AI solutions are everywhere, promising to transform industries with automated insights and intelligent problem-solving. However, many AI models falter when it comes to practical applications like vehicle damage detection. Their limitations often lead to incomplete or inaccurate results, undermining their effectiveness. Worse, some suffer from "hallucinations"—moments when the model generates incorrect or irrelevant outputs that have no basis in reality. These are serious gaps, especially in high-stakes industries like automotive and insurance.
At Click-Ins, we’ve taken a different approach. Our technology is purpose-built for damage detection, backed by various engineering disciplines and a structured knowledge framework (ontology of the vehicle). By combining 3D modeling, photogrammetry, and computer vision, we deliver accurate and reliable damage assessments, overcoming the common limitations of AI. Here's how we stand apart.
Traditional AI solutions are designed to be versatile, but this broadness comes at a cost. They lack the specialization and reasoning usually required for nuanced tasks like vehicle damage assessment. Often trained on vast amounts of diverse data not exactly tailored to specific use cases, these models need contextually relevant information such as ontology to interpret complex visual patterns accurately and explain model outputs.
These “AI hallucinations” are very common for groundbreaking Generative AI tasks such as language and image synthesis. They are biased toward learned patterns rather than facts, which can result in discrepancies when identifying real damage and positioning it on the correct body part. As a result, AI outputs are unpredicted and unreliable, creating challenges for businesses that foster precision.
Click-Ins was designed from the ground up to address these challenges. Our technology is built on three core pillars: Proprietary algorithms and models, Visual Intelligence, and simulated (synthetic) data. These elements comprise a system that consistently delivers accurate and reliable results.
Unlike many AI solutions, our models are not general-purpose - they’re engineered explicitly for the automotive domain and optimized for vehicle damage detection. By focusing solely on this domain, we’ve created a solution that excels in identifying, classifying, and localizing damage across all car models and body styles and operates under various environmental scenarios. This unbiasing ensures every assessment is accurate, actionable, and well-reasoned.
Visual Intelligence is the core of our approach at Click-Ins. Unlike many AI companies focusing solely on predictions, we emphasize a systematic engineering and methodological approach rooted in intelligence agency practices.
By employing Visual Intelligence principles, we incorporate domain-specific knowledge, thus improving the explainability of AI predictions and reducing so-called “AI hallucinations.”
For us, Visual Intelligence (which uses perception, object recognition, scene understanding, visual reasoning, and visual learning) isn’t just about analyzing images to make predictions; it's about gathering intelligence from visual sources to build a complete and accurate picture.
With our technology, we can provide precise damage measurements and classifications, allowing our customers to translate this into accurate estimations later. By addressing the common limitations of traditional AI, we provide a level of detail and reliability that other solutions cannot achieve.
The quality and diversity of the training data directly affect the performance of AI models. Most of the AI models fail not because of the algorithms but due to the sparsity and bias in the training sets that don’t account for real-world variability. Moreover, very few companies own the data they use to train their AI models. Ethical aspects, data privacy, and legal ownership are only a few problems AI companies have to address when training their models. From day one, we used proprietary simulated data to overcome these limitations, ensuring our models achieve the highest level of generalization to handle a wide range of real-world scenarios. For this purpose, we’ve developed a custom 3D/CAD system that automatically generates and annotates synthetic data used to train our AI models.
By simulating diverse conditions, we accelerate problem resolution and ensure our AI remains unbiased and reliable. Our proprietary datasets simulate countless scenes, environmental conditions, and vehicle models. We adopted our algorithms to learn as many features as possible from artificially simulated data and treat it as real. The result is a system that adapts seamlessly to current and future vehicle designs and unexpected adversarial effects such as strong light and object reflections, unique geometry, etc.
One of the biggest headaches in the industry is cybersecurity, particularly adversarial attacks on AI models. Imagine an insurance company with an intelligent robot that helps it settle motor insurance claims. This robot learned how to make good choices by looking at lots of pictures and information. But what if someone figured out a way to show the robot a picture that looks normal to you but makes the robot see something completely different? For example, the robot might see damage on a car that looks like a regular sticker to you. This could make the robot make bad decisions, such as paying bogus insurance claims or letting the wrong people into places they shouldn't be. These tricks are called "adversarial attacks," and they can fool AI systems that we rely on for security and other important tasks. It's like giving the robot glasses that make it see things wrong, even though everything looks normal to us.
To protect against such attacks, we initially train our AI models on adversarial patterns artificially simulated by our proprietary synthetic data engine. The use of synthetic data plays an integral part in Click-Ins’ preparedness strategy.
In a world filled with one-size-fits-all solutions, specialization is key. Various AI and GenAI solutions may work well for common tasks, but they fall short in high-precision applications like vehicle inspections. At Click-Ins, we’ve built technology tailored to the demanding requirements of our customers in the field of vehicle inspections. By integrating advanced engineering disciplines such as photogrammetry, 3D, and computer vision, we can facilitate complex tasks such as damage measurement or damage matching, which impose serious challenges on modern AI systems.
By coupling visual intelligence principles and ontology with AI, it becomes possible to generate more explainable and trustworthy AI systems that align with human expectations and domain-specific requirements. By utilizing this synergy, Click-Ins is committed to the ongoing efforts to develop trustworthy, responsible, and ethical AI technology.
