Built With MongoDB: ChargeHub Simplifies the Electric Charging Experience

Steve Jurczak
October 26, 2022 | Updated: January 4, 2023
#BuiltWithMongoDB#Startups

While the market for electric vehicles (EVs) continues to expand, several barriers to adoption continue to prevent buyers from making the switch. One of the top concerns among potential buyers is access to charging stations. Currently, there are more than 150,000 charging stations in the U.S. and Canada, but you wouldn't know it by driving along a highway or through a densely populated area. That's because unlike gas stations, charging stations are not advertised on the side of highways or with huge commercial signs. Quebec-based startup, ChargeHub, is out to solve this problem.

"ChargeHub's mission is to simplify the electric vehicle charging experience," says ChargeHub Co-founder and CTO, Olivier Proulx. "If we simplify it enough, it will increase electric vehicle adoption." With the ChargeHub app, EV owners can locate charging stations anywhere in North America and know if they're available for charging.

ChargeHub is also a member of the MongoDB for Startups program, which helps startups build faster and scale further with free MongoDB Atlas credits, one-on-one technical advice, co-marketing opportunities, and access to a vast partner network.

Company origins

Even though the app is the company's main focus today, it's not how ChargeHub started. Proulx says he and the co-founders worked as consultants in the EV space before EV cars were even on the road. "We were building electric off road vehicles,'' Proulx says. "Clients were asking us, where are the charging stations in Canada? And we thought the easiest thing to do was to build an app that would show that."

After putting the app on the app store for free, the number of downloads convinced them that knowing the location of charging stations was a real problem. So, they diverted their attention away from consulting and started putting more effort into the ChargeHub app.

Evolving the app

It's not just finding a charging station that EV owners find problematic. When you get to a charging station, you need to pay for charging. The market is highly fragmented, Proulx says. In North America, there are currently over 35 operators of charging stations. When you go to use a new one, you have to sign up for an account with the charging operator and deposit funds into the account to pay for charging. With so many different operators, you wind up with multiple accounts, each with a balance. "With ChargeHub," Proulx says, "you can create one account and charge at over 70% of the charging stations in North America."

Today, when EV owners find a station using the ChargeHub app, they can find out if there's a port available before they get there. And, once they start a charging session, the app shows that the charging session has started. That's a lot of transactions that have to happen in real time to ensure a seamless user experience. Proulx says MongoDB Atlas, when he compared it against other databases, gave them the performance they needed at a cost that made the decision easy.

Building with MongoDB

Booth learning session at MongoDB World, 2022.
ChargeHub Co-founder and CTO, Olivier Proulx, describes the EV charging experience for attendees at MongoDB World 2022.

Proulx says that the choice to build with MongoDB Atlas from the beginning was critical to its early success. "MongoDB Atlas helped us get the product up and running on a stable, scalable platform from day one," Proulx says. "We didn't have to worry about having to migrate later. And it helped us prove our concept without having to spend too much."

Getting free Atlas credits from the MongoDB for Startups program also helped. "When you're building a product and going to market, you're trying to save every penny that you can and extend your runway," Proulx says.

The security of Atlas was another key consideration. "Having industry-standard security was critical because we work with electric utilities that are very strict on security," Proulx says. "With MongoDB Atlas, being able to check that box from day one was really critical."

Like a lot of startups, the ChargeHub team had to be strategic about where it focused its resources. Managing a database was not part of that strategy. "We were a small team, we didn't want to have to run our own hardware, we wanted everything in the cloud as a service," Proulx says. "Being able to focus on building our solution instead of running things was critical for us. And being able to pick our cloud provider was helpful in managing costs."

Cloud flexibility was a big factor for the ChargeHub team according to Proulx: "MongoDB makes it really seamless to pick your cloud provider. And they work with all the main cloud providers. It makes our security policies easier to maintain."

Leveraging the cloud depends on how well you're able to integrate it into your existing tech stack. MongoDB scored high marks in that regard. "Our tech stack is based on Node.js and JavaScript. The connection with MongoDB and the document model was so seamless," Proulx says. "Even the Query API fits so well with Node and JavaScript. So for us, it was a no-brainer to go with MongoDB."

The road ahead

ChargeHub's goal is to reach 100% coverage of charging stations in North America. As EV infrastructure expands, and as more people know that a charging station is never that far away, Proulx says people will be less reluctant to choose an EV for their next car. If the feedback he gets from his users is any indication, new EV buyers don't have anything to worry about. "By having an app and a consumer product, you get feedback from your users," Proulx says. "It's so fun to hear from our users who go on road trips and use ChargeHub to go see the mountains and charge on the way. They're so happy they can finally use one app to charge anywhere they want."

If you're looking into an electric vehicle or you already have one, download the ChargeHub app for iOS or Android. Or you can try the all new web experience designed specifically for Tesla drivers to use in the Tesla browser. And be sure to reach out to the ChargeHub support channel if you have feedback. They're always looking to improve the app experience.

Are you part of a startup and interested in joining the MongoDB for Startups program? Apply now.

For more startups content, check out our Built With MongoDB blog collection.

← Previous

Modernizing Banking Technology Architecture with MongoDB and Gravity9

The banking industry has historically relied on legacy, on-premises systems to store critical financial data in a secure and resilient technology fabric. Today, as transactions happen in real-time and customer demands soar, legacy systems fail to support accelerated modernization and restrict innovation. This change has driven banking enterprises to consider transitioning to newer technologies that can plug better capabilities into business-critical systems while preparing them for tomorrow's challenges. By leveraging MongoDB, Gravity9 enables customers to migrate from legacy systems to more sophisticated technologies in a planned and piecemeal technique. In its effort to modernize technologies that are slowly turning obsolete, Gravity9 recognizes and retains the prominence of legacy systems while migrating with minimal disruption to the status quo. This article describes how Gravity9 performed a data offload from a core retail banking platform to MongoDB using a digital decoupling approach, as shown in Figure 1. Figure 1: Technologies used – Kafka Streams, MongoDB Kafka connectors, Apache Avro, Spring WebFlux, Spring Data Reactive MongoDB repository. The challenge Gravity9's client encountered many performance issues with their core banking platform, Temenos 24, backed by Oracle. Their attempt to offload frequently accessed data from the primary system to the Oracle database failed to give the edge of performance and scalability. The platform was being leveraged for analytical and transactional operations and could not cater to both areas as envisioned. In some instances, this approach resulted in customer transaction rejections. Additionally, the client faced the following challenges: The offload platform proved difficult to manage and extend with most of the logic for business processes written in the stored procedures. The data model within the core banking system was not inherently relational, which amplified the complexity of the platform due to the presence of only two columns: RECID for the ID or Unique Key of the record, and XMLRECORD to store the data. The complexities of the old data source compelled the client to adopt a migration strategy and gradually move into MongoDB, which offered better scalability and a flexible document data model while also migrating the corresponding business logic code. The client expected an improvement in overall performance, maintainability, extensibility, and the potential to deliver better customer experiences. The approach Gravity9 leveraged a digital decoupling approach to create message-driven microservices on top of the client's data. The approach was oriented to a continuous migration process executed in parts rather than in one fell swoop. Using this methodology, Gravity9 could help the client move from the old offload system to MongoDB by building fully scalable microservices for each business domain. The biggest advantage to this approach was that the client's legacy systems could continue as usual behind the scenes. Using a CDC pattern on the underlying Oracle database, the data from the core banking system was gathered, and snapshots modified in the core legacy system were published in real time. An event was generated every time a modification was made in the legacy/core system to keep track of changed data. Gravity9 built an application to learn about the new changes, transform the raw XML format into a structured message and refine it as necessary while broadcasting it as a message to other customers, who could process it or store it on the new MongoDB datastore. Specific dictionaries were developed for this purpose with clear directions about field markers and object structure for the transformed objects. The outcome Although the execution was intended to offload only a part of the data stored on the client's old system, the approach used helped build capabilities to support future migrations of larger volumes. The use of MongoDB Atlas for the implementation delivered efficiency and security in data offloading and met the desired level of performance. Consequently, it resolved scalability issues that would otherwise have occurred with on-premises systems. Although the migrated use cases work on MongoDB-based offload, the implementation left the remaining use cases untouched, allowing them to work on the older systems without handicapping the functioning. Seeking to modernize your banking technology environment? Speak to us today to learn how Gravity9 and MongoDB can help you make the best of new technologies and secure your performance-critical financial data.

October 25, 2022
Next →

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