Podder.ai is an AI platform that provides the tools you need to use AI in your business. By using Podder.ai, you can easily build a highly scalable application using the AI model you developed. Furthermore, it is possible to deploy the application to any environment, such as on-premises or private clouds. In addition, we have a large selection of libraries tailored to business and domain, and by combining with AI models according to customer needs, we support total prediction accuracy improvement as an AI application.
Nowadays, many companies are doing PoC to use AI in their business. However, when trying to introduce and operate an AI in a company, there are many cases in which the following issues cause abandonment of actual operation or project delays due to problems.
Many companies are promoting PoC, but one of the reasons for actually giving up is that AI does not have the expected results. For example, consider AI-OCR for invoice reading. Even if the reading accuracy of AI-OCR exceeds 90%, the contents and format of the extracted text data are different from the corporate master and currency format and it is not useful. As described above, there are many cases where the prediction accuracy of the AI model alone does not meet the needs of the enterprise.
Applications that use AI require processing a large amount of data, and so system design with high scalability is required. To do so, you need to have a high level of expertise on infrastructure, but often you can not meet your requirements due to the lack of the necessary skills.
When introducing AI into enterprise systems, customers are required to deploy on existing systems such as on-premises and private cloud represented by AWS. Therefore, the increase in development man-hours and costs due to the construction of environment and operation flow for each company causes a problem that it does not match the cost-effectiveness expected by customers.
We provide various libraries such as data validation, formatter, auto collector etc. When used for pre-processing and post-processing, you can respond to customer needs flexibly and quickly by simply updating the configuration file. This enables you to improve the accuracy according to your business and domain. In addition, the development costs that have been handled individually can be significantly reduced.
By providing infrastructure design and construction tools that can be used by non-engineers, anyone can easily build a system with high scalability. In addition, since the introduction of AI applications also provides automation tools, it will be possible to proceed smoothly in a short period.
Standard support for creating AI application server environments for on-premises and private clouds, such as AWS and GCP. As a result, you can deploy AI applications in any environment. In addition, it is possible to configure the environment and change the setting according to the customer's security requirements and the existing system, and system integration can be performed according to the customer's needs.