VIEWAPP: Why a Secure Digital Inspection Perimeter Is Even More Important in the Era of Neural Networks
Many companies — for example, insurers, appraisers, and adjusters — that work with object inspections and condition monitoring need to build a fully automated process: conduct an inspection, obtain photo and video materials, recognize damage, determine its nature and extent, calculate the cost of repair or the amount of damage, and then prepare a report. The fewer manual operations and intermediate stages, the faster a case is processed and the lower the burden on employees and the associated costs.
At first glance, modern artificial intelligence technologies offer a fairly simple path to such automation. Neural networks already exist that recognize damage in photographs with varying degrees of accuracy, classify it, assess the condition of objects, and help calculate restoration costs. It seems that all you need to do is choose a suitable model, connect it to an information system, and pass inspection results to it.
However, this approach has an important characteristic: a neural network analyzes the data it receives as input. In this article, we will not examine the accuracy of specific AI models, but will look at the process from another angle.
Even high accuracy in damage recognition does not by itself confirm that the photographs were actually taken of the required object, as part of a specific inspection, and at the specified point in time.
Therefore, automation cannot be limited to choosing an AI model. It is equally important to build a secure digital inspection perimeter — a system that organizes the collection of materials, verifies their authenticity, stores a history of actions, and passes the results on for further processing.
Digital Inspection and the Neural Network: Who Is Responsible for What
To analyze damage, simply obtaining a photograph is not enough. It is important to understand where it came from, when it was taken, which object it relates to, and whether it has been subject to substitution. If this information is not controlled in any way, the system receives an image without reliable confirmation of its origin.
For example:
• when inspecting a car, photographs may not have been taken at the inspection site but uploaded from the gallery;
• when assessing real estate, old images may be used instead of the current condition of the premises;
• an image may relate to another object or may not show the required area in sufficient quality.
A neural network is capable of recognizing a crack, traces of leakage, or body damage in such a photograph. But the fact of recognition itself does not confirm that the detected defect is actually present on the object being inspected right now.
Therefore, control must begin even before the materials are passed to the AI system — directly at the moment the inspection is conducted.
A Secure Perimeter Begins with Data Collection
Digital inspection in the VIEWAPP service is not just a form for uploading photographs. It is a managed process that records information about the object and the circumstances of the inspection.
Depending on the scenario, the system can control:
• the time and place of shooting. Geolocation, date, and time are recorded together with the inspection materials. The coordinates can be compared with the address of the object;
• the origin of the photographs. Using the device camera instead of uploading ready-made images from the gallery helps reduce the risk of material substitution;
• the completeness of the inspection. The scenario defines the required angles and sequence of actions so that important areas of the object are not missed;
• the quality of the materials. The system can identify blurry, too dark, or insufficiently informative photographs and suggest re-shooting;
• the integrity of the data. Technical control mechanisms help detect attempts to modify or substitute files.
As a result, what is formed is not just a set of images, but a structured history of the inspection: who conducted it, when and where the photographs were taken, which stages were completed, and which materials were obtained.
At the same time, the level of protection depends on the method of conducting the inspection. For example, a mobile application can use additional capabilities of the device and its sensors, whereas a browser-based scenario usually has a more limited set of control tools.
AI Models May Change, the Inspection Process Remains
Another important point relates to how quickly the artificial intelligence market is developing. New recognition models appear, image analysis algorithms improve, and damage calculation tools change. It is important for companies to be able to connect and compare different solutions without having to rebuild the entire process of working with an object each time.
Therefore, the architecture of an automated system should not be rigidly tied to a single neural network.
For example, after a car inspection, photographs and damage data can be passed to a specialized AI model. The recognition results are sent to the repair cost calculation system, after which a preliminary calculation is generated. A specialist checks the result, adjusts it if necessary, and completes the case processing.
When working with real estate, the scenario may be different. A neural network analyzes photographs of the premises and helps identify traces of leakage, cracks, the condition of engineering systems, or fire safety equipment. Based on the structured data, a draft report is generated, which is then reviewed by an expert.
In both cases, the sequence of actions around AI remains approximately the same:
1. Creating an inspection task and determining the required materials.
2. Collecting photo and video data with control over shooting conditions.
3. Verifying the received materials and forming a structured inspection result.
4. Passing the data to the selected AI model and calculation system.
5. Reviewing the results by a specialist, preparing the report, and signing it.
If a more suitable neural network appears or the calculation system changes, the company can replace the corresponding component while preserving the rest of the infrastructure.
VIEWAPP: Infrastructure for Working with Different AI Solutions
This comprehensive approach is at the core of VIEWAPP. The platform organizes digital inspection, controls the collection and transfer of materials, ensures interaction between participants, and forms a structured result that can be used in further information systems.
At the same time, VIEWAPP, which develops its own AI solutions, does not claim to replace all specialized neural networks. The platform can work with various AI engines and calculation systems: the company's own solutions, modules in insurers' information systems, or external developments. Integration via API makes it possible to embed digital inspection into the existing IT landscape and return results to the main case processing system.
For a company, this means the ability to develop automation gradually: first organize secure data collection, then connect damage recognition, damage calculation, automatic report generation, and other tools. As new technologies emerge, individual components can be updated without having to rebuild the entire process from scratch.
A neural network can become a powerful automation tool, but the reliability of the entire system depends not only on it. A secure digital inspection perimeter creates the foundation on which different AI solutions can work with verified materials, and companies can build a managed process from the first snapshot to the finished report.