What’s your Developer Persona?

Emily Sawicki
April 15, 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!

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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 .

April 11, 2024

Next →

Building AI with MongoDB: Conversation Intelligence with Observe.AI

What's really happening in your business? The answer to that question lies in the millions of interactions between your customers and your brand. If you could listen in on every one of them, you'd know exactly what was up--and down. You’d also be able to continuously improve customer service by coaching agents when needed. However, the reality is that most companies have visibility in only 2% of their customer interactions. Observe.AI is here to change that. The company is focused on being the fastest way to boost contact center performance with live conversation intelligence. Founded in 2017 and headquartered in California, Observe.AI has raised over $200m in funding. Its team of 250+ members serves more than 300 organizations across various industries. Leading companies like Accolade, Pearson, Public Storage, and 2U partner with Observe.AI to accelerate outcomes from the frontline to the rest of the business. The company has pioneered a 40 billion-parameter contact center large language model (LLM) and one of the industry’s most accurate Generative AI engines. Through these innovations, Observe.AI provides analysis and coaching to maximize the performance of its customers’ front-line support and sales teams. We sat down with Jithendra Vepa, Ph.D, Chief Scientist & India General Manager at Observe.AI to learn more about the AI stack powering the industry-first contact center LLM. Can you start by describing the AI/ML techniques, algorithms, or models you are using? “Our products employ a versatile range of AI and ML techniques, covering various domains. Within natural language processing (NLP), we rely on advanced algorithms and models such as transformers, including the likes of transformer-based in-house LLMs, for text classification, intent and entity recognition tasks, summarization, question-answering, and more. We embrace supervised, semi-supervised, and self-supervised learning approaches to enhance our models' accuracy and adaptability." "Additionally, our application extends its reach into speech processing, where we leverage state-of-the-art methods for tasks like automatic speech recognition and sentiment analysis. To ensure our language capabilities remain at the forefront, we integrate the latest Large Language Models (LLMs), ensuring that our application benefits from cutting-edge natural language understanding and generation capabilities. Our models are trained using contact center data to make them domain-specific and more accurate than generic models out there.” Can you share more on how you train and tune your models? “In the realm of model development and training, we leverage prominent frameworks like TensorFlow and PyTorch. These frameworks empower us to craft, fine-tune, and train intricate models, enabling us to continually improve their accuracy and efficiency." "In our natural language processing (NLP) tasks, prompt engineering and meticulous fine-tuning hold pivotal roles. We utilize advanced techniques like transfer learning and gradient-based optimization to craft specialized NLP models tailored to the nuances of our tasks." How do you operationalize and monitor these models? "To streamline our machine learning operations (MLOps) and ensure seamless scalability, we have incorporated essential tools such as Docker and Kubernetes. These facilitate efficient containerization and orchestration, enabling us to deploy, manage, and scale our models with ease, regardless of the complexity of our workloads." "To maintain a vigilant eye on the performance of our models in real-time, we have implemented robust monitoring and logging to continuously collect and analyze data on model performance, enabling us to detect anomalies, address issues promptly, and make data-driven decisions to enhance our application's overall efficiency and reliability.” The role of MongoDB in Observe.AI technology stack The MongoDB developer data platform gives the company’s developers and data scientists a unified solution to build smarter AI applications. Describing how they use MongoDB, Jithendra says “OBSERVE.AI processes and runs models on millions of support touchpoints daily to generate insights for our customers. Most of this rich, unstructured data is stored in MongoDB. We chose to build on MongoDB because it enables us to quickly innovate, scale to handle large and unpredictable workloads, and meet the security requirements of our largest enterprise customers.” Getting started Thanks so much to Jithendra for sharing details on the technology stack powering Observe.AI’s conversation intelligence and MongoDB’s role. To learn more about how MongoDB can help you build AI-enriched applications, take a look at the MongoDB for Artificial Intelligence page. Here, you will find tutorials, documentation, and whitepapers that will accelerate your journey to intelligent apps.

April 29, 2024