Beyond the challenge of mastering the technical components required to implement AI applications, an organization must master it at scale. This is the heart of every organization’s digital transformation – AI and ML applied to an expertise domain at scale.
Integration requires intuitive tools for employees across the organization so that interactions with data and models are accessible and possible in a low-code environment. From there, it is possible to easily extract business metrics and Key Performance Indicators (KPIs) to measure the success of implementation accurately.
For industry leaders, the natural strategic approach is choosing the right visual intelligence technology stack, developed, maintained, and operated as a service by a third-party specialist. At Alteia, we understand this, so we built a platform that offers a suite of tools for AI monitoring and improvement. This way, businesses can focus on their domain of expertise and maximize value creation through the rapid and efficient development of vertical-specific AI applications.
Architecture, technical features and benefits
The Alteia software is uniquely powerful as it enables rapid development and deployment of vision AI applications at scale. These applications are flexible, easily upgraded, and can be ported across various cloud platforms with little to no modification. At Alteia, we provide solutions that future-proof your investment in enterprise AI and IoT application development, with a fraction of the resources and time required for alternative approaches.
Massive amounts of data, many sources
With AI you can make sense of vast quantities and varieties of visual data. The machine is capable of indexing it automatically, structuring the database. Then, it can be mobilized to answer specific questions about the past or present, and sometimes future, state of industrial assets. Thus, it becomes possible to capitalize on visual data aggregation. Visual data is not the only type of data available for assessment. For instance, IoT is a powerful source of insight once contextualized. Visual data is technically the most complex; however, to be complete, the software stack must ingest all other data types coming from the field. The challenge lies in finding a common thread amongst data that does not speak the same language, for example, different data formats, natures, time samplings, etc. With the creation of a model: a representation of a physical thing associated with rules that describe its behavior, it suddenly becomes possible. Now, any inflow of data can be used to update the model’s current state with respect to the rules. In this way, you can reconcile all data types; even though they do not speak the same language, they now all speak to the model.
Toward smart industrial operations
Once you have accumulated sufficient data over time, you have a complete picture of the past. With enough history, you can predict the future; fundamentally, the models become predictive over time. Then, they can be used to run simulations and optimize the complete operation and maintenance chain. Just like that, visual data management becomes the cornerstone of your digital transformation.