Why automating your vehicle inspection process makes sense in 2025
You might already be aware that Artificial Intelligence has been a popular buzzword in recent times.
Since the release of ChatGPT, AI has been on the radar for every business and they are constantly on the lookout for tools and methods to help automate their daily operations. And it is no surprise that the businesses that aren't exploring this are lagging behind (or eventually will be).
As Artificial Intelligence develops further, we will start seeing newer ways of how we can leverage its power to improve our daily business operations making it more efficient and quicker.
In this blog, we will cover how AI has developed over the years, the innovations it has helped create in automating vehicle inspections, and why it makes sense for your business to automate your vehicle inspection process using Artificial Intelligence.
Let's dive in!
Table of contents
- How has AI evolved over the years
- How is Inspektlabs automating vehicle inspections using AI for your industry?
- Key features Inspektlabs built in 2024
- What to expect from Inspektlabs in 2025
- Conclusion
How has AI evolved over the years?
Artificial Intelligence, while being a trending word today, has been around since the early 1940s.
During this time, Warren McCulloh and Walter Pitts were already discussing the possibility of creating an artificial neural network that enabled computers to make logical decisions. They also published a paper called "A logical calculus of ideas imminent in the nervous activity" which was also the first mathematical model of a neural network.
In the 1950s, this paper was used as a reference to develop the Turing test, created by Alan Turing, who is known as the father of Artificial Intelligence and modern computer science.
The Turing test, which was published in his paper called "Computer Machinery and Intelligence" was a test made to evaluate a machine's ability to exhibit intelligent behaviour that was indistinguishable from a human.
Alan Turing proposed that if a machine can engage in a conversation with a human without being identified as a non-human, it'd be considered as "artificial intelligence", which later went on to become the benchmark for evaluating how smart AI systems actually are.
However, it wasn't until 1956 that the term "Artificial Intelligence" came into existence, which was coined by John McCarthy, the creator of LISP (the first AI programming language).
This led to a whole lot of exploration and experimentation to understand the ability of Artificial Intelligence and what it could do. However, AI still wasn't as smart and seemed more like a gimmick rather than a useful tool, which led to the first AI winter in the 1970s.
During this time, people started moving away from AI and stopped funding research projects because it didn't show any practical applications and failed to do the easiest tasks. This was also because AI until now had the ability to only follow a pre-defined set of rules which it received from very limited data. Anything that went beyond these pre-defined rules would result in AI failing to function.
Fast forward to the early 2000s, where we started seeing actual innovation in AI. During this time, Artificial Intelligence had evolved to learn patterns from labelled data and was capable of running various permutations to interpret information that wasn't pre-defined. This was also the time where deep-learning models that had the ability to read and interpret languages/images came into existence and eventually led to the development of practical applications like Apple's Siri and Amazon's Alexa.
In 2017, we saw the development of GPT (Generative Pre-trained transformers) which was already smarter than the existing AI models, and didn't rely on labelled data to function. This move also led to the democratization and widespread adoption of AI, because it finally meant that Artificial Intelligence could be used by anyone without any technical expertise.
Aspect | Pre-GPT AI | Post-GPT AI (Generative AI) |
Learning Method | Supervised (task-specific) | Unsupervised (general-purpose) |
Architecture | RNNs, CNNs | Transformer-based |
Model Size | Small to medium | Extremely large (e.g., GPT-3: 175B parameters) |
Data Requirement | Labeled data | Large, unlabeled datasets |
Capabilities | Narrow AI (specific tasks) | Broad and general (multi-task AI) |
Applications | Image classification, NLP, chess | Content generation, multimodal AI, chatbots |
While GPT became very popular very fast, it also saw a similar trajectory as AI in the 1970s where people eventually got tired of the novelty behind it and slowly started moving away from this.
The Gartner Hype Cycle, which is a graphical representation of the evolution and journey of any emerging technology, shows that AI currently is past the phase of hype and novelty and is currently in the phase of innovation leading to practical applications.
As of today, AI can be used to create any form of content (text, audio, images, videos) or very easily automate mundane tasks saving both time and effort.
AI has also evolved to be able to interpret information from images and videos (often more accurately than humans), which is the genesis of innovation in vehicle inspections.
Read more about the History of AI and Deep learning.
How is Inspektlabs automating vehicle inspections using AI for your industry?
Since 2018, Inspektlabs has been constantly trying to improve the process of damage detection and vehicle inspections. The current AI model (trained on 30M+ images and videos of vehicle damages) can easily detect any form of physical damage on 160+ parts of vehicles ranging from hatchbacks, SUVs, Sedans, Trucks, Motorbikes, and more.
Inspektlabs' AI vehicle damage detection model is currently being used across various industries to automate different business processes. For example -
Insurance
- Running pre-insurance inspections to identify vehicle conditions - With Inspektlabs' Vehicle Inspection model, auto insurance companies can very easily track and report the existing conditions of a vehicle, capturing every detail with precision.
This information can not only be used to generate a more realistic premium amount based on the vehicle's existing condition, but can also keep track of old damages that helps avoid the chances of fraud claims in the future. It also helps identify instances of damage cover up via paint jobs or stickers, and identifies cases of vehicle switching which are some methods often used by car owners to cheat auto insurance companies into paying a higher claim amount. - Automating FNOL inspections - With Inspektlabs, companies can easily conduct FNOL inspections remotely without having to visit the accident location to inspect the vehicle.
It enables companies to scan vehicles for damages using just a few images and videos, and generate detailed reports covering the extent of damage on the vehicle, and additionally automate decisions of repairable vs. total loss on vehicles. - Claim estimation and processing - Along with identifying the extent and type of damages on vehicles, Inspektlabs also helps automate the repair cost estimation process by generating repair cost estimates based on market prices.
This also helps automate auto insurance claims processing end-to-end via the STP method, saving time, effort, and reducing human intervention significantly.
Additionally, auto insurance companies can later use the information from each auto insurance claims settlement process to further refine the automated systems by rectifying error cases, and approving the right ones.
Vehicle Commerce
- Car remarketing/used car dealerships - With Inspektlabs' AI model, car remarketing and used car dealerships can easily automate the vehicle inspection process and generate a more realistic value for vehicles based on their conditions.
Using a digital vehicle inspection process helps reduce the time, cost, and effort involved in vehicle inspections, enables more thorough inspections, and helps improve retention by giving customers a novel and transparent experience. - Car rentals - Car rental companies can use Inspektlabs' AI model to generate reports of the vehicle's condition before it was rented by a customer and after it was returned post usage.
This helps create transparency between car rental companies and the customer, and also prevents instances of fraud where customers might try to hide damages while returning the vehicle or where the car rental company might try to force customers into paying for damages that already existed on a vehicle. - Car Auctions - The value of a car during auctions is highly dependent on the conditions and physical appearance of the vehicle.
With Inspektlabs AI, car auction companies can run detailed digital vehicle inspections, capturing even the small damages present on its surface. This in turn helps them generate a more realistic and transparent valuation of the vehicle during the auction process.
Fleet Management
Inspektlabs AI enables fleet management companies to keep track of incremental damage on the vehicle.
Put in simple terms, Inspektlabs AI allows fleet management companies (or companies using multiple vehicles for its business operations) to keep track of their vehicles before they leave a facility and after they arrive.
This is a necessary process needed to keep track of a vehicle's condition, ensuring its health, and also gauging the wear and tear on any vehicle.
This process also helps identify any serious damage on the vehicle ensuring boht the driver and the vehicle's safety.
Key features Inspektlabs built in 2024
Now that you have a better understanding of how Inspektlabs is building its AI model to solve different problems for various industries, let's also look at some of the interesting features we released this year to take this a step further.
Inspektbot for quick SMART repair cost estimates
Inspektbot is a WhatsApp bot that uses the Inspektlabs AI model to easily detect vehicle damages and share repair cost estimates to vehicle owners, all via a simple engagement over WhatsApp.
In a situation where a vehicle is damaged, the vehicle owner has to initiate a conversation with a company's repair center, post which they receive a link to engage with Inspektbot on WhatsApp.
The bot asks the vehicle owner to share images of damages on the vehicle from different angles, capturing every detail. Once received, the bot analyses the image to identify the type, location, and extent of damage and shares a detailed damage report in just a few minutes.
Inspektbot also has the ability to share real-time feedback on the image's quality by highlighting unclear images and asking the vehicle owner to re-upload a better version with more clarity.
The goal of building Inspektbot was to help companies reduce the time taken in repair cost estimations, enable vehicle owners to easily conduct inspections, and streamline the whole process which eventually result in better retention in the longer run.
Seamless integrations with Estimatics providers
In 2024, Inspektlabs decided to take it a step further and also provide comprehensive repair cost estimates for vehicle damages based on the market values of repairing vs. replacing parts.
To achieve this, we partnered with various Estimatics providers such as Mitchell and GT Motive and built channels allowing you to seamlessly integrate this into your existing system.
With this update, Inspektlabs can now deliver accurate part prices and codes, making it easier for our customers to make cost-effective repair decisions.
Enhanced user guidance during 360° vehicle inspections to facilitate proper reporting
Inspektlabs' AI model was already capable of reading images/videos and approving or rejecting them based on their quality.
However, to make things easier, we also introduced real-time nudges that guide users to capture detailed images/videos of damage during the inspection process using a state-of-the-art Machine Learning model trained to identify what part of the vehicle is being reported.
This feature is especially useful when you're inspecting vehicles in tight spaces with little room to move around the vehicle and/or when you're capturing micro damages that are commonly overlooked by untrained users.
Damage size and subcategory detection
One common request that we've often received from our users is to be able to report the size of damage on a vehicle during the inspection process.
We understand that this is a crucial step for many companies as it helps create more detailed reports and generate accurate repair cost estimates needed to fix a vehicle. And hence, we improved the AI model's efficiency to go beyond just classifying damages as dents, cracks, scratches etc.
With this release, companies can now report the size of damage, as well as the subcategory, allowing them to make more informed decisions, and allow us to cater to more use-cases and workflows.
Incremental Damage detection
Keeping track of a vehicle's condition before and after a journey is a crucial process for fleet management and other similar companies. This helps them constantly check the vehicle's health and also record the wear and tear of the vehicle. However, this process is very tedious when done manually.
To tackle this challenge, we updated our model to keep track of the incremental damage on a vehicle, where users can report the existing condition of a vehicle and compare it with the past/future conditions over time and capture additional damage and track the vehicle's overall condition.
What to expect from Inspektlabs in 2025
2024 has been quite an eventful year. We've constantly been building features that helped our users in their daily business processes with the goal of easing their work end-to-end.
However, this is just the beginning of what we have in store.
Here are some of the interesting Inspektlabs features you can look forward to in 2025
Fully customizable no-code app to fit your business needs
While Inspektlabs already provides customizable apps to cater to your business requirements, the back and forth between our tech team and your company is quite tedious and often takes very long for development.
In order to make things easier, we are working on giving you full customization control allowing you to build an Inspektlabs enabled app unique to your business.
With our no-code platform, you can choose what features you want to add, the colors that represent your business, the user interface that's ideal for your requirements and whole lot more.
Tire condition detection
While Inspektabs' AI is already trained to capture damages on 160+ parts of a vehicle, we will be taking it further and enhancing our model to also identify the vehicle's tire conditions.
With this release, you will soon be able to scan the tire with your camera to track the condition, age, tread depth, and amount of wear due to usage on the vehicle, allowing you to generate a more comprehensive report of your vehicle's conditions.
Enhanced fixed camera inspections
One important area of focus for us in 2025 would be to evolve from smartphone-based inspections and improve our fixed camera inspection process.
This means that with a simple hardware installation, you can run detailed vehicle inspections by just parking your vehicle in a designated location.
Once parked, the fixed cameras will capture every detail of your vehicle and generate a detailed report of your vehicle's condition within a few minutes.
Improved systems to enhance your business processes
In 2025, we will also focus on how we can improve our systems to make your business processes more efficient.
Some of these improvements include
- Enhancing our existing model to capture damages more accurately with increased precision across a more extensive list of damages.
- Improved user-guidance systems using Machine Learning to make sure that your inspections are detailed, generating more accurate reports.
- Increasing our list of integrations with Estimatics providers by onboarding 7+ companies to serve a broader market globally.
Conclusion
Artificial Intelligence has come a long way since its inception and the last few years have seen the most amount of development in technology, now enabling more and more businesses to automate their daily business processes.
Any business that fails to innovate using AI will be riding against the wave of growth and will find it very difficult to be able to survive in a market with cut-throat competition.
As AI continues to evolve, we at Inspektlabs are also evolving and enhancing our product's functionality to give our customers the best solution that helps automate their vehicle inspection process from start to end.
If you are looking for a solution that can help automate your vehicle inspection process and save you time, money, and effort, reach out to us to know more about how we can help you in this journey.