What’s your Developer Persona?

Emily Sawicki
April 15, 2024 | Updated: May 8, 2024

Cool stuff is getting built with MongoDB every day. Behind the scenes are developers, data scientists, and UX designers who use our products in truly innovative ways. We believe developers are doing way more than just writing code and we’ve outlined a few personas we commonly see.

Pick your Archetype for custom backgrounds and other shareable content!

The architect

Ideal team member: The Storyteller. Together you’d be unstoppable.

Description
People are usually intimidated by you at first. You’ll start all quiet and casually drop how you created the very board game everyone is playing at the dinner party you’re at. You have a knack for strategy and probably have a pretty good poker face.

Under your calm and collected demeanor is a perpetual motion machine. Scientists said it couldn’t be done but it’s happening inside your brain right now. It’s so effortless you probably don’t even notice it. You’re deeply intuitive and you have a unique combination of both big ideas and informed action.

The scientist

Ideal team member: The Architect

Description
You love a healthy debate. Not in the devil’s advocate way but you genuinely like hearing both sides (though you're likely always right). When starting a project, you’re already thinking about things to research so you can hit the ground running. You’re always up to date with the newest technology and constantly thinking about how you can implement it in your workflow. You’re a lifelong learner and you’re super sharp, which makes you an asset to any team.

The storyteller

Ideal team member: The Scientist

Description
You’re deeply interested in the human experience. When people enter your house they are wowed by how beautiful and well thought out everything is. You also probably greet them with a seasonally spiced espresso.

You have very niche-specific hobbies like crafting modern candlesticks for your friends. People come to you when they want someone to use their product or idea - you know how to craft an experience everyone will keep coming back to! Beauty is in the eye of the beholder, but you’re the beholder and you always know what’s best.

The visionary

Ideal team member: The Sprinter, they can ground you.

Description
Leaders are constantly asking you for opinions if you aren’t one yet. You’re interested in the big stuff that will change how people think and interact with the world around them.

Your dinner party conversation is strong and you pride yourself on being able to predict the next big thing. Some people may write you off as arrogant until that thing you swore to them would happen, finally did, and they text you saying, “Did you see the news?!?!? You called it!”. You’re uber confident and for good reason. You know exactly how to, as they say, strike while the iron is hot!

The sprinter

Ideal team member: The Grasshopper

Description
Your desk is full of half-drunk red-bulls. Or maybe not, but that’s what everyone assumes. You outpace all your colleagues because you simply love getting things done. We imagine you’re into low-effort activities in your spare time like bouldering or Ironman triathlons.

You are often the one people call when they want something done right but they are too lazy to do it themselves. You love a good checklist and you're probably the one who does all the work on that new startup venture you and your college friends have. You are deeply passionate about your work and think this meeting could have been an email.

The grasshopper

Ideal team member: The Architect, you’ll learn a lot!

Description
No is not in your vocabulary. You’re somewhat of a Renaissance person, people are usually in awe of how you can already be so natural at something you just started. You have a unique blend of independent and collaborative energy that touches everything you set your mind to.

You get things done in style. Your unique brand of optimism makes everything you do look effortless. Some people may write you off as a newbie. Don’t worry though, they’re probably just grumpy. The world is truly your oyster, the sky’s the limit, and whatever other saying there is out there you’ve probably thought of 10 already.

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Transforming Industries with MongoDB and AI: Insurance

This is the fifth in a six-part series focusing on critical AI use cases across several industries . The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries. With its ability to streamline processes, enhance decision-making, and improve customer experiences in far less time, resources, and staff than traditional IT systems, artificial intelligence offers insurers great promise. In an inherently information-driven industry, insurance companies ingest, analyze, and process massive amounts of data. Whether it’s agents and brokers selling more policies, underwriters adequately pricing, renewing and steering product portfolios, claim handlers adjudicating claims, or service representatives providing assurance and support, data is at the heart of it all. Given the volumes of data, and the amount of decision-making that needs to occur based on it, insurance companies have a myriad of technologies and IT support staff within their technology investment portfolios. It’s no surprise that AI is at the top of the list when it comes to current or prospective IT investments. With its ability to streamline processes, enhance decision-making, and improve customer experiences with far less time, resources, and staff than traditional IT systems, AI offers insurers great promise. Underwriting & risk management Few roles within insurance are as important as that of the underwriters who strike the right balance between profit and risk, bring real-world variables to the actuarial models at the heart of the insurer, and help steer product portfolios, markets, pricing, and coverages. Achieving equilibrium between exposures and premiums means constantly gathering and analyzing information from a myriad of sources to build a risk profile sufficient and detailed enough to make effective policy decisions. While many well-established insurers have access to a wealth of their own underwriting and claims experience data, integrating newer and real-time sources of information, keeping up with regulatory changes, and modeling out what-if risk scenarios still involve significant manual effort. Perhaps the single greatest advantage of AI will be its ability to quickly analyze more information with fewer people and resources. The long-term impact will likely be profound, and there is tremendous promise within underwriting. Advanced analytics: Traditional IT systems are slow to respond to changing formats and requirements surrounding data retrieval. The burden falls on the underwriter to summarize data and turn that into information and insight. Large Language Models are now being leveraged to help speed up the process of wrangling data sources and summarizing the results, helping underwriting teams make quicker decisions from that data. Workload and triage assistance: AI models are mitigating seasonal demands, market shifts, and even staff availability that impact the workload and productivity of underwriting teams, saving underwriting time for high-value accounts and customers where their expertise is truly needed. Amid high volumes for new and renewal underwriting, traditional AI models can help classify and triage risk, sending very low-risk policies to ‘touchless’ automated workflows, low to moderate risk to trained service center staff, and high-risk and high-value accounts to dedicated underwriters. Decision-making support: Determining if a quoted rate needs adjustment before binding and issuing can take significant time and manual effort. So can preparing and issuing renewals of existing policies, another large portion of the underwriters’ day-to-day responsibilities. Automated underwriting workflows leveraging AI are being employed to analyze and classify risk with far less manual effort. This frees up significant time and intellectual capital for the underwriter. Check out our machine learning solutions page to learn more about automated digital underwriting. Vast amounts of data analyzed by underwriters are kept on the underwriter's desktop rather than IT-managed databases. MongoDB offers an unparalleled ability to store data from a vast amount of sources and formats and deliver the ability to respond quickly to requests to ingest new data. As data and requirements change, the Document Model allows insurers to simply add more data and fields without the costly change cycle associated with databases that rely on single, fixed structures. For every major business entity found within the underwriting process, such as a broker, policy, account, and claim, there is a wealth of unstructured data sources, waiting to be leveraged by generative AI. MongoDB offers insurers a platform that consolidates complex data from legacy systems, builds new applications, and extends those same data assets to AI-augmented workflows. By eliminating the need for niche databases for these AI-specific workloads, MongoDB reduces technology evaluation and onboarding time, development time, and developer friction. Claim processing Efficient claim processing is critical for an insurer. Timely resolution of a claim and good communication and information transparency throughout the process is key to maintaining positive relationships and customer satisfaction. In addition, insurers are on the hook to pay and process claims according to jurisdictional regulations and requirements, which may include penalties for failing to comply with specific timelines and stipulations. To process a claim accurately, a wealth of information is needed. A typical automobile accident may include not only verbal and written descriptions from claimants and damage appraisers but also unstructured content from police reports, traffic and vehicle dashboard cameras, photos, and even vehicle telemetry data. Aligning the right technology and the right amount of your workforce in either single or multi-claimant scenarios is crucial to meeting the high demands of claim processing. Taming the flood of data: AI is helping insurers accelerate the process of making sense of a trove of data and allowing insurers to do so in real-time. From Natural Language Processing to image classification and vector embedding, all the pieces of the puzzle are now on the board for insurers to make a generation leap forward when it comes to transforming their IT systems and business workflows for faster information processing. Claims experience: Generating accurate impact assessments for catastrophic events in a timely fashion to inform the market of your exposure can now be done with far less time, and with far more accuracy, by cross-referencing real-time and historical claims experience data, thanks to the power of Generative AI and vector-embedding of unstructured data. Claim expediter: Using vector embeddings from photo, text, and voice sources, insurers are now able to decorate inbound claims with richer and more insightful metadata so that they can more quickly classify, triage, and route work. In addition, real-time insight into workload and staff skills and availability is allowing insurers to be even more prescriptive when it comes to work assignments, driving towards higher output and higher customer satisfaction. Litigation assistance: Claims details are not always black and white, parties do not always act in good faith, and insurers expend significant resources in the pursuit of resolving matters. AI is helping insurers drive to resolution faster and even avoid litigation and subrogation altogether, thanks to its ability to help us analyze more data more effectively and more quickly. Risk prevention: Many insurers provide risk-assessment services to customers using drones, sensors, or cameras, to capture and analyze data. This data offers the promise of preventing losses altogether for customers and lowering exposures, liability, and expenses for the insurer. This is possible thanks to a combination of vector-embedding, traditional, and generative AI models. Learn more about AI-enhanced claim adjustment for automotive insurance on our solutions page. Customer experience Accessing information consistently during a customer service interaction, and expecting the representative to quickly interpret it, are perennial challenges with any customer service desk. Add in the volume, variety, and complexity of information within insurance, and it’s easy to understand why many insurers are investing heavily in the transformation of their customer experience call center systems and processes. 24/7 virtual assistance: As with many AI-based chat agents, the advantage is that it can free up your call center staff to work on more complex and high-touch cases. Handling routine inquiries can now include far more complex scenarios than before, thanks to the power of vector-embedded content and Large Language Models. Claims assistance: Generative AI can deliver specific claim-handling guidelines to claim-handling staff in real time, while traditional ML models can interrogate real-time streams of collected information to alert either the customer or the claim-handler to issues with quality, content, or compliance. AI capabilities allow insurers to process more claims more quickly and significantly reduce errors or incomplete information. Customer profiles: Every interaction is an opportunity to learn more about your customers. Technologies such as voice-to-text streaming, vector embedding, and generative AI help insurers build out a more robust ‘social profile’ of their customers in near real-time. Real-time fraud detection: According to estimates from the Coalition Against Insurance Fraud , the U.S. insurance industry lost over $308 billion to fraud in 2022. With vector-embedding of unstructured data sources, semantic and similarity searches across both vector and structured metadata, and traditional machine learning models, insurers can detect and prevent fraud in ways that were simply not ever before possible. Other notable use cases Predictive Analytics: AI-powered predictive analytics can anticipate customer needs, preferences, and behaviors based on historical data and trends. By leveraging predictive models, insurers can identify at-risk customers, anticipate churn, and proactively engage with customers to prevent issues and enhance satisfaction. Crop Insurance and Precision Farming: AI is being used in agricultural insurance to assess crop health, predict yields, and mitigate risks associated with weather events and crop diseases, which helps insurers offer more accurate and tailored crop insurance products to farmers. Predictive Maintenance for Property Insurance: AI-powered predictive maintenance solutions, leveraging IoT sensors installed in buildings and infrastructure, are used in property insurance to prevent losses and minimize damage to insured properties. Usage-Based Insurance (UBI) for Commercial Fleets: AI-enabled telematics devices installed in commercial vehicles collect data on driving behavior, including speed, acceleration, braking, and location. Machine learning algorithms analyze this data to assess risk and determine insurance premiums for commercial fleets to help promote safer driving practices, reduce accidents, and lower insurance costs for businesses. Learn more about AI use cases for top industries in our new ebook, How Leading Industries are Transforming with AI and MongoDB Atlas. Read the full ebook here . Head over to our quick-start guide to get started with Atlas Vector Search today.

April 11, 2024

Next →

Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana

For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies, the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.

November 21, 2024