Application
Tracking System
The Issue
Our Federal Client had been receiving thousands of applications for a particular program every year. However, the application process was complex and Involved several checkpoints and validations that were not apparent to the applicants. As a result, applicants were often left in the dark about the status of their application, and the wait times were long, ranging from months to over a year. In addition, rejection notifications were not always consistent, and the method of commutation was not always clear. This lack of transparency and communication resulted in a high number of manual requests to retrieve the status of the application, which placed a significant burden on the department's staff
The Solution
To address these issues, our team implemented an online portal that allowed applicants to apply for the program and enabled automatic validations. Additionally we piloted a tracking mechanism that provided real-time updates to the applicants. The mechanism was integrated with the customer-facing portal, allowing applications to see the current state of their application, whether more information was needed, and the time elapsed from the beginning. This provided immediate and on-demand update to the applicants and reduced the number of manual requests fro status updates. The customer-facing portal also reduced the burden on the departments staff , who no longer had to retrieve requested status updates manually.
The Result
Overall, the implementation of the online portal and the tracking mechanism significantly improved the application process for the Federal Client. Applicants were no longer left in the dark about the status of their application and the burden on the department's staff was greatly reduced. The new system allowed for greater transparency and communication , which ultimately resulted in a more efficient and effective application process.
Integration
Platform
The Issue
Our team was engaged to help design and implement a hybrid integration platform for a federal department that was trying to combine on-remise and cloud-based integration tools and services to a create a unified platform. It enables organizations to connect different systems, applications , and data sources , whether they are hosted on-premises or in the cloud, and exchanged data in real-time.
The Solution
This platform is playing a critical role in enabling digital transformation initiatives by streamlining business processes and improving data accessibility. It helps connect disparate systems, applications, and data sources, creating a unified view of data across the organization. This enables faster decision-making and provides greater insights into business operations.
The Result
AWS was selected provide the underlying infrastructure for OpenShift. OpenShift provides an enterprise-grade solution for deploying and managing containerized applications. It enables organizations to build, deploy and scale applications in a flexible and scalable environment, making it an ideal component of a hybrid integration platform. By using aWS to host OpenShift, organizations can benefit form the scalability , reliability, and security of the AWS platform. They can leverage AWS services such as EC2 , EBS, and ELB to provide a scalable and highly available infrastructure for hosting containerized applications.
Authority to Operate
A Canadian Federal Government Agency recognized the need for an enhanced client interaction system and implemented the Amazon Connect Call Center solution. This initiative aimed to revolutionize the department's approach to customer service, leveraging innovative technologies to streamline processes and boost customer satisfaction.
Implementation: The Amazon Connect Call Center introduced Natural Language Processing (NLP) and Natural Language Routing (NLR) to intelligently triage questions and direct calls to Level 1 agents equipped with a strong foundation of information. Sentiment analysis was integrated to predict caller queries, allowing for proactive and personalized responses. The call center utilized data retrieval based on historical information, ensuring a first-time resolution for customers. Expert assistance was seamlessly provided through a callback mechanism.
Performance Metrics: Before the introduction of AWS Connect, Customer Satisfaction (CSAT) at the Canadian Federal Government Agency fluctuated between 20% and 30%. A trial was conducted following the implementation to assess the impact on CSAT. The results were compelling, showcasing a significant increase in customer satisfaction. This transformation underscored the success of the Amazon Connect Call Center in enhancing the overall client experience.
Sentiment Analytics and NLP: A noteworthy aspect of the call center's operation is its use of sentiment analytics, enabling the team to provide quicker and more tailored responses to customer queries. This deployment of Natural Language Processing serves as a beacon for how advanced technologies can be harnessed to elevate customer service standards.
Technological Framework:
The foundation of the Amazon Connect Call Center rests on AWS Connect, a suite of services that includes a Proof of Concept (POC) and an Efficient Triage question and mind map. Collectively, these components contribute to the call center's dynamic and efficient functioning. The POC serves as a testing ground, allowing the Canadian Federal Government Agency to validate the effectiveness of the implemented solutions. The Efficient Triage question and mind map ensure callers are promptly directed to the most relevant information or assistance.
Outcomes and Future Prospects
Implementing the Amazon Connect Call Centre at the Canadian Federal Government Agency marks a pivotal moment in the department's commitment to client satisfaction. The notable increase in CSAT numbers following the AWS Connect implementation demonstrates the solution's effectiveness. Looking ahead, they may explore further innovations in sentiment analytics and NLP to continue refining customer interactions and ensure sustained positive outcomes.
ChatBot
Background: A Canadian Federal Government Agency encountered a substantial challenge related to the usability of its website and the discoverability of resources, resulting in user dissatisfaction. In response, Chatbot Alpha (Anonymized), a chatbot developed by the Agency, was introduced as a solution. Alpha aimed to enhance the user experience by serving as a triage, identifying the specialists required for tax-related inquiries.
Implementation: Chatbot Alpha was built on AWS (Amazon Web Services) services, utilizing S3 as its building block. A pre-built chatbot module was employed to interact with users, helping on a limited set of topics related to tax inquiries. Released in 2021, Alpha's capabilities include answering FAQs, routing users to specialists, and providing additional information. It operates 24/7 and is accessible from the Canadian Federal Government Agency's homepage.
Challenges: The Canadian Federal Government Agency faced a multifaceted customer-facing problem involving the accessibility of information on the web, tax collection, and user engagement. Navigating the website posed difficulties for users, leading to a reluctance to explore its content. Moreover, the broader Security Assessment and Authorization (SA&A) use case highlighted the potential threat from nation-states aiming to undermine the Canadian Federal Government Agency. The Agency recognized the universal challenge of being a target for those wanting to take it down.
Secure and Customer-Friendly Approach: In response to these challenges, Chatbot Alpha was designed to be secure yet customer-friendly. It operates without tracking or enabling hate mail, prioritizing user privacy. The chatbot's primary focus is to solve tax-related problems, aligning with the Agency's commitment to security and effective customer service.
Technological Framework: Built on AWS Chatbot, Alpha leverages the cloud infrastructure for scalability and flexibility. It employs AWS services such as Help Desk (Connect), FAQs, Routing, and Menu of services to enhance its capabilities. Additionally, Chatbot Alpha incorporates third-party tools, including machine learning, to optimize its performance.
Assessment and Deployment: The Agile pilot mode for Chatbot Alpha lasted six months, with a February 2021 release. The assessment phase took another month, involving rigorous controls, evidence review, and documentation. The prototype's development on AWS made it adaptable to multi-cloud environments, highlighting its potential as a versatile solution for building chatbots.
Outcomes
Chatbot Alpha successfully addressed the customer-facing problem by providing a user-friendly alternative to navigating the Canadian Federal Government Agency website. The solution's ability to direct users to the correct information and specialists improved the overall customer experience. The secure design and cautionary measures against sharing sensitive information ensure privacy and data protection.
Future Considerations
While the case study provides insights into Chatbot Alpha's initial success, exploring avenues for continuous improvement and expansion could enhance its effectiveness further. This might involve incorporating more advanced machine learning capabilities, expanding the range of topics covered, or integrating user feedback mechanisms to refine its responses over time.
Azure DevOps with a Legacy
LNine designed and built a unique proprietary proof of concept (PoC) prototype of an Enterprise client utilizing the front-end of SalesForce that integrated seamlessly with a Siebel ERP system. The primary objectives were to enhance usability, scalability, and efficient interfacing with other crucial, legacy technologies.
The team faced some technological challenges when some APIs did not meet their objectives. They overcame these challenges by redesigning and iteratively redeveloping the PoC prototype, deploying ETL processes to extract data from Siebel, transform it, and load it into Salesforce. The team then further improved the system by designing and developing data cleansing and mapping adjustment algorithms, as well as error handling and data filtering. These enhancements allowed the team to handle specific error scenarios gracefully and either skip problematic records or log them for further investigation.
Overall, the team was able to successfully implement the system, overcoming the challenges and achieving their primary objectives of enhancing usability, scalability, and efficient interfacing with other crucial technologies.
SalesForce PoC
LNine designed and built a unique proprietary proof of concept (PoC) prototype of an Enterprise client utilizing the front-end of SalesForce that integrated seamlessly with their Siebel ERP system. The primary objectives were to enhance usability, scalability, and efficient interfacing with other crucial, legacy technologies.
The team conducted research to determined the best approach to this integration across 300 data points and which methodologies would be deployed. We designed and iteratively developed a custom-built IT system. Some of the technological challenges we faced were when some APIs did not meet their objectives. They redesigned and iteratively redeveloped the PoC prototype deploying ETL processes to extract data from Siebel, transform it, and load it into Salesforce.
The team then overcome the failure rate through the design and development of data cleansing and mapping adjustments algorithms. They redesigned and iteratively redeveloped the PoC prototype and conducted a series of systematic experiments and tests that were mostly successful. They also designed, developed, and deployed error handling and data filtering to handle specific error scenarios gracefully and either skip problematic records or log them for further investigation.
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