Judging whether an AI vision solutions company has reliable project experience cannot be based solely on the number of cases they cite or their demo results. You need to dig deeper into several specific aspects.
First, see whether they are willing to show the real details behind their projects. A trustworthy company will proactively discuss the difficulties encountered during a project, such as lighting changes, occlusion issues, false detections in complex backgrounds, and how they adjusted models and optimized algorithms to handle these challenges. If they only emphasize a 99 percent accuracy rate or that the system was stable from day one, yet cannot explain the data collection methods, annotation standards, or training iteration process, the experience is likely embellished. You can ask to see screenshots or recordings of the system in operation, or even request a site visit. Projects that can withstand such firsthand verification are far more credible.
Second, assess whether they understand your industry scenario. AI vision is not one-size-fits-all. Retail shelf recognition, industrial defect detection, and security behavior analysis each have vastly different data distributions and business logic. An experienced company will proactively ask about your production line workflow, environmental constraints, and cost expectations, and may even point out metrics you have not considered, such as downtime losses caused by false detections or low-light interference in nighttime images. If they only talk about technology and not business, it means their past experience is unlikely to transfer well to your project.
Third, focus on their ability to review and reflect on past projects. Reliable providers do not shy away from failed cases. Instead, they will honestly share which projects fell short of expectations and why, whether it was due to insufficient data, overly complex scenarios, or changing requirements. Such candor is far more revealing than sugarcoating. You can also learn about their customer retention rate indirectly, or ask whether existing clients are willing to serve as references. That is more convincing than any marketing material.
Finally, do not forget to verify the time span of their experience and the stability of their team. The AI vision industry has high turnover. If core algorithm engineers have changed multiple times within a year, there is a risk to ongoing maintenance and upgrades even if the company has historical projects. A stable team means the project experience is truly embedded within the organization, rather than lost when individuals leave.
In short, reliable project experience is not just talk. It is reflected in a company’s attention to detail, understanding of scenarios, honesty about failures, and team stability. During your screening, ask one more question: How was that project iterated afterward? Whether they hem and haw or answer clearly will already reveal their true colors.