The COVID-19 pandemic tested our nation’s public benefits system in unimaginable ways. With an unprecedented 60 million individuals turning to unemployment and social services to satisfy their basic needs, state and local governments were stretched to meet the demand.

But state and local government leaders have risen to the challenge of providing their constituents with critical services. They created and administered innovative solutions, complete with wholly original processes, and brought desperately needed employment, cash, food, and healthcare support to families in crisis. These public sector heroes raised the bar during this critical time and facilitated economic recovery for our local communities, making the impossible possible for its people. 

When state and local government agencies partner with Google Cloud, they can reimagine how they deliver human services and labor services for their communities. All in a matter of weeks, instead of months or years.

Tools that solve for remote work and service delivery hurdles

Never before was there such a need for modern and accessible tools to allow a workforce to secure new jobs and operate remotely. The Virtual Career Center (VCC), built on Google Cloud, was designed with these needs in mind.

Google Workspace, including Google Meet, allows job seekers to schedule video meetings with career coaches, job recruiters, and potential employers. The Google Job Search API will enable them to explore career opportunities best suited to their skills and interests.

A thousand miles away, the Rhode Island Virtual Career Center was helping its job seekers by offering virtual meetings with career coaches, the ability to schedule meetings with prospective employers, and help on building effective resumes. Skipper, the CareerCompass RI Bot, an intelligent agent for careers, uses data and machine learning to facilitate potential new career paths and reskilling opportunities for Rhode Islanders.

In addition to enabling remote work, Google Workspace can support remote service delivery, a critical need for those involved in health and human services programs. Engaging with individuals and families is an essential component for more of these programs, including eligibility determination, assessment, and service delivery. The ability to engage directly with the public through virtual interviews, counseling sessions, telehealth, etc. improves access for those on both sides of the screen. Further, through Google Classroom, a component of Google Workspace for Education, foster and adoptive parents can obtain virtual training required for licensing. 

Tools to expedite relief funds and employment assistance

As the number of people applying for unemployment assistance skyrocketed, so did the backlog of claims requiring review. The pandemic also saw a rise in fraudulent claims, which caused delays for families waiting on legitimate payments and resulted in unnecessary spending for government agencies. Realizing the magnitude of the challenge was beyond the agencies’ current resources, and they shifted their focus to machine learning.  

To address the massive backlog of claims, SpringML partnered with Google Cloud to develop the Improper Payment Analytics solution that successfully leverages AI to help agencies identify fraudulent claims and avoid improper payments so aid can be prioritized for those who need it. Though such a tool would typically take months to develop, the team was able to launch it in just weeks, to the benefit of all stakeholders involved. Families awaiting legitimate payments received checks more quickly, and state and local governments saved millions in spending.

For instance, to address erroneous payments totaling $330 million to fraudulent applicants, the State of Ohio partnered with Google Cloud to utilize artificial intelligence and machine learning solutions to proactively identify and decline improper payment risks. As a result, the State avoided paying fraudulent claims and accelerated the payment of legitimate benefits to families facing financial hardship.

The New York State Department of Labor launched a streamlined unemployment application to allow residents to apply for pandemic unemployment assistance without the added burden of applying for unemployment insurance. Success was immediate, and the application backlog plummeted as New Yorkers got the financial aid they desperately needed. Illinois took a different approach, deploying Contact Center Artificial Intelligence (CCAI) to create virtual agents who assist with specialized calls 24/7, in multiple languages, providing turn-by-turn guidance in real-time. Conversations can be turned into insights through analytics and reporting tools that uncover key call drivers and customer sentiment. By solving for spikes in call volume, CCAI has helped process more than 1 million unemployment claims.

Automated data solutions bring immediate food and cash assistance resources

With millions out of work and out of school during the pandemic, food and cash assistance became a critical need. Applying for all of these benefits requires significant documentation that, in most cases, has historically been manually processed. Given the volume of people in need, this took a massive amount of human resources and time.

Using Document AI (DocAI), agencies can automate this highly manual process and speed up the delivery of critical benefits to individuals and families. DocAI extracts the key data, provides a confidence score in a single review pane for staff review, and automatically uploads that data to the case management system–significantly reducing manual processing. Today, these state and local government organizations turn to DocAI to eliminate the difficult paperwork application process to help meet the benefit needs of their residents now and tomorrow, as illustrated by both the Wisconsin Department of Workforce Development (DWD) and the State of Hawaii.

The Wisconsin DWD streamlined their paper unemployment insurance claims using DocAI by enabling DWD staff to receive critical data extracted from submitted applications and make decisions rapidly, saving time for both applicants and staff. The State of Hawaii also used DocAI to extract, interpret, and transport COVID-19 test result data of incoming travelers to Google Cloud instantly. As a result, Hawaii was able to welcome travelers and reopen its economy in the midst of the pandemic.

Partner with us

After making it through this very difficult period, agencies have emerged from the pandemic with stronger labor and health and human service delivery for their residents. State and local governments recognized the necessity of virtual engagement and expanded access to services to meet this critical moment, solving for work, cash, food and healthcare needs.

To learn more about Google Cloud for human services and labor, watch our video on the power of customer innovation or contact your Google Cloud sales representative. Let’s get solving together.

AgroStar has launched a cloud-based mobile app that is helping to boost crop yields and encourage best practices for small farmers in India. Launched as an on-premises ecommerce platform selling farm tools in 2008, the firm turned to Google Cloud Platform (GCP) to expand its offering. It now uses cloud-based analytics and is deploying ML models to provide timely advice in five languages on everything from seed optimization, crop rotation, and soil nutrition to pest control.

A 2018 survey underscored the demand for agricultural planning for Indian farmers. While farming remains a dominant sector in India, employing half of its labor force, 70 percent of small farmers – those cultivating fewer than three acres – said their crops are damaged by unforeseen weather and pests. An even higher number – 74 percent – say they lack access to farming-related information.

Widening that gap is the relative lack of access to new, higher yield seeds and improved soil analyses for small farmers, who must otherwise rely on traditional methods. “It could take a few years for innovative information to trickle down from universities to small, grassroots farmers,” says Pritesh Gudge, AgroStar Software Engineer. “Today, just by clicking through our Android application, farmers learn about new, effective farming practices and receive advice customized to their crop and soil.”

Connecting a million farmers in the cloud

Operating in the Indian states of Gujarat, Maharashtra, Rajasthan, Orissa, Bihar, and Karnataka, AgroStar is closing the knowledge gap with a full-service, cloud-based SaaS solution – the only one of its kind in India. It combines agronomy, data science, and analytics to help farmers by providing a variety of resources.

AgroStar has reached over a million farmers through its Android app, the AgroStar Agri-Doctor. The mobile client is available as a web-based or full-featured native app. Both provide access to the firm’s knowledge base hosted on GCP, a Q&A forum that connects farmers to each other to help understand and better solve problems and to learn about innovative practices and products. Farmers can also click through to follow local and national market trends that help forecast crop prices.

In addition to the self-service knowledge base, AgroStar provides access to agronomy experts who use cloud-based analytics tools and historical data to provide season-and locale-specific advice to each farmer. “We are now tracking thousands of calls in 5 languages each day,” says Pritesh.

The AgroStar app also provides links to purchase and then track the delivery of farm tools and supplies such as cultivators and fertilizers. An in-house platform manages fulfillment centers and a doorstep delivery network simplifies the supply chain while giving farmers what they need, when they need it. By procuring directly from the manufacturers and primary distributors of farm supplies, Agrostar is achieving cost savings, which it passes on to farmers.

Build fast, pivot faster

From the start, the human and environmental variables of farming in India, not to mention the volume of AgroStar’s few hundred thousand monthly active users, made a highly scalable cloud-based solution inevitable. Farmers rely on the firm’s Agri-Doctor app to provide advice in multiple languages on topics that range widely throughout three growing seasons, each with distinct crop nutrition and rotation cycles and farm implementation requirements.

“For farmers, the focus keeps changing every month, and every season,” says Pritesh. “To serve our growing community, we needed a platform that could process images at high volume, fulfill tools and seed orders across thousands of miles, and respond to multilingual queries. We quickly moved away from spreadsheets and server-based solutions – we needed to build fast and pivot faster.”

Ending late-night deployments

The firm’s first cloud experience was with an AWS solution. At the time, AWS was the only cloud provider in India, but AgroStar wanted to find a solution that was easier to use and offered better integration with Android devices. “Deployment and processing costs were very high, and the developer tools and documentation were not as intuitive as we needed,” says Pritesh.

When GCP service arrived in India in October 2017, AgroStar embarked on a platform re-implementation that made possible dramatic changes in the way it developed and deployed its solution. Using Google Kubernetes Engine (GKE) for crop advice management and Compute Engine for its production application services, the firm built the backend for the Agri-Doctor discussion forum in only three weeks. The platform’s microservice architecture is implemented in Python and Golang and deployed on GCP.

AgroStar began to realize significant efficiencies in its build, deploy, and test cycles. “We previously needed to work overnight to deploy to production,” says Pritesh. “Now using Google for Kubernetes containers and a rolling update strategy, we can deploy during the day without any problems or interruptions to service.”

The move to GCP streamlined AgroStar’s stack. “We were running 12 independent instances on AWS,” says Pritesh. “With Google Kubernetes Engine, we are deployed on a single cluster at a cost savings of $1,300 per month and growing.”

Improving customer response times by 85 percent

With a managed deployment capability, AgroStar can devote more time and resources to executing on its platform and Agri-Doctor app development plan. A strategic goal was managing customer response times as the firm grew its base. GCP has helped the firm meet that goal, achieving an 85 percent improvement in customer response times even as traffic grew significantly.

“With our on-premises solution, we could handle around 100 customers daily, which took 30 to 50 minutes for each customer,” says Pritesh. “We now handle thousands of customers daily, taking only 4 to 5 minutes for each one.”

AgroStar used Firebase to implement its Agri-Doctor app. A real-time cloud database, Firebase provides an API that enables the Agri-Doctor advice forum to be synchronized across all its far-flung mobile clients, effectively sharing knowledge base updates with one million users in near real time.

Using cloud tools to manage and monitor

Cloud Pub/Sub, Kafka, and Cloud Dataflow manage data ingestion and queueing of event and transaction data to the analytics layer. BigQuery fetches and persists data to Cloud Storage. Cloud SQL and dashboards powered by Tableau deliver farmer crop and soil profiles within minutes.

Cloud IAM helps AgroStar control access to all its cloud resources. And Stackdriver, the integrated logging aggregation capability for GCP, helps monitor and speed debugging on every tier of the AgroStar solution.

Machine learning to enhance yields

AgroStar is developing a variety of ML components to improve responsiveness and extend its platform offerings.

To speed up the diagnosis of and treatment for crop blight, AgroStar is building a deep learning pipeline using TensorFlow. The pipeline relies on GoogLeNet models that use multi-layered convolutional visual pattern recognition. It will assess uploaded images to support a disease-detection capability on the mobile app. Based on the commercially successful AI algorithms that automated postal code processing, GoogLeNet offers improved performance and computational efficiencies by using a creative layering technique that distinguishes them from older, sequential recognition engines.

To improve its customer search experience, AgroStar is developing an ML pipeline that shrinks fetch times by suggesting tags mapped to stored data. Processed using TPUs, Cloud Natural Language and Video AI, the tags provide a metadata layer that supports queries in any of the ten natural languages that AgroStar farmers can use.

The AgroStar search pipeline consists of Long Short-Term Memory (LSTM) models of Recurrent Neural Networks. Recurrent networks exhibit “memory” through iterative processing and are distinguished from feedforward networks by a feedback loop connected to their past decisions, ingesting their own outputs moment after moment as input.

Implementing a recommendation engine

The firm is also adapting the Random Forests TensorFlow AI model to develop a crop and product recommendation engine. The model is trained by consuming numerical (rainfall, humidity, water availability per acre) and categorical (soil type, water sources) parameters to suggest appropriate products by season, region, and locale.

To simplify the product suggestion experience, AgroStar developers are testing Cloud Dialogflow, the Google Cloud conversational interface, to build a chatbot capability into its mobile app. The bot will track a farmer’s crop schedules and answer simple questions by linking to the recommendation engine.

AgroStar is also extending its analytics platform with AI-powered sales planning and forecasting. Using linear regression models implemented in TensorFlow and powered by Cloud ML Engine, the capability will enhance supply chain logistics as the company scales its operations across India.

To provide a credit on-demand offering for a range of seed-to-harvest cycle products, AgroStar is attempting to use Vision API to create an AI model that will convert uploaded photos of customer application records into standard data formats. The firm’s credit policy features a grace period in which farmers begin paying back loans after harvested crops go to market.

A versatile and friendly development ecosystem

AgroStar credits the convivial tools and documentation that GCP offers and its incremental, pay-as-you-go pricing model for both the firm’s success and its ability to manage growth.

“What Google Cloud offers is extremely good documentation and extremely simple-to-use tools and interfaces across all services,” says Pritesh. “It helped us initially deploy our platform and at every scale that we have required since then, and its cost effectiveness enabled us to staff up to meet new feature milestones.”