Built by MongoDB: Qubitro Makes Device Data Accessible Anywhere it's Needed

Steve Jurczak
November 9, 2022 | Updated: January 4, 2023
#BuiltWithMongoDB #Startups

Increased cloud adoption and the expansion of 5G networks are expected to drive growth in IoT technologies over the next few years. Emergent IoT technologies are poised to transform businesses and the social fabric, including healthcare, smart homes and cities, and the government sector. Delaware-based startup, Qubitro, looks to capitalize on the potentially explosive growth in IoT technology by helping companies bring smart solutions to market faster. Qubitro, which is also a member of the MongoDB for Startups program, offers the fastest way of collecting and processing device data to activate it wherever it's needed.

Product vision

Qubitro founder and CEO, Beray Bentesen, estimates that there are now billions of devices producing massive amounts of data. The company's mission, he says, is to make device data accessible anywhere it's needed as fast as possible and at a lower cost than ever before.

By collecting device data from multiple networks and providing various developer toolkits for activating data in applications, Qubitro enables data-driven decision making and modern application development.

The company has two main products: the Qubitro Portal, a user interface where users can collaborate with other members or their internal team and create real-time actions such as rules and output integrations with their applications, and developer tools including APIs and SDKs that allow for custom solutions without having to develop data infrastructure from scratch.

Bentesen wants Qubitro to become the fabric of a digital transformation powered by device data. "We aim to make any data published from devices flow over our network and make any application that relies on device data to integrate with our services," Bentesen says.

The ideal Qubitro customer is one that needs to put device data into their solutions. "It could be startups, IoT-adopting enterprises, or custom solution providers," Bentesen says. The company has also been heavily investing in developer experience, he adds.

A platform to build upon

The secret to building a platform that can process data in milliseconds with privacy and user experience combined is, not surprisingly, another platform — specifically the MongoDB Atlas developer data platform. "We offer managed connectivity solutions, user interface, and the APIs," Bentesen says. "So we process tons of data. And MongoDB is in the middle of all those inputs and outputs."

The MongoDB for Startups program helps startups build faster and scale further with free MongoDB Atlas credits, one-on-one technical advice, co-marketing opportunities, and access to a vast partner network. Bentesen says the company has benefitted from being in the program a number of ways. "In the early days when we joined the program, we were able to get answers to questions that would take probably weeks or maybe more if you search on the internet," he says. "We were able to understand what to develop, which saved us a lot of time and of course expense."

The MongoDB Atlas platform also helps their developers during those crucial stages prior to launching a new feature and as the product grows in popularity. "With MongoDB Atlas, we could test our development environment before going to production," Bentesen says. "And as we scale, we're able to observe the traffic through MongoDB Atlas and optimize thanks to the tools MongoDB offers, like MongoDB Compass, without dealing with code or complex environments."

MongoDB's document model database made it an easy choice for the company's needs. "We decided to use MongoDB because it's a flexible environment," Bentesen says. "We knew we would have to build new features over time. So we needed to go with a flexible database. We're still adding more and more features without breaking the entire system. We wanted that flexibility, and in a managed cloud offering, which MongoDB gives us." Bentesen also cites MongoDB's Time Series collections as one of the features he's most excited about, since the vast majority of IoT solutions rely on time series data.

Looking forward

Bentesen says Qubitro will likely add more enterprise features in the future. The more they grow, he says, the more insight they're getting about what customers want. The company also plans to invest heavily in growing its community of users and, of course, attracting more talent. Bentensen says the company fully embraces the remote-first culture and believes they can work faster working remotely.

If you're looking forward to building the next generation of connected solutions, visit Qubitro.com, join the company's Discord server, or have a chat anytime, even weekends!

Are you part of a startup and interested in joining the MongoDB for Startups program? Apply now.

For more startups content, check out our previous blog on ChargeHub.

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Hybrid Cloud: Flexible Architecture for the Future of Financial Services

Financial services companies are reimagining how they apply technology to meet the growing service demands of a digital-first world. As they recognize the operational and competitive advantages of the public cloud, many companies are migrating their computing needs to it as quickly as possible. For an industry with tight regulations, a vast amount of private data, and complex legacy infrastructure, however, moving every workload to the cloud isn’t feasible just yet. Instead, some companies are moving to hybrid cloud, an architecture that enables them to use the public cloud wherever possible, while keeping those applications and data with tricky legal or reputational exposure on in-house systems. In this article, we’ll examine advantages of a hybrid cloud approach and outline steps to consider when preparing for such a shift. Overview Hybrid cloud integrates public cloud and on-premises infrastructure into a single functioning unit. Through the public cloud, institutions gain valuable versatility, agility, and scale to run applications more efficiently and to turbo-charge experimentation. They can use existing infrastructure to handle sensitive workloads — including those storing Personally Identifiable Information (PII) — within a familiar, time-tested environment. Deciding where to host applications is usually a function of a workload’s data secrecy and sovereignty requirements and an institution’s assessment of risks and opportunities related to them. Developing the technical flexibility to move between public and private infrastructures makes it easier to match those requirements to the environment best suited to fulfill them. Advantages to hybrid cloud A hybrid cloud approach offers many advantages. For example, institutions can use public cloud infrastructure for tasks with dynamic resource requirements, such as payments processing over holidays or risk calculations for end-of-month reporting. This setup can reduce the delays, data center overhead, and sunk costs associated with adding in-house servers, some of which may ultimately be used situationally or not at all. Companies also save on capital expenses and improve responsiveness to internal and external demands. A hybrid cloud setup can also help organizations address compliance, resilience, and performance needs. Those operating in multiple countries can use in-house and public cloud resources across different regions to satisfy disparate requirements around data sovereignty and residency. This geographic and infrastructure diversity can also enhance a company’s failover and disaster recovery profile. By co-locating applications in public cloud regions near customers, institutions can also improve service performance — an important factor as the industry moves toward mobile-first solutions. As institutions pursue more efficient ways to work, the insight gained through planning and executing a hybrid cloud strategy can help inform and transform an organization’s operations. Institutions can begin the shift to the continuous cadence of DevOps, DevSecOps, and MLOps teams by incorporating public cloud tools and methods. This approach includes using process automation and orchestration tools to streamline delivery and maintenance, and management applications to free up in-house IT resources from undifferentiated work. The following section describes other ways a hybrid approach can encourage changes to institutional conventions. Rethinking budgeting Although fixed infrastructure costs and investments can limit an organization’s flexibility, most companies still budget for fixed costs and may find the usage-based billing of public cloud services unnerving. Making the shift from the transparency and stability of capital expenses for on-premises infrastructure to the unpredictability of operating expenses in public-cloud procurement requires an organizational adjustment. Vendors do offer cost-management tools to help budget for and accommodate these changes. Hybrid cloud can help ease this transition as the organization moves into an infrastructure-as-a-service model. Expanding a security mindset The financial services sector is a high-target industry for cyberattacks. Data loss and leakage are also significant concerns. Organizations, therefore, often struggle to transfer any control over security and system integrity to a third party, and disparate regulations increase those hurdles. Sometimes, though, organizations overestimate the effectiveness of their in-house security teams and underestimate the security capabilities of the largest cloud providers who, like banks, are charged with deflecting the most sophisticated attacks all day, every day. Cloud services providers and other third-party vendors invest heavily in security research and resources. They regularly certify to the highest compliance standards. They’re also constantly developing new solutions to help institutions bridge gaps in their homegrown security measures and team capabilities. The result is that the security capabilities of the public cloud providers are often more advanced than those of in-house teams. Simplifying infrastructure complexity The largest financial institutions with the greatest global coverage face the biggest challenges in building a hybrid architecture. What’s more, sunk investments in on-premises infrastructure can make it cost- and ROI-prohibitive to shift workloads to the public cloud. An architecture that can support a hybrid of public cloud, on-premises cloud, and bare-metal deployments offers a flexible solution to address this complexity. Preparing for the shift As with any big shift in technology, the move to hybrid presents a set of challenges that are as much cultural and operational as they are technical. Preparation for a hybrid cloud project, therefore, must include organizational readiness assessments across functions. It must take into consideration not just the technical, business, and monetary impact but also the legacy mindset and organizational rituals that can jeopardize the best-laid technology strategies. In pursuing a hybrid cloud strategy, institutions can begin to modernize outdated operating principles as they transform their approach to technology. Given the high uptake within the industry, the steps to adopt a public cloud-only model are well-documented. In a hybrid cloud approach, however, lack of expertise in integrating public and private cloud technologies is a frequent challenge. This, coupled with staff who may be reluctant to adopt unfamiliar technology, can create resistance among technology teams. Early successes with high management attention can create excitement; for a wider adoption, strong central platform support through the infrastructure team, as well as training and transparency, can help staff get on board. Other effective ways for an organization to prepare for a hybrid cloud future include setting clear business and technical goals, creating inventories of data and applications, and evaluating how customers might react to changes in responsiveness and security brought about by the switch to hybrid. Assessing in-house skills and managing transformation anxiety of existing technical staff are also crucial to team preparedness. The following steps can help financial institutions prepare for a hybrid approach. Know your company and your customer Set goals: Companies that articulate clear goals for their hybrid strategy are more likely to achieve them. These goals might include gains in operating efficiency, more flexible development, cost savings, speed of innovation, IT resiliency, or regulatory flexibility. Evaluate your customer profiles: Retail and institutional customers require different services and protections from their financial institutions. An understanding of these needs and concerns should inform any analysis of the potential for a hybrid cloud implementation. The storage of PII, for example, demands special consideration. Profile your assets: Financial institutions house data and applications that perform business functions. Understanding these in a regulatory context, from a commercial perspective and through a technical lens, will influence decisions about how best to optimize them in a hybrid cloud environment. Blend private and public cloud: To decrease effort, organizations should reduce differences between the two deployment methods. This aim is crucial for a successful adoption. Initiatives that require teams to manually request assets on the private cloud usually fail. Build your team Engage stakeholders: An effective hybrid cloud strategy engages functions across the enterprise. It incorporates business, legal, IT, and security priorities into a comprehensive plan. Engaging compliance officers and security professionals early on is critical, as compliance and system safeguards must be woven into the DNA of any hybrid cloud plan from the outset.. Assess skills and educate the team: At the start of a journey to hybrid cloud, organizations often lack the expertise and mindset to confidently shift to a new model. Simply understanding the myriad services offered by public cloud providers can be daunting. Combining public and private clouds in a hybrid setup requires another whole level of up-skilling. Evaluating in-house teams to determine education and training needs is essential for the new paradigm. Foster transparency: In any effective cloud strategy, transparency across the organization is crucial to gaining buy-in, just as education is crucial to building skills. A cloud adoption team can ensure that the training and cultural needs of the organization are met alongside financial, customer, and business imperatives. Engaging your cloud provider(s) in this process can help. Map your migration Start small: Small proofs of concept build confidence and allow teams to expand incrementally on the back of those successes.. Manage risk: Start with a low-risk approach. For example, organizations may choose to move workloads that are highly dynamic and less sensitive first. These might include some customer-facing apps that contain little PII. Institutions often start with retail applications, while still running their institutional-focused applications within their on-premises data centers. This approach may change over time as they become more comfortable with public cloud security and managing a hybrid environment. Moving to hybrid Financial services institutions are already adapting to greater demands for innovation and efficiency by designing responsive IT environments and taking advantage of the public cloud — and often, hybrid cloud is a crucial part of that pathway. A hybrid cloud strategy is a great solution to help organizations to meet their technical and business objectives more cost-efficiently and effectively than with either a public or private cloud alone. A hybrid cloud approach offers the flexibility that institutions need to meet rapidly changing customer demands as well as competition from a new wave of challengers. As best practices become clear and more implementation lessons emerge, the industry will further embrace hybrid cloud as an important step in an evolution to a fully managed multi-cloud solution. Finding the right partners, of course, is crucial. Experienced teams and the best technical solutions greatly increase the odds of executing a successful hybrid strategy. The team at MongoDB offers solutions and advice to help financial institutions progress toward a more functional, flexible, and future-forward enterprise technology platform. To learn more about how MongoDB can help you on your cloud adoption journey, check out the following resources: Finance, Multi-Cloud, and The Elimination of Cloud Concentration Risk How Financial Services Achieve A Strategic Advantage With Data-Driven Disruption The Road to Smart Banking The 5-Step Guide to Mainframe Modernization for Banks

November 3, 2022

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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