Influencing Product Strategy at MongoDB with Garaudy Etienne

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","datePublished":"2024-11-25T15:56:00.000Z","dateModified":"2024-11-25T15:59:51.207Z","mainEntityOfPage":{"@type":"WebPage","url":"https://mongodb.com/blog/post/influencing-product-strategy-at-mongodb-garaudy-etienne"},"image":["https://webassets.mongodb.com/_com_assets/cms/Screenshot 2024-11-21 at 8.33.07 AM-dwcb3hebku.png"],"publisher":{"@type":"Organization","name":"MongoDB","logo":{"@type":"imageObject","url":"https://webassets.mongodb.com/_com_assets/cms/mongodb_logo1-76twgcu2dm.png"}},"author":{"@type":"Person","name":"Garaudy Etienne"},"inLanguage":"English","articleBody":""}</script> <script async type="module" src="https://static.mongodb.com/blog/index.acc86020813f2359483f5a713d5f0581.js"></script> <noscript> <style> .card { opacity: 1 !important; transform: none !important } </style> </noscript> <link href="https://cdnjs.cloudflare.com/ajax/libs/prism/1.19.0/themes/prism-tomorrow.min.css" rel="stylesheet" /> <script defer src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.19.0/prism.min.js"></script> <script defer src="https://cdnjs.cloudflare.com/ajax/libs/prism/1.19.0/plugins/autoloader/prism-autoloader.min.js"></script> </head> <body class="body"> <noscript><iframe style="display:none" src="https://www.googletagmanager.com/ns.html?id=GTM-GDFN&nojscript=true"></iframe></noscript> <div id="mongo-app"> <div style="background-color: white; height: 70px;"></div><main class="app"><div class="relative w-full overflow-hidden fl fl-center-x fl-wrap"><header class="header relative fl fl-center fl-column w-full z-index-10 m-b-10 p-20 h-360"><div class="absolute top left w-full h-full" style="background-image: url("https://webassets.mongodb.com/_com_assets/cms/Screenshot 2024-11-21 at 8.33.07 AM-dwcb3hebku.png"); background-size: cover; background-position: center; background-repeat: no-repeat;"></div><div class="absolute top left w-full h-full" style="background-color: rgba(0, 0, 0, 0.4);"></div><h1 class="relative white w-max-800 m-h-20 m-t-0 m-b-10 txt-center font-35 font-mobile-25">Influencing Product Strategy at MongoDB with Garaudy Etienne </h1></header><div class="relative p-h-20 p-v-30 w-full w-max-800"><div class="fl fl-wrap fl-center-y m-b-20"><div class="fl fl-wrap p-v-10"><a href="/blog/authors/garaudy-etienne"><div class="relative m-r-10 w-50 h-50 b-r-full" style="background-image: url("https://webimages.mongodb.com/_com_assets/cms/kynf8pwz9wumasopd-Screen%20Shot%202022-01-20%20at%201.22.31%20PM.png?auto=format%252Ccompress"); background-size: cover; background-position: center;"></div></a></div><div><small><a href="/blog/authors/garaudy-etienne">Garaudy Etienne</a></small><br /><small class="post-date">November 25, 2024</small><br /><a href="/blog/tags/culture"><small class="m-r-10 tag">#Culture</small></a></div></div><div class="cms-content h2-30"><p><i>Garaudy Etienne joined MongoDB as a Product Manager in October of 2019. Since then, he’s experienced tremendous growth. Successful deliveries of MongoDB 4.4 features and MongoDB 5.0 sharding features helped fuel Garaudy’s career development, as did his work establishing a long-term sharding vision, mentoring others, and successfully managing interns. Now, as a Director of Product, he’s defining the strategic direction across multiple products and helping grow our product management organization and culture. Read on to learn more about Garaudy’s experience at MongoDB and his expanding team.</i></p> <figure> <div class="fl-center"> <img src="https://webassets.mongodb.com/_com_assets/cms/Screenshot 2024-11-21 at 8.33.07 AM-dwcb3hebku.png" alt=" " title=" " style="width: 750px"/> </div> <figcaption class="fl-center"> </figcaption> </figure> <h2>A team with impact</h2> <p>My team focuses on distributed systems within MongoDB's core database functions, also known as the database engine. Our team ensures the database is reliable and scalable for our most demanding customers. We ensure the product consistently performs as promised, especially at scale. MongoDB's dependability drives greater usage, which enhances our revenue and brand perception.</p> <p>The problems my team works on are vast and relatively undefined. These include revamping our Go-To-Market strategy for new and existing features, guiding the engineering team on architectural decisions driven by customer demands, identifying target markets, and assisting customers in challenging situations.</p> <h2>MongoDB and AI</h2> <p>We’re in the early stages of the AI boom. MongoDB’s document model is particularly well-suited for this era, as it excels in handling unstructured data, which makes up the majority of today’s information. As AI increasingly relies on diverse formats like text, images, and videos, our flexible schema enables efficient storage and retrieval of unstructured data, enabling applications to extract valuable insights. Our <a href="https://www.mongodb.com/products/platform/atlas-vector-search" target="_blank">vector search</a> capability enables fast, complex data matching and retrieval, making it ideal for AI-powered applications. This synergy between MongoDB’s document model plus Vector Search and the needs of AI-driven applications positions us as a powerful foundation for companies looking to enable AI into their workflows.</p> <p>The beauty of working in the core database is that it has to support every workload, including the new and expanding Vector Search applications. This means we need to ensure the database remains robust and scalable as AI demands evolve. Some examples are helping develop a more scalable architecture for Search or a new networking stack for Search. No matter what new capabilities MongoDB decides to deliver or the new markets we enter, everything must pass through the core database. This also allows you to meet lots of people and understand everything the company is doing instead of working in a silo.</p> <h2>A rewarding career in product</h2> <p>MongoDB is committed to career development, something I’ve experienced first-hand. The company has provided me with development opportunities through product management-specific training with Reforge, conferences, direct engagement with critical customers, and leadership training. As a product manager, I was offered mentorship and coaching with multiple experienced product leaders who provided guidance and support as I worked toward promotions. The company clearly communicates the expectations and requirements for advancement within the product management organization.</p> <p>Reflecting on my journey at MongoDB, I still remember the first two features I PM’d: Hedged Reads and Mirrored Reads. One of my first major highlights was presenting at the MongoDB 5.0 keynote to showcase resharding. Seeing genuine excitement from customers and internal teams about this new feature was incredibly fulfilling and reinforced its value. While the keynote was a public milestone, another personal highlight came when I finally visited one of my engineering teams in Barcelona after nearly two years of remote collaboration. This in-person time was invaluable and helped us bring the groundbreaking sharding changes for MongoDB 6.0 to the finish line.</p> <p>Most recently, defining the key strategic pillars for MongoDB 8.0 and allowing other product managers to take ownership of key initiatives has been more rewarding than I imagined. MongoDB’s engineering team is extremely talented, and collaborating with them always brings me tremendous joy. The most recent highlight of my career has been building a diverse product team and helping other product managers make a larger impact than they previously envisioned.</p> <h2>Why MongoDB</h2> <p>What keeps me at MongoDB is the opportunity to tackle significant challenges, make autonomous decisions, own multiple products, and take on greater leadership responsibilities. MongoDB also rewards and recognizes product managers who drive meaningful impact across the organization and its products. If these opportunities excite you, you'll thrive as part of MongoDB’s product management team!</p> <p>For my team, I’m committed to providing the right balance of guidance and autonomy. Your decisions will have a lasting impact at the executive and organizational levels, creating continuous opportunities to excel and deliver meaningful results. Plus, I always try to make the job fun.</p> <div class="callout"> <p><b>Head to our <a href="https://www.mongodb.com/company/careers/teams/product-management-and-design" target="_blank">careers site</a> to apply for a role on Garaudy’s team and <a href="https://www.gem.com/form?formID=a13ca4d9-9234-4a80-9b4d-f7ba8d60cd75" target="_blank">join our talent community</a> to stay in the loop on all things #LifeAtMongoDB! </b></p> </div> </div><div class="w-full fl fl-center m-v-20"><div class="w-full w-max-300" style="height: 1px; background: #D9E0E0;"></div><div class="fl fl-center p-20"><a target="_tab" href="https://www.facebook.com/sharer.php?u=https://www.mongodb.com/blog/post/influencing-product-strategy-at-mongodb-garaudy-etienne"><button aria-label="Share on facebook" class="fl fl-center white clickable h-35 w-35 m-h-5 b-r-full reset bg-green hover-fadein-half hover-lift icon-facebook"></button></a><a target="_tab" href="https://www.linkedin.com/shareArticle?mini=true&url=https://www.mongodb.com/blog/post/influencing-product-strategy-at-mongodb-garaudy-etienne&title=Influencing Product Strategy at MongoDB with Garaudy Etienne &summary=Learn more about product management at MongoDB from Director of Product Management, Garaudy Etienne. &source=MongoDB"><button aria-label="Share on linkedin" class="fl fl-center white clickable h-35 w-35 m-h-5 b-r-full reset bg-green hover-fadein-half hover-lift icon-linkedin"></button></a><a target="_tab" href="https://reddit.com/submit?url=https://www.mongodb.com/blog/post/influencing-product-strategy-at-mongodb-garaudy-etienne&title=Influencing Product Strategy at MongoDB with Garaudy Etienne "><button aria-label="Share on reddit" class="fl fl-center white clickable h-35 w-35 m-h-5 b-r-full reset bg-green hover-fadein-half hover-lift icon-reddit"></button></a><a target="_tab" href="https://twitter.com/intent/tweet?url=https://www.mongodb.com/blog/post/influencing-product-strategy-at-mongodb-garaudy-etienne&text=Influencing Product Strategy at MongoDB with Garaudy Etienne "><button aria-label="Share on twitter" class="fl fl-center white clickable h-35 w-35 m-h-5 b-r-full reset bg-green hover-fadein-half hover-lift icon-twitter"></button></a></div><div class="w-full w-max-300" style="height: 1px; background: #D9E0E0;"></div></div><div class="related-posts fl fl-wrap fl-justify-between p-v-20"><div class="card-container relative w-full w-max-360 h-475 "><div class="absolute" style="width: 1px; height: 1px; top: -350px; background: transparent;"></div><div class="absolute" style="width: 1px; height: 1px; bottom: -350px; background: transparent;"></div><div class="card reset relative clickable w-full h-full w-max-360 b-r-4 bg-white box-shadow transition-ease-out-4 overflow-hidden" style="opacity: 0; transform: translate(0, 15px); transition: 300ms; transition-delay: 0ms;"><a href="/blog/post/with-mongodb-atlas-italian-telemedicine-born-it" target="_self" class="reset w-full h-full" style="display: inline-block;"><div class="h-200 w-full overflow-hidden"><div class="bg h-full w-full" style="background: linear-gradient(180deg, rgba(17,97,73,0.6) 0%, rgba(10,29,41,0.6) 100%), 50% 50% / cover; transform: null;"></div><div class="absolute left top p-20 white"><strong>← Previous</strong></div></div><div class="p-20 h-max-220"><h3 class="m-0 fnt-20 relative line-clamp line-clamp-3">Innovazione e salute: MongoDB a supporto della Piattaforma Nazionale di Telemedicina</h3><p class="relative line-clamp line-clamp-3"> La medicina del futuro sarà molto più “tele” e meno fisica? Potremo monitorare la nostra salute senza spostamenti e senza sale d’attesa? Scopriamolo insieme ai protagonisti italiani di questa rivoluzione, costruita su importanti investimenti e tecnologie innovative. PNT Italia è la società di progetto costituita da Engineering e Almaviva, a cui AGENAS (Agenzia Nazionale per i Servizi Sanitari Regionali) ha affidato in concessione la progettazione, realizzazione e gestione della Piattaforma Nazionale di Telemedicina, che integra i servizi regionali per migliorare la qualità e l’accesso alle cure in tutto il Paese, in linea con gli obiettivi indicati dal Piano di Ripresa e Resilienza in ambito Sanità Digitale. Formalmente, PNT Italia è una società costituita dal 60% da Engineering e dal 40% da Almaviva, due tra i più grandi Gruppi tecnologici italiani. Su concessione di AGENAS PNT Italia ha creato la Piattaforma Nazionale di Telemedicina, che gestirà per 10 anni con il compito di assicurare la governance e il monitoraggio centralizzati dei processi di telemedicina attuati a livello regionale, mettendo in comunicazione l’Amministrazione Centrale con le Amministrazioni locali. Per capire meglio il percorso che ci porterà alla telemedicina abbiamo intervistato tre protagonisti di questa iniziativa: Stefano Zema, responsabile sviluppo e delivery PNT del Gruppo Engineering, Daniele Fortuna, PNT Infrastructure Architect di Almaviva e Angelo Immediata, PNT Solution Architect di Engineering. “Il progetto, che ha un respiro decennale, è diviso in tre macro fasi” dice Stefano Zema, responsabile sviluppo e delivery PNT del Gruppo Engineering, “la prima, che è durata da maggio a dicembre 2023, è stata la progettazione e implementazione, conclusa con il collaudo della piattaforma. Ora ci aspettano due anni di avvio e consolidamento (2024-25) in cui la piattaforma dovrà adattarsi e integrare le soluzioni regionali di telemedicina, che sono anch’esse in via di sviluppo. La terza fase comprende la gestione vera e propria della piattaforma”. “Tra i team di Engineering e Almaviva”, prosegue Zema, “si è subito creato un clima coeso, favorito anche dall’affiancamento dei tecnici italiani di MongoDB e dal supporto internazionale. Gli obiettivi del progetto, aiutare la transizione digitale del Paese e consentire ai malati cronici di stare vicino ai propri familiari, erano talmente alti e sfidanti da creare un clima di entusiasmo e voglia di fare bene in tutta l’organizzazione. Le persone, il metodo, la coesione e l’entusiasmo sono stati ingredienti fondamentali per la riuscita di questa prima fase”. “L’obiettivo”, aggiunge Angelo Immediata, “è anche quello di aiutare il Governo a far capire a operatori e pazienti l’importanza della telemedicina. Per raggiungerlo ci sono tante strade. Quelle più efficaci sono la definizione di standard comuni, e l’implementazione di soluzioni di telemedicina efficaci per fare in modo che gli operatori aderiscano in modo naturale e rispettino le indicazioni previste. Per raggiungere questo scopo, PNT impiega tra le 100 e le 150 persone che risiedono in 14 diverse regioni italiane, con Engineering e Almaviva che conferiscono risorse al progetto in modo dinamico”. Un progetto complesso per un obiettivo alto La sfida che il gruppo si trova ad affrontare è articolata: nel breve periodo bisogna creare il nucleo di una piattaforma flessibile, sicura e interoperabile (per mettere in comunicazione i sistemi regionali e quello centrale). Nel medio periodo è necessario garantire stabilità, scalabilità e resilienza. Nel lungo periodo, invece, sarà fondamentale supportare gli operatori a diffondere la cultura di un’assistenza sanitaria centrata sui bisogni degli assistiti e dei loro familiari. “Abbiamo scelto”, spiega Fortuna, “di preferire un’infrastruttura cloud e un’architettura orientata agli eventi, oltre che pensare a una piattaforma aperta, che evitasse i lock-in e che permettesse a PNT di trarre il maggior vantaggio dal cloud pubblico”. “Abbiamo scelto MongoDB Atlas come database per diversi motivi”, dice Angelo Immediata, PNT Solution Architect di Engineering, “primo tra tutti la tipologia di dati da trattare, in standard HL7 FHIR (Fast Healthcare Interoperability Resources), perfettamente gestibili da MongoDB, poi la natura multicloud della tecnologia, la gestione della sicurezza e la scalabilità orizzontale per trattare grandi moli di dati.” I vantaggi di un ambiente aperto e scalabile PNT sceglie di realizzare una piattaforma orientata agli eventi, semplice da espandere e adattare, scalabile e flessibile. “Tutto lo stack tecnologico”, spiega Immediata, “è basato sui framework Java Spring per il back-end e Angular per il front-end. Nei flussi di dati dalle infrastrutture regionali al sistema centrale, le informazioni vengono anonimizzate ma non perdono il loro valore, assicurando così sicurezza e privacy, con l’obiettivo di una vulnerabilità pari a zero”. “La piattaforma è tutta in cloud su infrastrutture AWS”, dice Fortuna, “ma è stata progettata per non avere nessun lock-in. Siamo convintamente cloud native, ma non abbiamo voluto usare servizi nativi del provider; questo è stato uno dei motivi per cui abbiamo scelto MongoDB Atlas, che vive in SaaS e che è per definizione multi-ambiente”. Tra i servizi associati a MongoDB già utilizzati da PNT c’è Online Archive, che permette di sfruttare in modo intelligente gli spazi di archiviazione dei Terabyte di dati provenienti dalle strutture sanitarie, favorendo la sostenibilità economica e ambientale del progetto, ed Encryption at Rest, che proteggendo i dati a livello di DB consente di abbattere la complessità e favorire la compliance. Nel mirino per il prossimo futuro c’è poi la tecnologia Vector Search, associata all’adozione dell’AI Generativa. “Conclusa la prima fase”, dice Fortuna, “ora dedicheremo le nostre energie alla verifica e alla validazione delle piattaforme regionali” e, come rinforza Immediata, “Partiamo dal dato incoraggiante che con MongoDB l’effort per creare le istanze è circa la metà di quello necessario con altre tecnologie e che i tempi si calcolano in ore invece che in giorni.” Il team è pronto alle imminenti sfide, e a sfruttare la flessibilità del database considerando che alla fine del 2025 ci saranno oltre 300mila pazienti assistiti in telemedicina contemporaneamente. In un progetto lungo 10 anni, nessuno può sapere come evolverà la tecnologia; per questo è importante aver scelto un partner che ci permette di scegliere sempre le soluzioni migliori. MongoDB ci fa dormire tranquilli. È una società giovane, dinamica ma nello stesso tempo leader di mercato. Stefano Zema, responsabile sviluppo e delivery PNT, Gruppo Engineering Guarda come gestire in sicurezza i dati sanitari con MongoDB Atlas . </p><div class="absolute bottom left bg-white w-full fl fl-justify-between p-20"><small class="fnt-14">November 25, 2024</small></div></div></a></div></div><div class="card-container relative w-full w-max-360 h-475 "><div class="absolute" style="width: 1px; height: 1px; top: -350px; background: transparent;"></div><div class="absolute" style="width: 1px; height: 1px; bottom: -350px; background: transparent;"></div><div class="card reset relative clickable w-full h-full w-max-360 b-r-4 bg-white box-shadow transition-ease-out-4 overflow-hidden" style="opacity: 0; transform: translate(0, 15px); transition: 300ms; transition-delay: 0ms;"><a href="/blog/post/ai-powered-call-centers-new-era-of-customer-service" target="_self" class="reset w-full h-full" style="display: inline-block;"><div class="h-200 w-full overflow-hidden"><div class="bg h-full w-full" style="background: linear-gradient(180deg, rgba(17,97,73,0.6) 0%, rgba(10,29,41,0.6) 100%), 50% 50% / cover; transform: null;"></div><div class="absolute right top p-20 white"><strong>Next →</strong></div></div><div class="p-20 h-max-220"><h3 class="m-0 fnt-20 relative line-clamp line-clamp-3">AI-Powered Call Centers: A New Era of Customer Service</h3><p class="relative line-clamp line-clamp-3"> Customer satisfaction is critical for insurance companies. Studies have shown that companies with superior customer experiences consistently outperform their peers. In fact, McKinsey found that life and property/casualty insurers with superior customer experiences saw a significant 20% and 65% increase in Total Shareholder Return , respectively, over five years. A satisfied customer is a loyal customer. They are 80% more likely to renew their policies, directly contributing to sustainable growth. However, one major challenge faced by many insurance companies is the inefficiency of their call centers. Agents often struggle to quickly locate and deliver accurate information to customers, leading to frustration and dissatisfaction. This article explores how Dataworkz and MongoDB can transform call center operations. By converting call recordings into searchable vectors (numerical representations of data points in a multi-dimensional space), businesses can quickly access relevant information and improve customer service. We'll dig into how the integration of Amazon Transcribe, Cohere, and MongoDB Atlas Vector Search—as well as Dataworkz's RAG-as-a-service platform— is achieving this transformation. From call recordings to vectors: A data-driven approach Customer service interactions are goldmines of valuable insights. By analyzing call recordings, we can identify successful resolution strategies and uncover frequently asked questions. In turn, by making this information—which is often buried in audio files— accessible to agents, they can give customers faster and more accurate assistance. However, the vast volume and unstructured nature of these audio files make it challenging to extract actionable information efficiently. To address this challenge, we propose a pipeline that leverages AI and analytics to transform raw audio recordings into vectors as shown in Figure 1: Storage of raw audio files: Past call recordings are stored in their original audio format Processing of the audio files with AI and analytics services (such as Amazon Transcribe Call Analytics ): speech-to-text conversion, summarization of content, and vectorization Storage of vectors and metadata: The generated vectors and associated metadata (e.g., call timestamps, agent information) are stored in an operational data store Figure 1: Customer service call insight extraction and vectorization flow Once the data is stored in vector format within the operational data store, it becomes accessible for real-time applications. This data can be consumed directly through vector search or integrated into a retrieval-augmented generation (RAG) architecture, a technique that combines the capabilities of large language models (LLMs) with external knowledge sources to generate more accurate and informative outputs. Introducing Dataworkz: Simplifying RAG implementation Building RAG pipelines can be cumbersome and time-consuming for developers who must learn yet another stack of technologies. Especially in this initial phase, where companies want to experiment and move fast, it is essential to leverage tools that allow us to abstract complexity and don’t require deep knowledge of each component in order to experiment with and realize the benefits of RAG quickly. Dataworkz offers a powerful and composable RAG-as-a-service platform that streamlines the process of building RAG applications for enterprises. To operationalize RAG effectively, organizations need to master five key capabilities: ETL for LLMs: Dataworkz connects with diverse data sources and formats, transforming the data to make it ready for consumption by generative AI applications. Indexing: The platform breaks down data into smaller chunks and creates embeddings that capture semantics, storing them in a vector database. Retrieval: Dataworkz ensures the retrieval of accurate information in response to user queries, a critical part of the RAG process. Synthesis: The retrieved information is then used to build the context for a foundational model, generating responses grounded in reality. Monitoring: With many moving parts in the RAG system, Dataworkz provides robust monitoring capabilities essential for production use cases. Dataworkz's intuitive point-and-click interface (as seen in Video 1) simplifies RAG implementation, allowing enterprises to quickly operationalize AI applications. The platform offers flexibility and choice in data connectors, embedding models, vector stores, and language models. Additionally, tools like A/B testing ensure the quality and reliability of generated responses. This combination of ease of use, optionality, and quality assurance is a key tenet of Dataworkz's "RAG as a Service" offering. Diving deeper: System architecture and functionalities Now that we’ve looked at the components of the pre-processing pipeline, let’s explore the proposed real-time system architecture in detail. It comprises the following modules and functions (see Figure 2): Amazon Transcribe , which receives the audio coming from the customer’s phone and converts it into text. Cohere ’s embedding model, served through Amazon Bedrock , vectorizes the text coming from Transcribe. MongoDB Atlas Vector Search receives the query vector and returns a document that contains the most semantically similar FAQ in the database. Figure 2: System architecture and modules Here are a couple of FAQs we used for the demo: Q: “Can you explain the different types of coverage available for my home insurance?” A: “Home insurance typically includes coverage for the structure of your home, your personal belongings, liability protection, and additional living expenses in case you need to temporarily relocate. I can provide more detailed information on each type if you'd like.” Q: “What is the process for adding a new driver to my auto insurance policy?" A: “To add a new driver to your auto insurance policy, I'll need some details about the driver, such as their name, date of birth, and driver's license number. We can add them to your policy over the phone, or you can do it through our online portal.” Note that the question is reported just for reference, and it’s not used for retrieval. The actual question is provided by the user through the voice interface and then matched in real-time with the answers in the database using Vector Search. This information is finally presented to the customer service operator in text form (see Fig. 3). The proposed architecture is simple but very powerful, easy to implement, and effective. Moreover, it can serve as a foundation for more advanced use cases that require complex interactions, such as agentic workflows , and iterative and multi-step processes that combine LLMs and hybrid search to complete sophisticated tasks. Figure 3: App interface, displaying what has been asked by the customer (left) and how the information is presented to the customer service operator (right) This solution not only impacts human operator workflows but can also underpin chatbots and voicebots, enabling them to provide more relevant and contextual customer responses. Building a better future for customer service By seamlessly integrating analytical and operational data streams, insurance companies can significantly enhance both operational efficiency and customer satisfaction. Our system empowers businesses to optimize staffing, accelerate inquiry resolution, and deliver superior customer service through data-driven, real-time insights. To embark on your own customer service transformation, explore our GitHub repository and take advantage of the Dataworkz free tier . </p><div class="absolute bottom left bg-white w-full fl fl-justify-between p-20"><small class="fnt-14">November 27, 2024</small></div></div></a></div></div></div></div></div><div class="fixed bottom w-full fl fl-center pointer-events-none"><div class="clickable relative bg-green white fl fl-center h-50 p-30 shadow transition-400 transition-ease-out-4" style="transform: translate(0, 70px); pointer-events: auto; border-radius: 30px;"><p></p><span class="icon-x m-l-10"></span></div></div></main><div style="background-color: white;"></div> </div> <script hidden id="server-data" type="text/template"> {"baseUrl":"https://www.mongodb.com","channels":[{"_id":"5d1a9e24659d12dfa24ba28c","name":"Home","title":"MongoDB Blog","description":"Announcements, updates, news, and more","featured":"66ec18ab1e965fd84b7d111f","index":0,"featured_node_id":"65e9de49a4e0d91ee54f3f6e","updated_at":"2024-10-02T09:34:05.058Z","updated_by":"5f74b33bd21a1f001470a770","created_at":"2019-07-01T23:58:28.773Z","created_by":"5571ce696d6f6e1ab80f0000"},{"_id":"5d13472763ca09ceaccd1e5d","name":"News","title":"Company News","description":"The latest news from around MongoDB","featured":"672e1449da7a8be03a0932b1","featured_node_id":"65e9de3f402e3732ae795eb0","updated_at":"2024-11-12T14:05:11.017Z","updated_by":"5f74b33bd21a1f001470a770","created_at":"2019-06-26T10:21:27.162Z","created_by":"5be2cd65b7ddf201c80385dc","index":1},{"_id":"5d134f924d49320f1040896d","name":"Applied","title":"Applications ","description":"Customer stories, use cases, and experiences of MongoDB ","featured":"67115a173a16387e3b582d27","featured_node_id":"65e9de5ca18fc6e724c103bf","updated_at":"2024-10-30T13:25:07.678Z","updated_by":"5f74b33bd21a1f001470a770","created_at":"2019-06-26T10:57:22.657Z","created_by":"5be2cd65b7ddf201c80385dc","index":2},{"_id":"5d134fa91f62955efbec3b62","name":"Updates","title":"Product Updates","description":"The most recent MongoDB product releases and updates","featured":"670922083a16387e3bbaf1ba","featured_node_id":"65e9de90402e37498a795eb1","updated_at":"2024-10-30T13:25:29.867Z","updated_by":"5f74b33bd21a1f001470a770","created_at":"2019-06-26T10:57:45.332Z","created_by":"5be2cd65b7ddf201c80385dc","index":5},{"_id":"5d134f422660a6bf5acebb5d","name":"Culture","title":"Life at MongoDB","description":"Who MongoDB is as a company on the inside and outside","featured":"6720d5993a16387e3ba7f5f3","featured_node_id":"65e9de9da4e0d959524f3f70","updated_at":"2024-10-30T13:25:36.762Z","updated_by":"5f74b33bd21a1f001470a770","created_at":"2019-06-26T10:56:02.400Z","created_by":"5be2cd65b7ddf201c80385dc","index":6},{"_id":"5d1ab09b93db7ef9ca5b3b41","name":"Events","title":"Company Events","description":"Events from MongoDB and its Partners","featured":"6718f2de3a16387e3b780f23","index":7,"featured_node_id":"65e9deaf402e3735dd795eb2","updated_at":"2024-10-30T13:25:51.943Z","updated_by":"5f74b33bd21a1f001470a770","created_at":"2019-07-02T01:17:15.801Z","created_by":"5571ce696d6f6e1ab80f0000"},{"_id":"653f0042771a22cb798a8006","name":"Artificial Intelligence","title":"Artificial Intelligence","description":"Building AI-powered Apps with MongoDB","featured":"67115c633a16387e3b598d7b","index":8,"featured_node_id":"65e9debb402e3744d0795eb3","updated_at":"2024-10-30T13:26:03.246Z","updated_by":"5f74b33bd21a1f001470a770","created_at":"2023-10-30T01:00:50.757Z","created_by":"5f74b33bd21a1f001470a770"},{"_id":"61608bf446bc0deae5b2e538","created_at":"2021-10-08T18:20:36.721Z","description":"Posts on Engineering efforts, achievements, and culture at MongoDB.","featured":"66faf07e1e965fd84b9643ff","index":9,"name":"Engineering Blog","title":"MongoDB Engineering Blog","updated_at":"2024-10-02T10:01:05.052Z","featured_node_id":"623b3bef26d2d263beba305c","updated_by":"616dd2e92c89a60011114b63"}],"posts":[{"_id":"674478a8d12bdbac520ac927","url":"blog/post/with-mongodb-atlas-italian-telemedicine-born-it","body":"La medicina del futuro sarà molto più “tele” e meno fisica? Potremo monitorare la nostra salute senza spostamenti e senza sale d’attesa? Scopriamolo insieme ai protagonisti italiani di questa rivoluzione, costruita su importanti investimenti e tecnologie innovative.\n\nPNT Italia è la società di progetto costituita da Engineering e Almaviva, a cui AGENAS (Agenzia Nazionale per i Servizi Sanitari Regionali) ha affidato in concessione la progettazione, realizzazione e gestione della Piattaforma Nazionale di Telemedicina, che integra i servizi regionali per migliorare la qualità e l’accesso alle cure in tutto il Paese, in linea con gli obiettivi indicati dal Piano di Ripresa e Resilienza in ambito Sanità Digitale.\n\nFormalmente, PNT Italia è una società costituita dal 60% da Engineering e dal 40% da Almaviva, due tra i più grandi Gruppi tecnologici italiani. Su concessione di AGENAS PNT Italia ha creato la Piattaforma Nazionale di Telemedicina, che gestirà per 10 anni con il compito di assicurare la governance e il monitoraggio centralizzati dei processi di telemedicina attuati a livello regionale, mettendo in comunicazione l’Amministrazione Centrale con le Amministrazioni locali.\n\nPer capire meglio il percorso che ci porterà alla telemedicina abbiamo intervistato tre protagonisti di questa iniziativa: Stefano Zema, responsabile sviluppo e delivery PNT del Gruppo Engineering, Daniele Fortuna, PNT Infrastructure Architect di Almaviva e Angelo Immediata, PNT Solution Architect di Engineering.\n\n“Il progetto, che ha un respiro decennale, è diviso in tre macro fasi” dice Stefano Zema, responsabile sviluppo e delivery PNT del Gruppo Engineering, “la prima, che è durata da maggio a dicembre 2023, è stata la progettazione e implementazione, conclusa con il collaudo della piattaforma. Ora ci aspettano due anni di avvio e consolidamento (2024-25) in cui la piattaforma dovrà adattarsi e integrare le soluzioni regionali di telemedicina, che sono anch’esse in via di sviluppo. La terza fase comprende la gestione vera e propria della piattaforma”.\n\n“Tra i team di Engineering e Almaviva”, prosegue Zema, “si è subito creato un clima coeso, favorito anche dall’affiancamento dei tecnici italiani di MongoDB e dal supporto internazionale. Gli obiettivi del progetto, aiutare la transizione digitale del Paese e consentire ai malati cronici di stare vicino ai propri familiari, erano talmente alti e sfidanti da creare un clima di entusiasmo e voglia di fare bene in tutta l’organizzazione. Le persone, il metodo, la coesione e l’entusiasmo sono stati ingredienti fondamentali per la riuscita di questa prima fase”.\n\n“L’obiettivo”, aggiunge Angelo Immediata, “è anche quello di aiutare il Governo a far capire a operatori e pazienti l’importanza della telemedicina. Per raggiungerlo ci sono tante strade. Quelle più efficaci sono la definizione di standard comuni, e l’implementazione di soluzioni di telemedicina efficaci per fare in modo che gli operatori aderiscano in modo naturale e rispettino le indicazioni previste. Per raggiungere questo scopo, PNT impiega tra le 100 e le 150 persone che risiedono in 14 diverse regioni italiane, con Engineering e Almaviva che conferiscono risorse al progetto in modo dinamico”.\n\n## Un progetto complesso per un obiettivo alto\n\nLa sfida che il gruppo si trova ad affrontare è articolata: nel breve periodo bisogna creare il nucleo di una piattaforma flessibile, sicura e interoperabile (per mettere in comunicazione i sistemi regionali e quello centrale). Nel medio periodo è necessario garantire stabilità, scalabilità e resilienza. Nel lungo periodo, invece, sarà fondamentale supportare gli operatori a diffondere la cultura di un’assistenza sanitaria centrata sui bisogni degli assistiti e dei loro familiari.\n\n“Abbiamo scelto”, spiega Fortuna, “di preferire un’infrastruttura cloud e un’architettura orientata agli eventi, oltre che pensare a una piattaforma aperta, che evitasse i lock-in e che permettesse a PNT di trarre il maggior vantaggio dal cloud pubblico”.\n\n“Abbiamo scelto MongoDB Atlas come database per diversi motivi”, dice Angelo Immediata, PNT Solution Architect di Engineering, “primo tra tutti la tipologia di dati da trattare, in standard HL7 FHIR (Fast Healthcare Interoperability Resources), perfettamente gestibili da MongoDB, poi la natura multicloud della tecnologia, la gestione della sicurezza e la scalabilità orizzontale per trattare grandi moli di dati.”\n\n## I vantaggi di un ambiente aperto e scalabile \n\nPNT sceglie di realizzare una piattaforma orientata agli eventi, semplice da espandere e adattare, scalabile e flessibile. “Tutto lo stack tecnologico”, spiega Immediata, “è basato sui framework Java Spring per il back-end e Angular per il front-end. Nei flussi di dati dalle infrastrutture regionali al sistema centrale, le informazioni vengono anonimizzate ma non perdono il loro valore, assicurando così sicurezza e privacy, con l’obiettivo di una vulnerabilità pari a zero”.\n\n“La piattaforma è tutta in cloud su infrastrutture AWS”, dice Fortuna, “ma è stata progettata per non avere nessun lock-in. Siamo convintamente cloud native, ma non abbiamo voluto usare servizi nativi del provider; questo è stato uno dei motivi per cui abbiamo scelto MongoDB Atlas, che vive in SaaS e che è per definizione multi-ambiente”.\n\nTra i servizi associati a MongoDB già utilizzati da PNT c’è Online Archive, che permette di sfruttare in modo intelligente gli spazi di archiviazione dei Terabyte di dati provenienti dalle strutture sanitarie, favorendo la sostenibilità economica e ambientale del progetto, ed Encryption at Rest, che proteggendo i dati a livello di DB consente di abbattere la complessità e favorire la compliance. Nel mirino per il prossimo futuro c’è poi la tecnologia Vector Search, associata all’adozione dell’AI Generativa.\n\n“Conclusa la prima fase”, dice Fortuna, “ora dedicheremo le nostre energie alla verifica e alla validazione delle piattaforme regionali” e, come rinforza Immediata, “Partiamo dal dato incoraggiante che con MongoDB l’effort per creare le istanze è circa la metà di quello necessario con altre tecnologie e che i tempi si calcolano in ore invece che in giorni.” Il team è pronto alle imminenti sfide, e a sfruttare la flessibilità del database considerando che alla fine del 2025 ci saranno oltre 300mila pazienti assistiti in telemedicina contemporaneamente.\n\n<div class='blockquote-sm'>\n<blockquote>\n<p>In un progetto lungo 10 anni, nessuno può sapere come evolverà la tecnologia; per questo è importante aver scelto un partner che ci permette di scegliere sempre le soluzioni migliori. MongoDB ci fa dormire tranquilli. È una società giovane, dinamica ma nello stesso tempo leader di mercato.</p>\n<small>Stefano Zema, responsabile sviluppo e delivery PNT, Gruppo Engineering</small>\n</blockquote>\n</div>\n\n<div class=\"callout\">\n\t\n<b>Guarda come gestire in sicurezza i dati sanitari con <a href=\"https://www.mongodb.com/it-it/lp/cloud/atlas/try4\" target=\"_blank\">MongoDB Atlas</a>. </b>\t\n</div>","created_at":"2024-11-25T13:16:24.089Z","meta":{"title":"Innovazione E Salute: MongoDB A Supporto Della Piattaforma Nazionale Di Telemedicina","title#localised":true,"description":"La Piattaforma Nazionale di Telemedicina si affida a MongoDB Atlas per gestire efficacemente i dati sanitari, permettendo di migliorare la vita degli assistiti e dei loro familiari.","description#localised":true},"node_type":"blog_post","node_type_attributes":{},"published_at":"2024-11-25T08:30:00.000Z","status":"published","title":"Innovazione e salute: MongoDB a supporto della Piattaforma Nazionale di Telemedicina","updated_at":"2024-11-25T16:56:41.972Z","cms":{"editedURL":true},"draft":true,"globals":[{"_id":"6001f22ac1f95e773a0e0044","key":"AccountLogin","created_at":"2021-01-15T19:51:06.717Z","props":{"title":"MongoDB Stands with the Black Community, changes","subtitle":"Join MongoDB in supporting organizations that are fighting for racial justice and equal opportunity","cta":{"text":"Join Now","href":"https://mongodbforjustice.mongodb.events/","openInNewWindow":false,"faux":false},"image":{"desktop":"https://account.mongodb.com/static/images/auth/racial_justice_desktop_login.png","mobile":"https://account.mongodb.com/static/images/auth/racial_justice_mobile.png"},"artist":"Artwork by [Lo Harris](http://loharris.com/)"},"updated_at":"2024-10-21T20:11:05.302Z"},{"_id":"601c7536f53e6b3af09679d3","key":"PromoBanner","created_at":"2021-02-04T22:29:10.420Z","props":{"type":4,"typeColor":0,"title":"Join us at AWS re:Invent 2024! 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Since then, he’s experienced tremendous growth. Successful deliveries of MongoDB 4.4 features and MongoDB 5.0 sharding features helped fuel Garaudy’s career development, as did his work establishing a long-term sharding vision, mentoring others, and successfully managing interns. Now, as a Director of Product, he’s defining the strategic direction across multiple products and helping grow our product management organization and culture. Read on to learn more about Garaudy’s experience at MongoDB and his expanding team.</i>\n\n<figure>\n<div class=\"fl-center\">\n<img src=\"https://webassets.mongodb.com/_com_assets/cms/Screenshot 2024-11-21 at 8.33.07 AM-dwcb3hebku.png\" alt=\" \" title=\" \" style=\"width: 750px\"/>\n</div>\n<figcaption class=\"fl-center\"> </figcaption>\n</figure>\n\n## A team with impact \n\nMy team focuses on distributed systems within MongoDB's core database functions, also known as the database engine. Our team ensures the database is reliable and scalable for our most demanding customers. We ensure the product consistently performs as promised, especially at scale. MongoDB's dependability drives greater usage, which enhances our revenue and brand perception. \n\nThe problems my team works on are vast and relatively undefined. These include revamping our Go-To-Market strategy for new and existing features, guiding the engineering team on architectural decisions driven by customer demands, identifying target markets, and assisting customers in challenging situations.\n\n## MongoDB and AI\n\nWe’re in the early stages of the AI boom. MongoDB’s document model is particularly well-suited for this era, as it excels in handling unstructured data, which makes up the majority of today’s information. As AI increasingly relies on diverse formats like text, images, and videos, our flexible schema enables efficient storage and retrieval of unstructured data, enabling applications to extract valuable insights. Our <a href=\"https://www.mongodb.com/products/platform/atlas-vector-search\" target=\"_blank\">vector search</a> capability enables fast, complex data matching and retrieval, making it ideal for AI-powered applications. This synergy between MongoDB’s document model plus Vector Search and the needs of AI-driven applications positions us as a powerful foundation for companies looking to enable AI into their workflows.\n\nThe beauty of working in the core database is that it has to support every workload, including the new and expanding Vector Search applications. This means we need to ensure the database remains robust and scalable as AI demands evolve. Some examples are helping develop a more scalable architecture for Search or a new networking stack for Search. No matter what new capabilities MongoDB decides to deliver or the new markets we enter, everything must pass through the core database. This also allows you to meet lots of people and understand everything the company is doing instead of working in a silo.\n## A rewarding career in product\n\nMongoDB is committed to career development, something I’ve experienced first-hand. The company has provided me with development opportunities through product management-specific training with Reforge, conferences, direct engagement with critical customers, and leadership training. As a product manager, I was offered mentorship and coaching with multiple experienced product leaders who provided guidance and support as I worked toward promotions. The company clearly communicates the expectations and requirements for advancement within the product management organization.\n\nReflecting on my journey at MongoDB, I still remember the first two features I PM’d: Hedged Reads and Mirrored Reads. One of my first major highlights was presenting at the MongoDB 5.0 keynote to showcase resharding. Seeing genuine excitement from customers and internal teams about this new feature was incredibly fulfilling and reinforced its value. While the keynote was a public milestone, another personal highlight came when I finally visited one of my engineering teams in Barcelona after nearly two years of remote collaboration. This in-person time was invaluable and helped us bring the groundbreaking sharding changes for MongoDB 6.0 to the finish line.\n\nMost recently, defining the key strategic pillars for MongoDB 8.0 and allowing other product managers to take ownership of key initiatives has been more rewarding than I imagined. MongoDB’s engineering team is extremely talented, and collaborating with them always brings me tremendous joy. The most recent highlight of my career has been building a diverse product team and helping other product managers make a larger impact than they previously envisioned. \n\n## Why MongoDB\n\nWhat keeps me at MongoDB is the opportunity to tackle significant challenges, make autonomous decisions, own multiple products, and take on greater leadership responsibilities. MongoDB also rewards and recognizes product managers who drive meaningful impact across the organization and its products. If these opportunities excite you, you'll thrive as part of MongoDB’s product management team! \n\nFor my team, I’m committed to providing the right balance of guidance and autonomy. Your decisions will have a lasting impact at the executive and organizational levels, creating continuous opportunities to excel and deliver meaningful results. Plus, I always try to make the job fun. \n\n<div class=\"callout\">\n\t\n<b>Head to our <a href=\"https://www.mongodb.com/company/careers/teams/product-management-and-design\" target=\"_blank\">careers site</a> to apply for a role on Garaudy’s team and <a href=\"https://www.gem.com/form?formID=a13ca4d9-9234-4a80-9b4d-f7ba8d60cd75\" target=\"_blank\">join our talent community</a> to stay in the loop on all things #LifeAtMongoDB! </b>\n</div>\t","created_at":"2024-11-21T15:33:12.155Z","meta":{"title":"Influencing Product Strategy At MongoDB With Garaudy Etienne ","title#localised":true,"description":"Learn more about product management at MongoDB from Garaudy Etienne.","description#localised":true},"node_type":"blog_post","node_type_attributes":{},"published_at":"2024-11-25T15:56:00.000Z","status":"published","title":"Influencing Product Strategy at MongoDB with Garaudy Etienne ","updated_at":"2024-11-25T15:59:51.207Z","channel_ids":["5d1a9e24659d12dfa24ba28c","5d134f422660a6bf5acebb5d"],"cms":{"editedURL":true},"draft":true,"globals":[{"_id":"6001f22ac1f95e773a0e0044","key":"AccountLogin","created_at":"2021-01-15T19:51:06.717Z","props":{"title":"MongoDB Stands with the Black Community, changes","subtitle":"Join MongoDB in supporting organizations that are fighting for racial justice and equal opportunity","cta":{"text":"Join Now","href":"https://mongodbforjustice.mongodb.events/","openInNewWindow":false,"faux":false},"image":{"desktop":"https://account.mongodb.com/static/images/auth/racial_justice_desktop_login.png","mobile":"https://account.mongodb.com/static/images/auth/racial_justice_mobile.png"},"artist":"Artwork by [Lo Harris](http://loharris.com/)"},"updated_at":"2024-10-21T20:11:05.302Z"},{"_id":"601c7536f53e6b3af09679d3","key":"PromoBanner","created_at":"2021-02-04T22:29:10.420Z","props":{"type":4,"typeColor":0,"title":"Join us at AWS re:Invent 2024! 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Wir arbeiten jedoch daran, so viele lokalisierte Inhalte wie möglich bereitzustellen. Vielen Dank für Ihre Geduld."},"fr-fr":{"text":"Le contenu de cette page n'est actuellement pas disponible dans la langue sélectionnée. Nous mettons toutefois tout en œuvre pour proposer autant de contenu localisé que possible. Merci de votre patience."},"ja-jp":{"text":"現在、このページの選択した言語のコンテンツはありません。ローカライズされたコンテンツをできるだけ多く提供できるよう取り組んでいます。しばらくお待ちください。"},"ko-kr":{"text":"본 페이지 컨텐츠는 현재 선택된 언어로는 볼 수 없습니다. 가능한 빨리 현지화된 컨텐츠를 제공해 드리기 위해 노력하고 있습니다. 기다려 주셔서 감사합니다."},"zh-cn":{"text":"本页面内容目前不支持所选语言。我们将尽可能提供更多的本地化内容。敬请期待。"}}},{"_id":"616eeecda9b8227a40aa618c","key":"DTRolloutComponent","props":{"targetAudience":"100"},"created_at":"2021-10-19T16:14:05.400Z","updated_at":"2024-10-21T20:11:05.326Z"},{"_id":"653956df6e40c7d11245d051","key":"PencilBanner","props":{"pillText":"Event","disabled":false,"bannerTheme":0,"theme":"forestGreen","bannerText":"Join us at AWS re:Invent 2024! 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","twitter_image":"https://webassets.mongodb.com/_com_assets/cms/Screenshot 2024-11-21 at 8.33.07 AM-dwcb3hebku.png","twitter_title":"Influencing Product Strategy at MongoDB with Garaudy Etienne ","updated_by":"5f74b33bd21a1f001470a770","url_type":"alias","tags":[{"_id":"5f98644186009eb0714b7311","tag_group_id":"5754e1a5bc0141ac1d4d099b","name":"Culture","updated_at":"2020-10-27T18:17:37.703Z","updated_by":"5f74b33bd21a1f001470a770","created_at":"2020-10-27T18:17:37.703Z","created_by":"5f74b33bd21a1f001470a770"}],"channels":[{"_id":"5d134f422660a6bf5acebb5d","name":"Culture","title":"Life at MongoDB","description":"Who MongoDB is as a company on the inside and outside","featured":"6720d5993a16387e3ba7f5f3","featured_node_id":"65e9de9da4e0d959524f3f70","updated_at":"2024-10-30T13:25:36.762Z","updated_by":"5f74b33bd21a1f001470a770","created_at":"2019-06-26T10:56:02.400Z","created_by":"5be2cd65b7ddf201c80385dc","index":6},{"_id":"5d1a9e24659d12dfa24ba28c","name":"Home","title":"MongoDB Blog","description":"Announcements, updates, news, and more","featured":"66ec18ab1e965fd84b7d111f","index":0,"featured_node_id":"65e9de49a4e0d91ee54f3f6e","updated_at":"2024-10-02T09:34:05.058Z","updated_by":"5f74b33bd21a1f001470a770","created_at":"2019-07-01T23:58:28.773Z","created_by":"5571ce696d6f6e1ab80f0000"}],"authors":[{"_id":"61e9c4c305d9c8b3cad816c2","node_type":"profile","node_type_attributes":{"company":"MongoDB ","job_title":"Senior Product Manager","photo_url":"https://webimages.mongodb.com/_com_assets/cms/kynf8pwz9wumasopd-Screen%20Shot%202022-01-20%20at%201.22.31%20PM.png?auto=format%252Ccompress","blog_url":"","twitter_handle":"","linkedin_url":"","github_name":""},"title":"Garaudy Etienne","jira":"","status":"draft","published_at":null,"content_expires_at":null,"expired_redirect_to":"","url":"profile/garaudy-etienne","url_type":"alias","meta_title":"","meta_description":"","meta_canonical_url":"","meta_og_image":"","meta_noindex":false,"header_bg":"","hero_cta_text":"","hero_cta_link":"","form_label":"","header_opacity":"6","twitter_title":"","twitter_description":"","twitter_image":"","tag_ids":[],"node_ids":[],"region":"","summary":"","body":"","script":"","updated_at":"2022-01-20T20:23:31.557Z","updated_by":"5f74b33bd21a1f001470a770","created_at":"2022-01-20T20:23:31.557Z","created_by":"5f74b33bd21a1f001470a770"}]},{"_id":"6745d4e8f6a2e6fee7290e53","url":"blog/post/ai-powered-call-centers-new-era-of-customer-service","body":"Customer satisfaction is critical for insurance companies. Studies have shown that companies with superior customer experiences consistently outperform their peers. In fact, McKinsey found that life and property/casualty insurers with superior customer experiences saw a significant <a href=\"https://www.mckinsey.com/industries/financial-services/our-insights/insurance/elevating-customer-experience-a-win-win-for-insurers-and-customers\" target=\"_blank\">20% and 65% increase in Total Shareholder Return</a>, respectively, over five years. \n\nA satisfied customer is a loyal customer. They are <a href=\"https://www.mckinsey.com/industries/financial-services/our-insights/the-growth-engine-superior-customer-experience-in-insurance\" target=\"_blank\">80%</a> more likely to renew their policies, directly contributing to sustainable growth. However, one major challenge faced by many insurance companies is the inefficiency of their call centers. Agents often struggle to quickly locate and deliver accurate information to customers, leading to frustration and dissatisfaction.\n\nThis article explores how Dataworkz and MongoDB can transform call center operations. By converting call recordings into searchable vectors (numerical representations of data points in a multi-dimensional space), businesses can quickly access relevant information and improve customer service. We'll dig into how the integration of Amazon Transcribe, Cohere, and MongoDB Atlas Vector Search—as well as Dataworkz's RAG-as-a-service platform— is achieving this transformation.\n\n## From call recordings to vectors: A data-driven approach\n\nCustomer service interactions are goldmines of valuable insights. By analyzing call recordings, we can identify successful resolution strategies and uncover frequently asked questions. In turn, by making this information—which is often buried in audio files— accessible to agents, they can give customers faster and more accurate assistance.\n\nHowever, the vast volume and unstructured nature of these audio files make it challenging to extract actionable information efficiently.\n\nTo address this challenge, we propose a pipeline that leverages AI and analytics to transform raw audio recordings into vectors as shown in Figure 1: \n\n* Storage of raw audio files: Past call recordings are stored in their original audio format\n\n* Processing of the audio files with AI and analytics services (such as <a href=\"https://aws.amazon.com/transcribe/call-analytics/\" target=\"_blank\">Amazon Transcribe Call Analytics</a>): speech-to-text conversion, summarization of content, and vectorization\n\n* Storage of vectors and metadata: The generated vectors and associated metadata (e.g., call timestamps, agent information) are stored in an operational data store\n\n<figure>\n<div class=\"fl-center\">\n<img src=\"https://webassets.mongodb.com/_com_assets/cms/Screenshot 2024-11-26 at 7.07.59 AM-qcba50hcob.png\" alt=\"Diagram showing the call insight extraction and vectorization flow. The incoming call connects to the object storage, the object storage flows into analytics services, and then that flows into operational data store.\" title=\" \" style=\"width: 750px\"/>\n</div>\n<figcaption class=\"fl-center\">Figure 1: Customer service call insight extraction and vectorization flow</b> </figcaption>\n</figure>\n\nOnce the data is stored in vector format within the operational data store, it becomes accessible for real-time applications. This data can be consumed directly through vector search or integrated into a retrieval-augmented generation (RAG) architecture, a technique that combines the capabilities of large language models (LLMs) with external knowledge sources to generate more accurate and informative outputs.\n\n## Introducing Dataworkz: Simplifying RAG implementation\n\nBuilding RAG pipelines can be cumbersome and time-consuming for developers who must learn yet another stack of technologies. Especially in this initial phase, where companies want to experiment and move fast, it is essential to leverage tools that allow us to abstract complexity and don’t require deep knowledge of each component in order to experiment with and realize the benefits of RAG quickly.\n\n<a href=\"https://www.dataworkz.com/\" target=\"_blank\">Dataworkz</a> offers a powerful and composable RAG-as-a-service platform that streamlines the process of building RAG applications for enterprises. To operationalize RAG effectively, organizations need to master five key capabilities:\n\n* <b>ETL for LLMs:</b> Dataworkz connects with diverse data sources and formats, transforming the data to make it ready for consumption by generative AI applications.\n\n* <b>Indexing:</b> The platform breaks down data into smaller chunks and creates embeddings that capture semantics, storing them in a vector database.\n\n* <b>Retrieval:</b> Dataworkz ensures the retrieval of accurate information in response to user queries, a critical part of the RAG process.\n\n* <b>Synthesis:</b> The retrieved information is then used to build the context for a foundational model, generating responses grounded in reality.\n\n* <b>Monitoring:</b> With many moving parts in the RAG system, Dataworkz provides robust monitoring capabilities essential for production use cases.\n\nDataworkz's intuitive point-and-click interface (as seen in Video 1) simplifies RAG implementation, allowing enterprises to quickly operationalize AI applications. The platform offers flexibility and choice in data connectors, embedding models, vector stores, and language models. Additionally, tools like A/B testing ensure the quality and reliability of generated responses. This combination of ease of use, optionality, and quality assurance is a key tenet of Dataworkz's \"RAG as a Service\" offering.\n\n<iframe width=\"800\" height=\"425\" src=\"https://www.youtube.com/embed/cMv6NKEW9HA?si=o4opav-HQHw5J631\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen></iframe>\n\n## Diving deeper: System architecture and functionalities\n\nNow that we’ve looked at the components of the pre-processing pipeline, let’s explore the proposed real-time system architecture in detail. It comprises the following modules and functions (see Figure 2):\n\n* <a href=\"https://aws.amazon.com/transcribe/\" target=\"_blank\">Amazon Transcribe</a>, which receives the audio coming from the customer’s phone and converts it into text.\n\n* <a href=\"https://cohere.com/\" target=\"_blank\">Cohere</a>’s embedding model, served through <a href=\"https://aws.amazon.com/bedrock/\" target=\"_blank\">Amazon Bedrock</a>, vectorizes the text coming from Transcribe.\n\n* <a href=\"https://www.mongodb.com/products/platform/atlas-vector-search\" target=\"_blank\">MongoDB Atlas Vector Search</a> receives the query vector and returns a document that contains the most semantically similar FAQ in the database.\n\n<figure>\n<div class=\"fl-center\">\n<img src=\"https://webassets.mongodb.com/_com_assets/cms/Screenshot 2024-11-26 at 7.17.25 AM-d00hiuu0s9.png\" alt=\"Diagram showing the system architecture. The customer reaches out to customer service, who then utilizes Cohere and Amazon Transcribe with data stored on MongoDB Atlas.\" title=\" \" style=\"width: 750px\"/>\n</div>\n<figcaption class=\"fl-center\">Figure 2: System architecture and modules </figcaption>\n</figure>\n\nHere are a couple of FAQs we used for the demo:\n\n* <b>Q:</b> “Can you explain the different types of coverage available for my home insurance?” <br><b>A:</b> “Home insurance typically includes coverage for the structure of your home, your personal belongings, liability protection, and additional living expenses in case you need to temporarily relocate. I can provide more detailed information on each type if you'd like.”\n\n* <b>Q:</b> “What is the process for adding a new driver to my auto insurance policy?\" <br><b>A:</b> “To add a new driver to your auto insurance policy, I'll need some details about the driver, such as their name, date of birth, and driver's license number. We can add them to your policy over the phone, or you can do it through our online portal.”\n\nNote that the question is reported just for reference, and it’s not used for retrieval. The actual question is provided by the user through the voice interface and then matched in real-time with the answers in the database using Vector Search. This information is finally presented to the customer service operator in text form (see Fig. 3).\n\nThe proposed architecture is simple but very powerful, easy to implement, and effective. Moreover, it can serve as a foundation for more advanced use cases that require complex interactions, such as <a href=\"https://www.mongodb.com/resources/basics/artificial-intelligence/ai-agents\" target=\"_blank\">agentic workflows</a>, and iterative and multi-step processes that combine LLMs and <a href=\"https://www.mongodb.com/docs/atlas/atlas-vector-search/tutorials/reciprocal-rank-fusion/\" target=\"_blank\">hybrid search</a> to complete sophisticated tasks.\n\n<figure>\n<div class=\"fl-center\">\n<img src=\"https://webassets.mongodb.com/_com_assets/cms/customer_service-3z1tdgr1aj.gif\" alt=\"GIF of the app interface for leafy insurance, a mock-up created by MongoDB.\" title=\" \" style=\"width: 750px\"/>\n</div>\n<figcaption class=\"fl-center\"><center>Figure 3: App interface, displaying what has been asked by the customer (left) and how the information is presented to the customer service operator (right)</center></figcaption>\n</figure>\n\nThis solution not only impacts human operator workflows but can also underpin chatbots and voicebots, enabling them to provide more relevant and contextual customer responses.\n\n## Building a better future for customer service\n\nBy seamlessly integrating analytical and operational data streams, insurance companies can significantly enhance both operational efficiency and customer satisfaction. Our system empowers businesses to optimize staffing, accelerate inquiry resolution, and deliver superior customer service through data-driven, real-time insights. To embark on your own customer service transformation, explore our <a href=\"https://github.com/mongodb-industry-solutions/Insurance-customer-service\" target=\"_blank\">GitHub repository</a> and take advantage of the <a href=\"https://ragapps.dataworkz.com/\" target=\"_blank\">Dataworkz free tier</a>.","created_at":"2024-11-26T14:02:16.596Z","meta":{"title":"AI-Powered Call Centers: A New Era Of Customer Service","title#localised":true,"description":"Learn how AI and MongoDB are transforming insurance call centers through vector search and RAG pipelines, enabling faster customer service and improved satisfaction by making call recordings searchable and accessible.","description#localised":true},"node_type":"blog_post","node_type_attributes":{},"published_at":"2024-11-27T15:00:00.000Z","status":"published","title":"AI-Powered Call Centers: A New Era of Customer Service","updated_at":"2024-11-27T17:28:57.654Z","channel_ids":["5d1a9e24659d12dfa24ba28c","653f0042771a22cb798a8006"],"cms":{"editedURL":true},"draft":true,"globals":[{"_id":"6001f22ac1f95e773a0e0044","key":"AccountLogin","created_at":"2021-01-15T19:51:06.717Z","props":{"title":"MongoDB Stands with the Black Community, changes","subtitle":"Join MongoDB in supporting organizations that are fighting for racial justice and equal opportunity","cta":{"text":"Join Now","href":"https://mongodbforjustice.mongodb.events/","openInNewWindow":false,"faux":false},"image":{"desktop":"https://account.mongodb.com/static/images/auth/racial_justice_desktop_login.png","mobile":"https://account.mongodb.com/static/images/auth/racial_justice_mobile.png"},"artist":"Artwork by [Lo Harris](http://loharris.com/)"},"updated_at":"2024-10-21T20:11:05.302Z"},{"_id":"601c7536f53e6b3af09679d3","key":"PromoBanner","created_at":"2021-02-04T22:29:10.420Z","props":{"type":4,"typeColor":0,"title":"Join us at AWS re:Invent 2024! 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