How are we using Predictive Analytics to estimate the repair cost of a vehicle damage?

In the commercial vehicle industry, time is literally money. Traditionally, assessing vehicle damage has been a bottleneck characterized by manual inspections, subjective estimations, and lengthy wait times. At Checkturio, we are eliminating this friction by fusing Computer Vision (CV) with robust Predictive Analytics to deliver instant, data-driven cost estimates.

Here is an inside look at the engineering and data science that powers our vehicle damage estimator.

Phase 1: From Pixels to Vehicle Parts

Before our engine can calculate a price tag, we first employ the Computer Vision to assess the vehicle damage. The process begins when a user captures a photo of the damage. Our pipeline then executes two concurrent deep learning tasks:

1. Damage Recognition: The AI automatically detects the type and severity of the damage—such as scratches, dents, or structural tears—and visualizes them as bounding boxes.
2. Vehicle Part Identification: The model maps the damage to a specific component. Our system currently supports granular identification for 134 distinct vehicle parts.

As shown in the example below, when a "Tipper Body" is identified with a "Bent" damage type, the system doesn't just see a shape; it identifies a specific repairable asset.

Phase 2: Data is the Key

In the world of Machine Learning, your model is only as reliable as its training set. We believe that empirical precision is anchored in the quality of the foundation, which is why our cost estimation model isn't based on simulations, but on real-world ground truth.

Our predictive engine is built on commercial vehicle inspection reports performed by an independent, certified organization.

The Characteristics of Our Dataset:

• 59,750 Data Points: Our repair cost model is trained on tens of thousands of historical assessments, allowing the AI to understand the complex nuances of repair costs across various vehicle types and locations.
• Data Pre-Processing: We utilized data transformation, data science and cleaning techniques to prepare it for usage in our system.
• Systematic Labeling: Because our source data comes from certified partners, it is labeled in a highly coherent and systematic way, minimizing "noise" and maximizing the reliability of the output.
• Human Feedback: We consulted domain-knowlegde experts from a repairshop.

Phase 3: Predicting the Price Tag

Once the ML models have identified the part and the damage type, the Predictive Analytics engine takes the lead. 

As seen in our UI, the system provides a logical range (e.g., Repair vs. Replace), giving fleet managers and insurance providers the flexibility to set costs based on data-backed suggestions. This moves the entire workflow from a subjective guess to a reliable financial estimate in seconds.

Scalability for the Modern Fleet

By leveraging this massive, high-quality dataset, we provide a solution that is both grounded in real-world logic and capable of the high-throughput scalability required by modern logistics and insurance providers. We aren't just automating a process; we are providing actionable financial intelligence.

If you’re interested in how Computer Vision + Predictive Analytics can unlock operational efficiency in logistics and fleet management, let’s talk.

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Agnieszka Michalik, Erdem Bulgur, Dmytro Dehtyarov

Copyright by Checktur.io GmbH, 2026

Note on content: with language refinement and visual assets enhanced or generated through Artificial Intelligence to ensure clarity and high-quality presentation.