DATA FOR GOOD

Spx.ai is founded on the principle that the real value of data lies in the way it can enhance our understanding of the world, empower our communities, and improve our lives.

  • .UNDERSTAND ENVIRONMENT
  • .ENABLE DECISIONS
  • .IMPROVE LIVES
WORK WITH US

The Power of Data

Spx.ai leverages artificial intelligence and complex modeling to combine often disparate data in ways that would otherwise be unexplored. We recognize that virtually all data has a human dimension that enables us to enhance our understanding of society and our environment and improve human condition.Data has the answers to What, Why, and How to our questions it’s just not obvious in raw context. Leveraging Ai and Data produces the Power of Data to improve our society for all.

Our use cases

Why

The data universe is massive and growing exponentially.
Deploying the infrastructure necessary to array the vastly different data sources, types, and states (in motion, at rest, and at use) of data to meet research requirements is costly and time consuming. Artificial Intelligence is an emerging and largely developmental discipline that requires specialized skills and creative insights. Spx.ai brings together the data and AI capabilities in one place to develop creative ways to answer hard, often complex, questions.

How can we help

Spx.ai_logo Analyzes Widely Disparate Data

Spx.ai recognizes that the not all data is created equal or equally available. Data sets that may reveal the critical answer to an analytical question may be just out of reach, or even unknown. Spx.ai specializes in bringing these data sets together and analyses them in a way that makes them useable in analytical processes.

Ways to Engage

Data Structuring & Management

Bring your own data to the Spx.ai management platform.
Spx.ai will run our proprietary ETL process and either transfer it back to you, or make it available as structured, indexed data for your own analytics. Spx.ai specializes in optimizing data ingest workloads across several media types and repositories.

Consulting

Let the Spx.ai team put set of fresh eyes on your most daunting AI and modeling challenges. Let us understand your problems and data and apply technology to produce answers.Spx.ai consulting can be combined with any of the other services we provide.

USE CASE

Food Insecurity

Commodity Markets and Social Media as predictive indicators of food insecurity in urban markets.

Commodity markets have long been known to be indicators of food insecurity in rural or agrarian markets.In urban environments however, the factors that contribute to food insecurity are more complex and factors like localized economic disparity, ethnic distribution and social pressures can create urban food deserts that change from block-to-block. Spx.ai fused traditional techniques for analyzing commodity prices with behavioral models employing entities extracted from unstructured social media data and news feeds to accurately model localized areas of food insecurity to enable aid organizations to identify and mitigate root causes.

USE CASE

Media Influence

The impact of geographically separated populations and overseas fake news on media campaigns.

The effectiveness of media on local popular opinion and social trends can be influenced by populations and media platforms that that are far away. This fact is further complicated by the fact that, (1) even at the local level, metrics that assess and predict the effectiveness of media efforts must account for the influence of content that might be generated half a world away, and (2) troll-farmed media can both rapidly dilute actual sentiment in analytics and have profound effects on the sentiment of the subject populations, even at the local level. Spx.ai correlated sentiment and opinion trends with external influence (geospatially) to accurately demonstrate the impact of external media on local sentiment. Spx.ai employed meta-data trends to isolate the effects troll-farm-produced media and quantify its influence.

USE CASE

Resources Allocation

Understand population responses to natural disasters and assess effectiveness of public safety.

Understanding and predicting collective behaviors both before and after natural disasters optimizes the allocation and pre-position of resources, minimizing loss of life and expediting recovery. Understanding population propensities also informs media and messaging for public safety announcements before and after an event to improve their effectiveness. Spx.ai employed widely disparate data sets from both public safety data (traffic densities) and social media to quantify compliance with public safety advisories and assess their effectiveness. Spx.ai models considered, not only the media and messaging, but also metrics related to population fatigue with storm warnings late in the tropical storm season. Spx.ai behavioral models assessed where, when and how populations would move, and further correlated movement with the form and format of messaging.