Data is everywhere, but managing it—especially unstructured time-stamped data for edge AI—can feel like herding cats. For startups, small enterprises, and industrial automation companies, navigating the complexities of data management is a daily challenge. Often, you’re left wrestling with inefficient systems and struggling to integrate the necessary infrastructure that makes sense of your data chaos.
Imagine this: Developers focus on smart cities, researchers dive into groundbreaking discoveries, but the framework for handling their data isn’t swift or reliable. As a result, innovation can stall.
Fortunately, there’s a bright spot—ReductStore. This startup is leading the charge with a speedier time-series object store, simplifying the way organisations handle vast amounts of data.
In a recent interview with ReductStore Co-founder Anthony Cavin, we discovered how this dynamic team tackles the challenge of data management head-on. Let’s look closer at their journey and vision for transforming the edge AI landscape.
What is ReductStore?
ReductStore is a groundbreaking platform focused on time-series object storage tailored specifically for edge AI applications. It addresses the issue of managing large volumes of unstructured, time-stamped data which can overwhelm organisations. By providing a high-performance, easily integrable solution, it empowers startups, small enterprises, industrial automation companies, and research organisations to handle their data more efficiently.
In dealing with complex data management challenges, ReductStore’s architecture streamlines the storage process. It offers unmatched speed, drastically reducing the time developers spend on data logistics, allowing them to refocus on innovation. For instance, a smart city developer can now swiftly analyse sensor data without the delays often tied to traditional storage solutions.
What sets ReductStore apart is its unique combination of unstructured data management and time-series functionality. This dual capability ensures that users experience a seamless interface, fostering a straightforward workflow. By prioritising performance, it competes effectively in a crowded marketplace, positioning itself as a leader in edge AI data management.
ReductStore Founders
Anthony Cavin, one of the co-founders of ReductStore, brings a strong background in data science and artificial intelligence. His expertise in machine learning and analytics laid a solid foundation for tackling the challenges of managing sizeable unstructured data— a core issue for many organisations today. Alongside him is Alexey Timin, a senior developer and database expert proficient in Rust and C++. Their combined experience creates a dynamic duo ready to face the complexities of edge AI applications.
In the early days of ReductStore, the founders recognised a significant gap in the market. The management of unstructured data within IoT and edge computing environments was complex and inefficient. While many sought to innovate, they were often thwarted by outdated systems that couldn’t keep up with the demands of real-time data monitoring. Understanding this, Cavin and Timin decided to build a solution from the ground up, aiming to simplify data logistics for developers.
The motivation behind starting ReductStore was personal for the founders. A desire to streamline the handling of large data volumes drove them to innovate in a fractured market. They wanted to make artificial intelligence and big data more accessible for startups, small enterprises, and manufacturers. This vision led them to create a high-performance time-series object store tailored to meet the unique needs of the edge AI sector.
Their mission from day one was simple. They set out to deliver the fastest and most efficient time-series object store available, merging unstructured data management with time-series functionality in a way that sets them apart. This unique approach speaks directly to developers who need reliable, straightforward solutions that support innovation without the usual complexity of traditional systems.
Interview with Anthony Cavin, Co-founder of ReductStore
We had the opportunity to interview Anthony Cavin, co-founder of ReductStore, to gain a more in-depth understanding of what drives this startup and the people behind it. Here’s our conversation:
Q: Please introduce yourself and your role at ReductStore.
A: I’m Anthony Cavin, co-founder of ReductStore. My background is in data science and artificial intelligence, which has given me insight into the data management challenges facing many organisations today. Along with Alexey Timin, my co-founder who is a senior developer and database expert, we founded ReductStore to address these issues directly.
Q: Could you give us a brief overview of what ReductStore does?
A: ReductStore is designed as the fastest time-series object store for edge AI applications. It’s built to handle large volumes of time-stamped, unstructured data efficiently. The platform integrates easily into existing workflows, allowing developers and businesses to manage their data without the usual bottlenecks or delays. In short, we’re providing a high-performance solution that meets the specific needs of edge AI.
Q: Who is your target audience, and what problem does ReductStore solve for them?
A: Our primary audience includes startups, small enterprises, industrial automation companies, manufacturers, smart city developers, R&D organisations, and IT service providers. We focus on the challenge of efficiently managing large volumes of unstructured, time-stamped data—data that’s critical in edge AI environments. ReductStore makes it possible for these organisations to manage their data more effectively, giving them a streamlined, performance-oriented platform that supports real-time data analysis.
Q: What motivated you and Alexey to start ReductStore?
A: It started with a realisation. The tools available to handle unstructured data in IoT and edge computing environments were outdated, inefficient, and prone to causing delays in workflows. We saw the limitations firsthand. Managing large data volumes at the edge was a complex process, especially when it came to time-series data. We both wanted to simplify this process, so developers could focus on building innovative applications instead of wrestling with data storage issues.
Q: Can you tell us more about the early days of ReductStore?
A: In the beginning, it was all about problem-solving. We identified the need for a platform that could manage unstructured data and time-series functionality simultaneously, particularly optimised for edge AI. We built ReductStore from the ground up, with a focus on performance and simplicity. Our objective was to create a solution that developers could rely on, one that wouldn’t bog them down with the usual complications of traditional storage systems.
Q: What makes ReductStore different from other storage solutions?
A: Speed and specialisation. ReductStore is currently the fastest time-series object store on the market. It’s specifically designed for unstructured data and time-series functionality, making it unique. Traditional systems might handle structured data well, but they struggle with the kind of unstructured, time-stamped data that edge AI applications rely on. ReductStore bridges that gap, providing both speed and compatibility for edge environments.
Q: Have you received any external funding?
A: No, we haven’t taken any external funding so far. We’re a lean team, and we’ve been able to grow organically, thanks to the support and dedication of our early clients.
Q: Could you share your current revenue and customer base?
A: At present, our monthly revenue is around £10,000, with an average of one customer per month. It’s a steady start, and we’re gradually expanding our reach as more companies discover the benefits of ReductStore for their edge AI needs.
Q: What are your future plans for ReductStore?
A: We’re focused on enhancing our product offerings. One of our main goals is to develop a comprehensive data lake specifically for industrial data. This will expand ReductStore’s capabilities further, catering to industries that need highly optimised, high-volume data solutions. We want ReductStore to be the go-to platform for edge AI data management, and this development will bring us closer to that vision.
Q: What advice would you give to aspiring entrepreneurs?
A: Find a challenge that genuinely excites you. The process of building a startup is hard work, and the only way to stay motivated is by tackling a problem that means something to you. Keep your focus on creating real value, and don’t get sidetracked by the noise.
Feedough’s Take on ReductStore
ReductStore is not just another player in the crowded data management arena; it’s poised to revolutionise how businesses handle unstructured time-stamped data. With its speedy time-series object store, the platform significantly enhances performance for developers, which is crucial for smart cities and industrial automation. This focus on efficiency disrupts traditional data logistics, making it easier for organisations to innovate.
However, as they scale, ReductStore should consider strategic partnerships and external funding to accelerate growth. Their vision of a dedicated data lake for industrial applications is promising, and if executed well, it could position them as a market leader. Exciting times lie ahead!
A startup consultant, digital marketer, traveller, and philomath. Aashish has worked with over 20 startups and successfully helped them ideate, raise money, and succeed. When not working, he can be found hiking, camping, and stargazing.
