dispatch and procurement plans.

Key Differentiators

Integrated Harvest Mapping

AI-powered field mapping with predictive harvest timing optimization

Spoilage Pattern Alerts

Predictive analytics for early detection of quality deterioration

Quality Assurance Automation

Automated quality checks and compliance monitoring systems

Impact Realized

Measurable improvements across critical agricultural logistics operations from harvest to market delivery

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Reduced grain spoilage through intelligent monitoring and alerts

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Accelerated delivery times through optimized routing and planning

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AI-driven predictive models ensure efficient inventory turnover