Overview

WheelsEye is a technology-led logistics marketplace that connects shippers with truck drivers across India. Often described as a marketplace built for operators, WheelsEye enables drivers to discover loads, manage trips, and monetize their fleet through a combination of GPS tracking, app subscriptions, and marketplace access.

On the supply side, WheelsEye works with a diverse operator base, classified into:

• MFO: Multiple Fleet Owners
  

• MVO: Multiple Vehicle Owners.
  

• SFO: Single Fleet Owners
  

• SVO: Single Vehicle Owners
  

For this engagement, the focus was on SFO and SVO operators, who form a high-volume but operationally complex segment of the marketplace.

The Challenge

Rate card collection is critical for the WheelsEye marketplace, as route-level pricing directly impacts shipper matching and marketplace margins. However, the existing human-led process faced several operational challenges.

1. Incomplete Rate Sharing

   Drivers typically shared only a single rate, often missing return pricing or vehicle-specific variations, resulting in incomplete rate cards.
   

2. Driver Availability Constraints

   Since most drivers are actively on the road, longer conversations often lead to drop-offs or incomplete responses, making it difficult for agents to capture structured rate information consistently.
   

3. Complex Query Handling for Agents

   Agents had to handle multiple variables such as route pricing, vehicle types, return routes, and custom routes, making knowledge base management during live calls challenging.
   

4. High Seat Costs
   

   The process relied on a dedicated telecalling team, creating higher operational costs and limited scalability.
   

5. Low Rate Card Collection Success
   

   The earlier workflow delivered less than 20% success rate, meaning most calls failed to capture usable rate cards.
   

6. No CAC Visibility
   

   The team lacked a clear benchmark for the cost per rate card collected, making optimization difficult.
   

The Solution

SquadStack deployed a voice-led AI workflow purpose-built for rate card collection from SFO and SVO operators.

What the system was designed to do:

• Engage drivers in natural, conversational Hindi and Hinglish
  

• Collect route-specific, vehicle-specific pricing
  

• Capture both ongoing and return rates
  

• Adapt dynamically when drivers operated on non-standard or custom routes
  

How it worked:

• The AI was trained on a dataset of 13,000 leads, each tagged with:

  • Route
    

  • Vehicle type
    

  • Vehicle number
    

• The system prompted drivers intelligently instead of asking generic pricing questions

• Drivers could specify rates for exact routes or define custom routes when applicable

• Conversations were optimized for brevity without compromising data quality

This approach replaced rigid scripts with structured, outcome-driven conversations.

The Impact

The AI-led workflow delivered a significant improvement in efficiency, cost, and conversion, outperforming the earlier human-led process across key operational metrics.

Key Outcomes

• Lower CAC for Rate Card Collection
  AI enabled a highly cost-efficient rate card collection workflow, achieving significantly lower customer acquisition cost per rate card shared, bringing predictable unit economics to the process.
  

• 87% Higher Rate Card Collection Success
  The success rate of collecting usable rate cards improved dramatically, with conversion increasing by ~87% compared to the earlier process.
  

• 40% Reduction in Average Handling Time
  AI-led conversations reduced the time required to collect pricing information, resulting in a ~40% decrease in average handling time, allowing more drivers to be reached in less time.
  

• High and Consistent Connectivity
  The system maintained ~85% connectivity, ensuring reliable engagement with truck drivers despite their on-road availability constraints.
  

The takeaway is clear: AI didn’t just reduce cost, it made rate collection predictable and scalable.

Looking Ahead

Following the success of the rate card collection deployment, WheelsEye has expanded its engagement with SquadStack to support GPS renewal outreach, another high-volume operator-facing workflow.

Together, these deployments demonstrate how Voice AI can improve conversion, reduce handling time, and create more predictable unit economics across operator engagement workflows.