Built With MongoDB: Shipright

Siya Raj Purohit
November 11, 2020 | Updated: January 11, 2024
#BuiltWithMongoDB #Startups #DevOps

User research is fundamental to guide great design and define a company’s brand and viability.

For this edition of #BuiltWithMongoDB, we are featuring Shipright, a Netherlands-based Techstars company from the Netherlands that enables software businesses to seamlessly track customer feedback and make better-informed product decisions.

Shipright provides a central place for user feedback, collating suggestions that customer support receives and sharing it across the company. The tool allows product managers to connect with customers who have left feedback, and ask for more details or provide personalized updates.

Merging customer feedback ―traditionally spread around the company and left forgotten inside support conversations―into the product development process enables product people to deeper understand, and act upon, user requests.

We spoke with co-founder Steven Aanen about Shipright’s journey to centralize product feedback and how they are growing with MongoDB.

How did the idea for Shipright come about?

As a team of four college friends, we had collaborated on software products for several years and repeatedly saw how difficult it is for companies to understand what their users really need. The result is that product teams are too-often building based on the input of the loudest person in the room, typically one angry customer or a product manager’s gut feel.

With many new products hitting the market, and the cost of switching between them being so low, companies that fail to build something users love won’t survive for long.

The good thing is that software companies actually get lots of feedback through customer support, live chat, and online reviews. The problem is that this feedback lives in many places and across different teams. There wasn’t any way to get a clear picture of, and act upon, a user’s perspective. That’s why we created Shipright.

Shipright allows companies to easily collect feedback in one centralized location so that product teams get a clear sense of what matters. All feedback is transparent, from real users with associated identities, making it approachable and possible to open a dialogue. Teams that use Shipright collaborate with users to build with them instead of for them.

What were some of the early challenges in building Shipright?

Product management is inherently a very complex and messy process. There are many factors that come into play when defining product strategy. The challenge was to help streamline the process without creating a solution that is too complex or creates unnecessary overhead. We wanted to make it possible for any team to create amazing products. We pivoted a few times, from focusing on user research to incorporating user perspective deeply into product roadmaps.

Another challenge is dealing with the variety of systems in which feedback and user data lives. Our solution is to offer a browser plugin that consolidates feedback across several channels and integrations.

What technologies are you using to help build Shipright?

Shipright is built in Vue.js, Typescript, NodeJS, and, of course, MongoDB. We run the platform on Kubernetes, where we also deployed a bunch of microservices and a message queue to process asynchronous jobs.

A fun fact is that every customer has their own database, which is something we started with to ensure data protection.

Why did you decide to have Shipright #BuiltWithMongoDB?

MongoDB allows us to be flexible as we learn to support our customers in the best possible way. Functionality changes regularly, and it’s super important for us to be able to adapt our course as needed.

We initially picked MongoDB for quick prototyping of the MVP that would later become Shipright, and it stuck. At first, we hosted the product ourselves, which quickly became very challenging. We regularly had to pause our work to fix things in production. With help of our friend David Asabina, we switched to a new hosting infrastructure, including MongoDB Cloud. That really helped us to get our focus back on product development instead of managing servers.

Now that we have worked with MongoDB for nearly four years, we deeply understand the API and leverage our compounded knowledge to ship updates even faster.

Which MongoDB products do you use to power Shipright?

Our hosting infrastructure is defined using Terraform. We have a staging cluster and production cluster in MongoDB Atlas, and if necessary, we can spawn up a new cluster that looks exactly the same. On every signup, one of our microservices creates and prepares a new database specifically for that customer.

In the near future, we’d like to try out Atlas Search as well, as we rely heavily on search engines to power the product. We’re fans of the MongoDB ecosystem and are excited to grow with the platform.

Looking to build something cool? Get started with the MongoDB for Startups program.

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How Growth and Leadership Foster Change In The Tech Space

Editor's note: This was originally published on AfroTech’s website . Are you on the hunt for your next role in the tech space? We know firsthand just how difficult it can be to find a company that is truly committed to investing in Black talent, so we’re highlighting a few of our top picks that are currently hiring. First up is MongoDB — with over 2,200 employees, this New York-based tech company provides database software that powers companies like Epic Games (best known for its popular game Fortnite), Shine Text, Coinbase, 7-Eleven, and more. MongoDB prides itself in cultivating an environment for employees to share their ideas about diversity and inclusion in the workplace without judgment. The general purpose database platform focuses on elevating productivity and scalability for both its clients and employees, which sets up a space for everyone to shine. The company offers opportunities for Black tech professionals to thrive as it values different thoughts and perspectives and those who approach tech solutions in a unique way. No matter their gender, race, age or sexual orientation, employees are valued and respected by MongoDB’s leadership. Its open environment encourages employees to perform at their best and elevates the success of the company. Let’s explore one employee’s experience and his unique role at the company. Tosin Ajayi leads MongoDB’s global corporate Solutions Architect team. He is a prime example of how a Black tech leader creates and influences an inclusive company culture for all employees. Ajayi uses his position to promote growth, leadership and foster change. For example, he’s currently building an Associate Solutions Architects team . The team is suited for junior or early-career professionals and provides them a great start to a highly coveted technical presales career. “The presales role is unique as it combines the technical prowess of an engineer, the vision of a product manager, the sales acumen of a sales rep and the design and troubleshooting skills of a consultant,” Ajayi said. “In essence, it bridges several functions within an organization to bring solutions to our customers and revenue to MongoDB,” all while furthering MongoDB’s goal of inclusion as a top priority. While Ajayi believes that practicing inclusion is everyone’s responsibility, he asserts that MongoDB’s tone about inclusiveness is set at the executive level, which helps such a culture thrive. Here are concrete initiatives that MongoDB has implemented to support diversity in the workplace: The company holds all-hands meetings where the executive team takes open questions. For a company this size, it’s quite impressive considering the diversity of thoughts and opinions in a large employee base. And yes, the questions often reflect that diversity in thoughts and opinions. MongoDB signed the ParityPledge to ensure that at least one qualified woman candidate is interviewed for all VP and higher positions. There is a company-wide Decoding Inclusion series that addresses a variety of topics like race, the LGBTQ+ community, and mental health. MongoDB is really big on feedback. Surveys are consistently run in order to seek to understand employees’ visceral feelings towards their work, their team, team makeup, leaders, workplace, and work conditions. MongoDB has a dedicated D&I team. In fact, this interview is happening as a result of the great work Cindy Class and Danielle James are doing. Companies across the country are tackling current events such as the disproportionate rate of COVID-19 affecting Black people and the violence at the hands of police. These events require open dialogue for employees to express their thoughts and feelings and MongoDB has done just that. Ajayi agrees that it’s important to have these conversations in order to “disambiguate the stance on human decency issues and promote inclusiveness”. “Employees aren’t a monolith,” Ajayi said. “Yes, we’re a collection of driven and talented professionals — but well above that, we’re human beings. Employees want to feel heard, they want to know that their feelings and opinions matter, they want a company whose philosophy they can align with. Talking about current events is a display of awareness, it shows a sense of connectedness to the outside events that can and often affect employees. More importantly, it shows empathy and support for employees.” These candid conversations help Ajayi as a leader, allowing him to “address the historically taboo topic of race and racial injustice.” When topics such as these are addressed it impacts the success of the company positively and creates a “psychologically safe environment” at work. “Another point here is that employees are only as good as they feel,” Ajayi said. “I find that people give more of themselves when they’re in a space where they feel psychologically safe.” MongoDB continues to promote their mission of inclusivity and diversity through various initiatives like scholarships to their MongoDB World conference, an Intern Mentorship program, affinity groups like The Underrepresented People of Color Network (TUPOC), Queeries, MDBWomen, Underrepresented Genders in Tech, Veterans, and the Green Team, and the company’s Decoding Inclusion series that was launched last year by the Diversity and Inclusion team. “The [Decoding Inclusion Series] is an opportunity to educate and sometimes challenge preconceived ideas about D&I,” Ajayi said. “[These sessions] are sponsored by MongoDB executives. We feature employees and bring guest speakers to talk about a variety of topics including race, gender, mental health issues, and other topics that pertain to D&I.” Ajayi revealed that he found sessions like the most recent Decoding Inclusion conversation on race very informative, resulting in his own self-evaluation about his understanding of community and societal differences. He is proud to see these types of programs not only deconstruct the taboo topic of racism in the workplace, but make changes as a result of it. “I encourage all organizations to embrace the humanity of their employees, not just the workers in them, and promote an environment where people can talk, like my company has done for us,” Ajayi added. MongoDB is dedicated to creating opportunities to impact change, not only at the company but throughout the community. Are you a tech-minded dreamer, who is passionate about innovation? Grow your career at MongoDB, view open roles here and make sure to indicate that you learned about the role through AfroTech when applying. Join MongoDB in supporting organizations fighting for racial justice and equal opportunity. Donate to our fund by December 31, 2020 and MongoDB will match the donation up to a maximum aggregate amount of $250,000. Learn more here.

November 10, 2020

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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024