AI Platform

Access cloud and on-premise data sources with pre-built connectors. Developers and data engineers can use these connectors to integrate data on the Alteia platform without developing any custom integrations.

• Data ingestion interfaces, data capture module.
• Databases (SQL, noSQL).
• Data streaming pipeline (Kafka).
• File systems including AWS S3, Azure Data Lake Store gen2, local file systems.

Experience built-in visualization tools for 2D and 3D to automatically generate an internal representation of all data available on the platform to accelerate web visualization. The Data Visualization tools allow for contextualized navigation by date, location, or type of business attribute.

The Alteia platform provides built-in tools relying on artificial intelligence to contextualize data and accelerate data analysis. Contextualization can reduce the amount of data based on various inputs like applications, services, or users. Its true power lies in improving the scalability and efficiency of data process, query, and delivery.

With the Enterprise Data Catalog, developers and business users can access all relevant metadata on data objects, features, and machine learning models through an enterprise data catalog.

• Capture technical and business metadata about data and machine learning models via a flexible and powerful cataloging system.
• Discover and trace lineage from source systems to machine learning models for an end-to-end view of your data.
• Easily maintain where the data comes from and who owns it and uses it for data analysis and extended collaboration.
• Accelerate data discovery by applying custom tags to the metadata.

Alteia’s structure is on top of a unified data model. It takes advantage of the elasticity and scalability of cloud-based storage to analyze large volumes of diverse and co-registered data and take advantage of flexible compute resources. With an abstraction layer between the underlying storage formats, and the application programming interface (data model), unified data can accelerate the development of new machine learning models and insights. A unified data model can also reduce the redundancies and potential inconsistencies of multiple copies of data in the enterprise.

Data Transformation tools enable you to apply specific rules to a set of data and transform its attributes or features.

Experience effortless data collection in the field or ground truthing with Infield, Alteia’s mobile application (available on iOS and Android).

The Data Pipeline Manager offers a comprehensive suite of tools to schedule data acquisition campaigns, manage teams, and automatically generate the desired outputs.

Ingest, transform, and contextualize 3D models and other visual data (orthomosaics, DSM, DTM, etc.). Visually explore and assess complex data dependencies. Design better models faster.

Make use of a variety of pre-built ML models to accelerate the development of enterprise AI applications. These packages contain methods for predictive analytics such as anomaly or object detection, task segmentation, and point cloud classification. Companies can also choose to extend these pre-built object models to reflect their unique business process or develop custom new object models.

The Alteia platform offers a suite of tools that support creating and using time series data for AI applications. Time series refers to a list of data points in time order. Time series are essential for representing the change in value over time of data relevant to a particular problem, such as inventory levels, equipment temperature, or wear and tear. Time series data provides the historical information needed for analysis by AI and ML algorithms to generate and test predictive models. With correct cleansing, normalization, aggregation, and combination, time series data can represent the state of a process over time to identify patterns and correlations to create and evaluate predictions applicable to future behavior.

With the Smart Annotation Toolkit, you can simultaneously label data with internal and external teams and work collaboratively to review and comment on annotations. Use it to implement incremental learning strategies while tracking your activity and progress.

• Bounding box
• Points & lines
• Polygons
• Support for tiled imagery (slippy maps)

Enable a variety of pre-built logic-based algorithms that can accelerate the development of visual intelligence. These tools do not just enable the calculation of distances, surfaces, and volumes but also the analysis of reflectance maps and other types of indexes. You can also choose to extend the pre-built tools to reflect your unique business process or develop custom new packages.

• Employ multi-factor authentication for peace of mind.
• Manipulate access and user control based on positions or attributes.
• Experience secure and reliable systems with virtual private clouds / networks that are accessible over robust network infrastructure.

• Select any SAML v2.0 compliant identity provider.
• Leverage two-factor authentication support to meet advanced authentication requirements.
• Delegate user authentication and authorize third-party applications to access the Alteia AI platform.

Alteia provides feedback loop workflows for incremental learning. A feedback loop, or closed-loop learning, describes the process of leveraging the output of an AI system and corresponding end-user actions to retrain and improve models over time. The AI-generated outputs (predictions or recommendations) are compared against the final decision (for example, to perform work or not) and provide feedback to the model, allowing it to learn from its mistakes.

• Leverage the Alteia platform’s comprehensive approach for managing access to data, APIs, and infrastructure resources.
• Segregate duties within the team and limit user access to resources or create implications in the business process based on job requirements.
• Define expressions selectively to provide or restrict access to application and infrastructure resources.
• Share information, organize your change management, and define your organizational processes.

• Ingest and integrate efficiently with data management APIs.
• Analyze effectively with the Analytics API.
• Organize data and set terms for how your users interact with it with the Project Management API. Further organize per location (projects) and acquisition date (mission).
• Access thousands of low-level micro-services with thousands of documented endpoints.

•  Map service
  • Feature Service
  • Map Service
  • Annotation API

The Alteia Software Development Kit (SDK) is an additional set of standardized software tools that allows you to develop data exchange and processing directly with Alteia. Used to create, test, and develop specific applications quickly, on top of the current Alteia platform, and then industrialize them to integrate the Alteia platform with other applications seamlessly. It enriches the platform with advanced functionalities, industry-specific or customer-specific analytics, push notifications / data, and more. Everything needed to create your own modules and analytics is included, such as a code library, examples, documentation, and the methodology necessary to perform specific functions.

• Monitor all aspects of the Alteia platform, from the state of an analytical job to API performance.
• Visualize current and historical environment workload using an intuitive environment operations portal.
• Monitor the status and throughput of invalidation queues, the mechanism by which the Alteia platform handles asynchronous processing requests.
• Configure auto-scaling rules to maintain the cost and availability of compute resources.