Infosys Media Platform & MongoDB: Metadata Management and Workflow Orchestration Across Media Supply

Capitalize on current and innovative technologies in the media supply chain with the Infosys Media Platform (IMP). As a part of the cloud and cloud-based Infosys Cobalt™ portfolio, Infosys’ unifying framework, built on MongoDB Atlas and MongoDB Enterprise Advanced, helps you facilitate creative collaboration, enable productions on an industrial scale, and monetize customer relationships. How? By integrating the various ecosystems involved and providing a common platform to connect you to services and technology solutions for the media content value chain.

Infosys’ intelligently-woven media and metadata management framework, leveraging MongoDB’s document model, enables smart workflows and incorporates ML/AI to create, manage and moderate content metadata. This allows the orchestration workflows across different business functions. Additionally, the platform delivers the benefits of productivity, scalability, and agility via the cloud and streamlines collaboration among the ecosystem of partners and technology solutions.

Why Infosys Media Platform (IMP)?

The Infosys Media Platform consists of various modules that serve critical business functions, such as:

  • The Curation & Digitization Module -- provides the master workflow and ingests content from internal archives and multiple sources using AI/ML to create a composite index of frame level and time-coded metadata of recognized elements (such as celebrities/known personalities, objects, brands, text, and images). It also enables intelligent ad spot identification and includes functions like automated QC, editing, review, and approval, and censor editing

  • The Custom AI Module -- ensures that newly introduced elements such as celebrities, brands, etc., can be continuously trained and recognized. This can also be used to custom-train the AI models to recognize specific content as per customer’s needs

  • The Localization Module -- enables collaboration across multiple locations and global vendors through automatic generation of closed captions and subtitles in multiple languages

  • Metadata Management and Distribution Module -- enables global distribution to digital platforms at scale through standard workflow models and a state-of-the-art dashboard by orchestrating the accumulation of asset level and descriptive time-based metadata from production to delivery

With the above modules, Infosys Media Platform can attain the following capabilities:

  • Content Enrichment -- leverages AI models to process video files and generate time-coded metadata for post-production and distribution process

  • Closed Captioning, Subtitling and Localization -- processes audio (dialogs from a video, lyrics from a song, speech from a podcast) and converts it into closed captions and subtitles

  • Content Moderation -- recognizes the presence of mature content (profanity, violence, gore etc.) using the video content and speech detection capabilities

  • Image Processing -- identifies various attributes of an image file, similar to the capabilities on video/music content

  • Metadata Packaging & Distribution -- manages end-to-end supply chain of digital metadata creation, updates, packaging and distribution

  • NLP Based Analytics -- using natural language processing capabilities of the platform, users can review any string of text (dialogs, lyrics, conversations) to determine the context of the conversation as well as the sentiment

Why MongoDB for Infosys Media Platform?

What company wouldn’t want a database platform that increases developer productivity and data-driven operational efficiency? MongoDB offers both.

With MongoDB Atlas, you can reduce the time developers spend managing data and databases, so they can focus on value-added tasks like developing new apps. MongoDB’s document model and query language provide easier access to data, allowing developers to work quickly and efficiently to support new data structures and data types, as well as leverage database-supported roll-ups for analysis.

Additionally, MongoDB Atlas introduces 100+ metrics and monitoring capabilities with its complete data platform built to improve operational productivity, so you can work smarter, not harder. A main feature of Infosys Media Platform is its cloud-agnostic nature; and as a cloud-agnostic and multi-cloud data platform, MongoDB is the only platform that satisfies IMP with its ability to run seamlessly across the globe. Through a mixed workload of real-time and transactional analytics, IMP also offers a roadmap on analytics, text search and data visualization capabilities--and MongoDB provides all these features.

How MongoDB powers the data platform for Infosys Media Platform (IMP)

  • Data for all the modules previously described is powered by the MongoDB data platform

  • Details like profile data, accounts, ratings, translations, country/region details are stored in MongoDB

  • Audit transactional data are currently running on SQL and there is a roadmap for moving to MongoDB data platform.

In the future, MongoDB’s core capabilities will further enhance the Infosys Media Platform and customer experience. Our roadmap includes utilizing MongoDB ACID transactional capabilities to store audit details, as well as using MongoDB functions and triggers for creating cloud agnostic serverless functions. Additionally, MongoDB Atlas may be leveraged for full-text search capabilities applied to media data, and to create charts for dashboarding and real-time analytics of user subscriber data.

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Built With MongoDB: Italic

Derek Tu and Jeremy Cai , two high school friends from the Chicago suburbs, began aspiring to become entrepreneurs at a young age. Their pursuits took them to Babson College, where they overlapped until Jeremy received a Thiel Fellowship and dropped out of school to build his first startup. Now, the two friends have teamed back up to create Italic , a membership-based marketplace, with Jeremy as Founder/CEO and Derek as Product Lead. The game-changing Italic platform offers manufacturers the chance to sell their products directly to consumers and reach a global customer base. Since its launch in 2018, the company has raised $15 million and given consumers the chance to shop unbranded quality goods at prices 80 percent lower than what comparable brands would ordinarily charge. Italic launched with a waitlist of more than 50 thousand people, and it’s also built a team of 54 employees across the world. In this edition of #BuiltWithMongoDB, we talk with Derek about building the new “everything store” and his experience building a game-changing ecommerce platform with MongoDB. MongoDB: What was the original vision for Italic, and what does the product look like now? Derek: The original vision for Italic was pretty simple in my opinion. It was to make good on the original promise of direct-to-consumer (DTC) ecommerce, which is to cut out the middleman, sell goods or services straight from the source, and pass the margin savings back to the customer. We strongly felt that existing DTC incumbents weren’t upholding this promise and had become themselves the middlemen in this entire equation. We eventually found that we could best deliver on this promise by operating a subscription model in which we charge for a membership that grants customers access to our entire product offering. By doing that, we’ve been able to drop the prices on products to a level where we are essentially passing all the savings directly to the members and where we profit only from the annual subscription fee that our members pay. MongoDB: What does the tech stack consist pf at a high level? Derek: We’re running on a Jamstack , consisting of a Next.js framework on the front end with deployments handled by Vercel. Our back-end system consists of Node.js services that interact with our MongoDB cluster via an Apollo GraphQL client. MongoDB: How did you decide to start building on MongoDB? Derek: Prior to MongoDB, we used Shopify directly as our pseudo-database, but when we went to launch our membership app, we realized the need to create and manipulate data outside Shopify. There was pressure to launch and test the membership app as quickly as possible, so we tried to find a database system that’s highly performant and easily scalable, yet quick to implement. We needed to start deploying features ASAP, because time was a luxury we didn’t have. That’s where a nonrelational database such as MongoDB helped, because we didn’t need to spend hours upon hours finalizing a data schema with primary and foreign keys first. We were also looking to ingest data from Shopify, so not being bound to a certain table structure allowed us to save time on ETL. We tried hosting a Mongo cluster ourselves at first but quickly realized we needed a fully managed service that could abstract out DevOps for us. Ultimately, that’s why we started using MongoDB’s Atlas solution. MongoDB: As you've grown over the past two years, how has it been working with MongoDB? Derek: The best web services are the ones you set up once and they keep running in the background with minimal upkeep. That’s how I’d describe MongoDB Atlas. I honestly can’t remember the last time I had to log into our dashboard to resolve a database-related issue. When we started, we leaned heavily on MongoDB’s technical support to help answer our questions and triage some of our issues. And of course, the startup credits that came with being a part of the YC alumni network definitely helped, because they provided us the opportunity to test the service out before truly committing and paying for it. MongoDB: As a product lead, what resources do you use to upscale in your job as you grow in your career? Derek: Honestly my engineering peers have been the most valuable resource for me as a product manager. Just from answering my questions and through candid discussions, they’ve taught me more about building products than any bootcamp or college degree ever could’ve. And, as I’ve progressed in my career, I’ve stopped asking them how the software is being built and started asking them instead why the software is being built a certain way. I’ve learned over time that feature limitations often stem from how underlying technologies are selected and implemented. Therefore I’m a firm believer that all successful product managers need a deep understanding of systems design. MongoDB: What is the last good technical book that you read or podcast that you listened to? Derek: Not super technical, but I’m currently listening to All-In with Chamath, Jason, Sacks & Friedberg . It’s refreshing to hear their unfiltered, personal takes on major events in the public and private markets. In terms of more technical reading, Wiley’s textbook Systems Analysis and Design is my reference for anything related to systems design. I’ve also been a huge fan of Glossier’s tech team from the early days and have learned a lot from studying them. This article from an early team member provides an in-depth walkthrough of their data pipeline and shows their deliberate efforts to invest upfront in technology that paid dividends for them later on. MongoDB: Who are some technical mentors that you admire for their technical and product management skills? Derek: We have a large roster of angel investors who are seasoned tech executives and founders at hyper-growth startups, public companies, and everything in between. I won’t name drop them all here, but they’ve all been instrumental to our success to date. We’ve been able to lean on every one of them for advice and mentorship from time to time. Looking to build something cool? Get started with the MongoDB for Startups program.

May 19, 2021
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Customer Service Expert Wati.io Scales Up on MongoDB

Wati.io is a software-as-a-service (SaaS) platform that empowers businesses to develop conversation-driven strategies to boost growth. Founded by CEO Ken Yeung in 2019, Wati started as a chatbot solution for large enterprises, such as banks and insurance companies. However, over time, Yeung and his team noticed a growing need among small and medium-sized businesses (SMBs) to manage customer conversations more effectively. To address this need, Wati used MongoDB Atlas and built a solution based on the WhatsApp Business API. It enables businesses to manage and personalize conversations with customers, automate responses, improve commerce functions, and enhance customer engagement. Speaking at MongoDB.local Hong Kong in September 2024, Yeung said, “The current solutions on the market today are not good enough. Especially for SMBs [that] don’t have the same level of resources as enterprises to deal with the number of conversations and messages that need to be handled every day.” Supporting scale: From MongoDB Community Edition to MongoDB Atlas “From the beginning, we relied on MongoDB to handle high volumes of messaging data and enable businesses to manage and scale their customer interactions efficiently,” said Yeung. Wati originally used MongoDB Community Edition , as the company saw the benefits of a NoSQL model from the beginning. As the company grew, it realized it needed a scalable infrastructure, so Wati transitioned to MongoDB Atlas. “When we started reaching the 2 billion record threshold, we started having some issues. Our system slowed down, and we were not able to scale it,” said Yeung. Atlas has now become an essential part of Wati’s infrastructure, helping the company store and process millions of messages each month for over 10,000 customers in 165 countries. “Transitioning to a new platform—MongoDB Atlas—seamlessly was critical because our messaging system needs to be on 24/7,” said Yeung. Wati collaborated closely with the MongoDB Professional Services and MongoDB Support teams, and in a few months it was able to rearchitect the deployment and data model for future growth and demand. The work included optimizing Wati’s database by breaking it down into clusters. Wati then focused on extracting connections, such as conversations, and dividing and categorizing data within the clusters—for example, qualifying data as cold or hot based on the read and write frequencies. This architecture underpins the platform’s core features, including automated customer engagement, lead qualification, and sales management. Deepening search capabilities with MongoDB Atlas Search For Wati’s customers, the ability to search through conversation histories and company documents to retrieve valuable information is a key function. This often requires searching through millions of records to rapidly find answers so that they can respond to customers in real-time. By using MongoDB Atlas Search , Wati improved its search capabilities, ultimately helping its business customers perform more advanced analytics and improve their customer service agents’ efficiency and customer reporting. “[MongoDB] Atlas Search is really helpful because we don’t have to do a lot of technical integration, and minimal programming is required,” said Yeung. Looking ahead: Using AI and integrating more channels Wati expects to continue collaborating with MongoDB to add more features to its platform and keep innovating at speed. The company is currently exploring to build more AI capabilities of Wati KnowBot , as well as how it can expand its integration with other conversation platforms and channels such as Instagram and Facebook. To learn more about MongoDB Atlas, visit our product page . To get started with MongoDB Atlas Search, visit the Atlas Search product page .

November 25, 2024