The footprint of humanity is evolving substantially – from climate change alone scientists estimate that more than 600 million people on the planet have already been stranded outside of habitats that best support life. Other factors such as natural disasters, conflict, a global pandemic, and economic opportunity are changing the map faster than traditional predictive platforms and systems can keep pace with. How we understand this change and anticipate its direction impacts how governments allocate social support, development institutions finance sustainable development initiatives, and commercial enterprises pursue growth in the most inclusive and low carbon manner possible. 

Atlas AI has built a geospatial artificial intelligence platform that helps every organization anticipate changing societal conditions — where people live, where wealth and poverty are concentrated, how the physical makeup of communities are evolving, and more — to determine where to invest today to prepare for the world of tomorrow.

Challenges

There are a range of alternative geospatial data sources that can help companies better understand their markets. However, while these additional sources have been available to companies for years, this data has not yet resulted in greater awareness of market changes or the ability to respond to them with agility.

There are many reasons behind this, including:

  • The available data sources only tell one part of the story. But many different types of data — each archaic in their own way — need to be brought together into a single integrated ‘fabric’ to tell the complete story of local change.
  • There isn’t enough geospatial information available from Europe and the wide range of rapidly growing markets that make up the global operating footprint for most multinational companies. And while there is better information available from the US, it’s often incomplete. 
  • The modern data and analytics stack is still not mature when it comes to developing predictive machine learning models on geospatial data. Structuring this data for the purposes of training ML models is typically a bespoke activity within data science teams. In particular, integrating external market data with internal operations data is a poorly supported workflow within data science workflows.
  • Even the best market data available can only tell you what happened a minute, an hour, a week or a year ago. None of this information alone can help you anticipate what comes next. If you’re investing in new supply chain capacity, the demand for your product a year from now is far more relevant than the demand a year or even a month ago.

When you compound these challenges with increasing levels of migration, growth, and upheaval in the world, you can see the massive opportunity in helping companies meet the evolving demands of our society and promote inclusive commercial growth. 

Solution

Commercial enterprises most frequently use Atlas AI’s platform to plan investments and operational priorities at the intersection of sustainable supply chains and consumer demand forecasting. The Atlas AI platform monitors and anticipates changing patterns in local development for every community on the planet, predicts the implications of those trends in the context of demand for new infrastructure as well as consumer products and services, and supports decision makers to act with agility to meet that demand. Companies that utilize Atlas AI’s predictive intelligence platform invest more efficiently, accelerate revenue growth, and reduce risk of being poorly positioned in their most attractive future markets.

By working in partnership with BigQuery and utilizing Google Cloud’s infrastructure, AI platform capabilities, and geospatial analytics, Atlas AI has been able to realize their vision of a truly global AI-powered platform. Companies using the Atlas AI platform have successfully monitored changing industrial supply chains in the US, forecasted consumer demand in Indonesia, optimized farm equipment utilization in Africa and assisted vulnerable communities by providing life-saving interventions in Southeast Asia.

Companies that benefit most from Atlas AI’s platform:

  • Are seeking to better integrate data across the business with the latest market insights for the purposes of business intelligence and business planning
  • Have ambitious growth plans but are struggling to optimize predictive models with the best data and AI techniques to optimize and adapt pathways to growth
  • Are particularly sensitive to changing human migration and development patterns, given infrastructure and supply chains that are not easily adjusted once implemented
  • Are focused on bringing infrastructure, products and services to traditionally underserved communities around the world that are not as well captured in traditional market research

Discover the business impact of Atlas AI

Engie Energy Access, the global renewable energy company, is using Atlas AI’s platform in Kenya to predict the location of the optimal customers for home solar energy-powered appliances. Home solar energy adoption is a critical component to SDG7, the sustainable development goal focused on universal energy access by 2030, but it is often unclear which households have the need and the capacity to adopt solutions from commercial providers. This is all the more challenging as solar energy companies have to grow alongside a rapidly growing market, meaning a company can’t set a fixed growth strategy and execute it. 

Responding to a rapidly evolving market context is essential to commercial success. In early usage of the Atlas AI platform, Engie’s commercial team was able to experience a 48% increase in sales in the sales regions where the platform was deployed.

Built with BigQuery: Atlas AI creates global awareness on a local level 

Underpinning the commercial and sustainability opportunity requires substantial cloud and data infrastructure to power the extent of imagery, spatial data, artificial intelligence, and enterprise analytics capabilities to realize the above vision. Atlas AI is only able to achieve global insight into market trends at such a local level because of extensive integration with Google Cloud solutions such as BigQuery and years of platform development at the intersection of software development, MLOps, and data engineering. 

These capabilities allow Atlas AI to:

  • Use a range of structured and unstructured datasets within BigQuery and GCS (Google Cloud Storage) in an “AI lakehouse” architecture to model the state of wellbeing of local communities around the world.
  • Harness scalable geospatial ML capabilities within Google Earth Engine and Vertex AI to integrate a range of disparate data about a community to model core commercial and social sector outcomes such as consumer demand potential or nutrition vulnerability.
  • Integrate operational data from our customers’ CRM, ERP and other decision support systems into a dynamic geospatial feature store built on BigQuery. This then enables real-time predictive modeling, in order to forecast where the greatest future opportunity will be for the organization to advance its commercial and sustainability goals.
  • Deploy this information in the manner most conducive to the end users consuming it – whether via data exchange on Analytics Hub, API integration or our Aperture® web application, enabling more commercially and societally impactful decisions, optimizing supply chains, and better responding to consumer demand signals.

This end-to-end technology stack offers an innovative and powerful illustration of the breadth of Google Cloud services, from Earth Engine, to BigQuery, to Vertex AI as depicted in the following architecture diagram; and in particular the application of these platform capabilities to globally scalable sustainability solutions.

The Built with BigQuery advantage for ISVs and Data Providers

Google is helping companies like Atlas AI build innovative applications on Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs through the Built with BigQuery initiative. Participating companies can: 

  • Accelerate product design and architecture through access to designated experts who can provide insight into key use cases, architectural patterns, and best practices. 
  • Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.

BigQuery gives ISVs the advantage of a powerful, highly scalable data warehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. And with a huge partner ecosystem and support for multi-cloud, open source tools and APIs, Google provides technology companies the portability and extensibility they need to avoid data lock-in.