MongoDB and Clarity Business Solutions: Enabling Modernization for Public Sector Clients

Prasad Pashte and TJ Greenier
June 23, 2022 | Updated: December 12, 2022
#Modernization

Cloud-based transformation is now a must-have for federal agencies. And, in partnership with Clarity Business Solutions, MongoDB is making that transformation easier for government agencies, particularly those that work in closed, air-gapped environments and that require security clearances for their support staff. In this article, we’ll look at specific ways MongoDB and Clarity Business Solutions are working together to support public sector clients.

Cloud challenges

IT teams within government agencies want many of the same cloud benefits that their colleagues in the private sector enjoy: better performance, the ability to outsource the management of their infrastructure, and a path to building more resilient applications.

And government leaders, at the national level, are pushing for more cloud adoption. A May 2021 executive order from President Joe Biden called on all federal agencies to “accelerate movement to secure cloud services.” In the mission to support U.S. troops, Danielle Metz, the U.S. Department of Defense’s Deputy CIO for Information Enterprise, said, “It all comes down to harnessing the power of cloud compute and then being able to natively build applications continuously and often in that space.”

For government agencies, security is an overriding concern. However, some of the precautions that enable the highest levels of security also make it more difficult to keep applications up to date, to modernize them, and to move them to the cloud.

Government agencies often work in closed networks, without access to the internet. The need for security clearances makes it difficult for agencies to take advantage of support from software companies, consultants, and other members of the technology ecosystem. Even personnel with appropriate security clearances aren’t always allowed to go on-site to assist their government clients.

These extra layers of security can also make it difficult for MongoDB to support public sector clients that require top secret clearances. Now, we’re pleased to announce that our ability to service these clients has been enhanced through our partnership with Clarity Business Solutions, a software and systems engineering company that focuses on data analytics, processing, and data flow.

Clarity specializes in working with the federal government and understands the constraints under which government agencies operate — as well as the requisition procedures and security protocols unique to the federal government. The company has experience working in closed, air-gapped environments, and all but two of Clarity’s employees hold security clearances.

Joint solutions

In the first phase of our partnership, MongoDB and Clarity are jointly offering three unique solutions to better support our public sector clients:

  • Application modernization

  • Government Support

  • Rapid start

Let’s look at each of these solutions in turn.

Application modernization

Together, Clarity and MongoDB offer public sector clients a proven, iterative approach to application modernization. Clarity’s security clearances allow them to sit side-by-side with public sector clients when necessary, and Clarity has deep experience with the environments common to government agencies.

  • Clarity and MongoDB leverage a strategic process for analyzing legacy applications and modernizing and migrating them iteratively, rather than trying to update an entire system in a “big bang” approach.

  • This iterative approach allows clients to modernize without downtime.

  • Legacy and modernized systems can run in parallel for a period of time, enabling troubleshooting and increasing confidence.

  • Clarity and MongoDB combine the power of the MongoDB developer data platform with Clarity’s extensive client domain knowledge. This partnership allows teams to focus on the application feature development and quickly get the data platform operational.

Public sector clients often operate systems with significant accumulated technical debt. Clarity and MongoDB are partnering to help clients increase efficiency, improve performance and scalability, and optimize maintenance as each monolithic application is modernized, rather than waiting years for an entire system to be replaced.

Government support

MongoDB and Clarity Business Solutions are offering a concierge support service specifically for the public sector.

  • Government Support engages U.S.-only technical staff, with appropriate clearances, to provide phone, online, or even on-site support for MongoDB government customers.

  • Government Tier Support provides continuity between call-in support and support offered by individuals with on-site clearance.

  • Clarity Government Support engineers are tightly integrated with the MongoDB support team and can rely on the expertise of the broader MongoDB engineering organization while ensuring that all necessary details remain confidential.

  • Service-level agreements are twice the MongoDB published response times for commercial support.

Rapid start

This new service helps public sector clients get operational with MongoDB as quickly and efficiently as possible. This intense, short engagement ensures the following:

  • Networking layouts are optimized and secured using appropriate firewall rules and TLS to encrypt all data in transit.

  • Data storage is set up to meet applications’ needs.

  • Backup and recovery are properly enabled.

  • Agencies have the proper guidance to achieve environment security requirements. For example, data at rest is encrypted according to client requirements through disk encryption and/or MongoDB’s encrypted storage engine. Data in use can be protected with client-side field-level encryption.

Additionally, Clarity engineers can consult and provide input on schema design, leveraging key MongoDB features and working with training staff on the best practices for using MongoDB.

We believe these three new offerings will significantly ease the way for our government clients, enabling them to make the best use of MongoDB and cloud technologies and to better serve their end customer — all of us.

Learn more about Clarity Business Solutions.

For more information, check out our Solutions Briefs:

← Previous

How Telcos Are Transforming to Digital Services Providers

The telecommunications industry is in the midst of a digital revolution, shifting from a traditional service delivery model to one that is increasingly customer-centric and that extends beyond the provision of traditional connectivity services to include diverse digital services. Telcos undergoing this modernization journey are digital services–focused first, offering apps, streaming services, retail platforms, peer-to-peer payment platforms, and more. As telcos delve into the complex 5G, IoT, and AI technologies powering personalized and real-time user experiences, pressure is increasing on aging networks and business support system (BSS) infrastructures. MongoDB customers like TIM and Telefónica are using the MongoDB Atlas developer data platform to deliver a robust platform-focused experience that complements existing technologies. Through an integrated modernization approach, telcos are improving both customer and developer experiences, building innovative new applications. In a recent roundtable discussion , Boris Bialek , MongoDB global head of industry & solutions, sat down with telco IT leaders Paolo Bazzica , head of digital solutions at Italy’s TIM, and Carlos Carazo , global CTO of Spain’s Telefónica Tech IoT and Big Data division. This article provides an overview of the discussion and insights into how platform thinking is invigorating telco IT teams. From communications services providers to digital services providers The shifting value chain in telecommunications. Source: Kearney The shift and expansion from traditional communications services to a comprehensive digital services suite requires global telecommunications companies to rethink their monetization strategies. Even before the pandemic, an evolution was well underway for telecommunications providers. From 2010 to 2020, overall revenue coming from connectivity services grew by only 2%, according to research compiled by Kearney. During the same period, digital services experienced a five-fold increase. Although telecommunications providers successfully sparked a revolution that grew into a $6.3 trillion digital economy, only those capitalizing on digital services reaped the benefits. In 2020, digital services like e-commerce and online advertising surged, capturing nearly 80% of growth. Leveraging platform thinking As network operators evolve to digital service providers, the idea of platform thinking is rippling across the industry. Network connectivity was tested with the hardships of the March 2020 COVID-19 lockdown in Italy, but TIM’s digital platform project Fly Together , which was initiated in 2018, helped bridge the divide. “People went from their normal lives to a full lockdown in one day. People realized that telco was a key point, because you need to stay at home, but you still need to communicate to work and go to school,” said Bazzica in the virtual roundtable discussion hosted by MongoDB. “Our digital platform was the way to refill or top up your account, and access ebooks and so on, so I think it’s more than just an evolution for the business; it's a different positioning.” Today, customer trust is a key differentiator and essential focus for TIM. People rely on TIM’s services to keep the country going. And TIM continues to modernize the digital experiences of its customers through the Fly Together platform. “From my perspective, this is definitely a trend, and I think it’s the evolutionary stalwart of the digital life of the people to be relevant and continue to be their trusted partner,” Bazzica said. A similar dynamic led to the creation of Telefónica Tech two years ago, a division of Spain’s Telefónica SA, according to Carazo. The new business is split into two units: one dedicated to offering cloud or cybersecurity solutions and the other offering IoT or big data digital services, which are the services customers need to pursue their own digital transformations. “We are strongly convinced that connectivity is the basis for any new digital economy, so we are really proud to offer connectivity for these customers,” Carazo said. At the center of Telefónica Tech’s transformation is its Kite Platform , run on MongoDB, which is a managed connectivity platform running close to 30 million IoT devices all over the world. The platform provides connectivity, but it goes beyond IoT connectivity and provides multidimensional benefits across all IoT environments from the devices to the product connecting the clouds. This is the foundational component of Telefónica Tech’s portfolio, which delivers new business use cases across industries. Modernizing applications and evolving to microservices and APIs How can a telco simplify this complex journey to modernization? For TIM, the change was driven by a desire to modernize 700 different applications before effectively going into the digital business. TIM launched Fly Together to build a digital layer that serves the scalability and latency needed to transform customers’ digital service experiences. Before, a customer could be querying up to 14 systems, depending on which apps were open. Without the digital experience layer, you can’t express an SLA or determine how long it takes to open an app, according to Bazzica. The first task of Fly Together was to build the layer that decoupled the backend systems from the model that helps run TIM’s digital channels. Through its work with MongoDB over the past four years, TIM launched a resilient platform that doesn’t require exotic hardware to run efficiently. Because the platform was developed in a cloud-native environment, it comprises containerized microservices and RESTful APIs, setting a new standard for the company’s development of applications. “We are able to modernize, but gradually. We still have our mainframe running,” Bazzica said. “The real experience is seeing the company learning and experimenting. That’s another value with this type of technology; we can try a lot of different things with minimum effort and make big discoveries.” Four digital services trends to watch IoT is driving many exciting use cases for Telefónica Tech’s new business division. Within the B2B sector, there is healthy growth across four key industry use cases, according to Telefónica’s Carazo. Connected Industry and IoT — Telefónica starts with providing private network solutions. These technologies are expected to evolve to more complex use cases like robotics and predictive maintenance in small and medium factories within the next five years. Smart metering — Massive growth is expected in smart metering, which uses electronic devices to measure energy consumption. The implementation of this trend could spur demand for millions of connected devices. Connected cars — This sector is expected to grow significantly in the next five to 10 years as operators deploy new digital services like infotainment, security, and safety applications. Smart cities — Cities around the world are seeking services for their digital citizens looking to live in more sustainable and flexible communities. These use cases are critical to building modern cities, societies, and industries. Platform thinking and an integrated approach to modernization will help telcos create modern applications, extending their businesses beyond conventional services to include novel digital services. Watch our webinar to learn more about TIM and Telefónica’s transformation to digital services providers.

June 22, 2022
Next →

Retrieval Augmented Generation for Claim Processing: Combining MongoDB Atlas Vector Search and Large Language Models

Following up on our previous blog, AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases , we’ll pick up the conversation right where we left it. We discussed extensively how Atlas Vector Search can benefit the claim process in insurance and briefly covered Retrieval Augmented Generation (RAG) and Large Language Models (LLMs). MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More One of the biggest challenges for claim adjusters is pulling and aggregating information from disparate systems and diverse data formats. PDFs of policy guidelines might be stored in a content-sharing platform, customer information locked in a legacy CRM, and claim-related pictures and voice reports in yet another tool. All of this data is not just fragmented across siloed sources and hard to find but also in formats that have been historically nearly impossible to index with traditional methods. Over the years, insurance companies have accumulated terabytes of unstructured data in their data stores but have failed to capitalize on the possibility of accessing and leveraging it to uncover business insights, deliver better customer experiences, and streamline operations. Some of our customers even admit they’re not fully aware of all the data in their archives. There’s a tremendous opportunity to leverage this unstructured data to benefit the insurer and its customers. Our image search post covered part of the solution to these challenges, opening the door to working more easily with unstructured data. RAG takes it a step further, integrating Atlas Vector Search and LLMs, thus allowing insurers to go beyond the limitations of baseline foundational models, making them context-aware by feeding them proprietary data. Figure 1 shows how the interaction works in practice: through a chat prompt, we can ask questions to the system, and the LLM returns answers to the user and shows what references it used to retrieve the information contained in the response. Great! We’ve got a nice UI, but how can we build an RAG application? Let’s open the hood and see what’s in it! Figure 1: UI of the claim adjuster RAG-powered chatbot Architecture and flow Before we start building our application, we need to ensure that our data is easily accessible and in one secure place. Operational Data Layers (ODLs) are the recommended pattern for wrangling data to create single views. This post walks the reader through the process of modernizing insurance data models with Relational Migrator, helping insurers migrate off legacy systems to create ODLs. Once the data is organized in our MongoDB collections and ready to be consumed, we can start architecting our solution. Building upon the schema developed in the image search post , we augment our documents by adding a few fields that will allow adjusters to ask more complex questions about the data and solve harder business challenges, such as resolving a claim in a fraction of the time with increased accuracy. Figure 2 shows the resulting document with two highlighted fields, “claimDescription” and its vector representation, “claimDescriptionEmbedding” . We can now create a Vector Search index on this array, a key step to facilitate retrieving the information fed to the LLM. Figure 2: document schema of the claim collection, the highlighted fields are used to retrieve the data that will be passed as context to the LLM Having prepared our data, building the RAG interaction is straightforward; refer to this GitHub repository for the implementation details. Here, we’ll just discuss the high-level architecture and the data flow, as shown in Figure 3 below: The user enters the prompt, a question in natural language. The prompt is vectorized and sent to Atlas Vector Search; similar documents are retrieved. The prompt and the retrieved documents are passed to the LLM as context. The LLM produces an answer to the user (in natural language), considering the context and the prompt. Figure 3: RAG architecture and interaction flow It is important to note how the semantics of the question are preserved throughout the different steps. The reference to “adverse weather” related accidents in the prompt is captured and passed to Atlas Vector Search, which surfaces claim documents whose claim description relates to similar concepts (e.g., rain) without needing to mention them explicitly. Finally, the LLM consumes the relevant documents to produce a context-aware question referencing rain, hail, and fire, as we’d expect based on the user's initial question. So what? To sum it all up, what’s the benefit of combining Atlas Vector Search and LLMs in a Claim Processing RAG application? Speed and accuracy: Having the data centrally organized and ready to be consumed by LLMs, adjusters can find all the necessary information in a fraction of the time. Flexibility: LLMs can answer a wide spectrum of questions, meaning applications require less upfront system design. There is no need to build custom APIs for each piece of information you’re trying to retrieve; just ask the LLM to do it for you. Natural interaction: Applications can be interrogated in plain English without programming skills or system training. Data accessibility: Insurers can finally leverage and explore unstructured data that was previously hard to access. Not just claim processing The same data model and architecture can serve additional personas and use cases within the organization: Customer Service: Operators can quickly pull customer data and answer complex questions without navigating different systems. For example, “Summarize this customer's past interactions,” “What coverages does this customer have?” or “What coverages can I recommend to this customer?” Customer self-service: Simplify your members’ experience by enabling them to ask questions themselves. For example, “My apartment is flooded. Am I covered?” or “How long do windshield repairs take on average?” Underwriting: Underwriters can quickly aggregate and summarize information, providing quotes in a fraction of the time. For example, “Summarize this customer claim history.” “I Am renewing a customer policy. What are the customer's current coverages? Pull everything related to the policy entity/customer. I need to get baseline info. Find relevant underwriting guidelines.” If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: RAG for claim processing GitHub repository From Relational Databases to AI: An Insurance Data Modernization Journey Modernize your insurance data models with MongoDB and Relational Migrator

April 18, 2024