CogniCode - Let us Introduce Ourselves

Smart devices have integrated themselves into almost every aspect of our lives. Even the way we do business has been digitized. However, software efficiency is starting to struggle to fulfill the demands of this modern way of doing business.

Machine learning presented itself as a solution to this problem. Implementing machine learning into app development tackles this problem by making apps faster and more autonomous. This, however, demands a complex system of tools and resources to achieve effectively. That is most definitely a big challenge for one company to take upon.

Most companies specialize in one part of the production process and outsource the rest. However, that kind of solution brings out a myriad of other problems. Cooperation, coordination, and communication between teams become difficult and deliver a product of lower quality, considering the resources invested in its production. For that reason, we at CogniCode worked on building up our infrastructure, assembling teams of experts, and developing our methods, to provide the best possible result for our clients.

Why Is Building Smart Apps So Challenging?

Machine learning makes it possible for apps to learn, adapt, and make decisions on their own. Still, many intelligent actions we do on a day-to-day basis using different applications can be very challenging for a machine to perform. As we start to delegate more and more of our tasks to our apps and devices, the demand for reliable, fast, and, most importantly, autonomous software is increasing.

Engineers use machine learning to generate innovative and clever software solutions that facilitate our modern lives. However, that is not always as straightforward as it seems. To fulfill these demands, app developers and machine learning engineers must work in concordance to develop software solutions that can draw the maximum from their respective industries. That’s easier said than done! Many issues arose from the attempt to implement machine learning in app development, such as a lack of training data, not meeting the infrastructure requirements for testing, a lack of business model flexibility, a lack of expertise, and many others.

What Makes Us Stand Out

We, here at CogniCode, studied these issues in detail and assembled all the necessary elements to combat them effectively. We made it our mission to form a bridge between app efficiency and machine learning reliability. Over the years, we established and perfected our technical infrastructure and development practices. Reliable data infrastructure, a wide range of tools, and witnessed experts can guarantee our clients a fast, production-ready, and scalable product.

Already veterans in this field, we can consistently provide healthy machine-learning ecosystems, high-quality apps, and seamless integration between the two. Through numerous deployments and iterations, we effectively modified and improved our methods. We were so confident in their superiority that we included them in our machine learning service, which offers our clients bespoke search algorithms and recommendation engines.

Our Leadership Codes

Above all, we are unapologetically an engineering company with people at its core. We are a group of engineers and scientists with extensive experience in our respective fields. Our engineering teams have diverse knowledge and skills, as they are proficient in many programming languages and tech stacks. Front and back engineers, data scientists, machine learning engineers, data architects, cloud architects (GCP, AWS, Azure), and UI/UX designers are some of the experts here in CogniCode. However, our pursuit of highly qualified experts doesn’t stop here, as we are still a growing company. Our organizational structure revolves around these three significant teams: app engineering, machine learning, and infrastructure. Good communication and close cooperation between these teams ensure the seamless integration of their technologies.

App Development That Surpasses Expectations

After carefully reviewing and collecting knowledge from deploying production infrastructure, we handpicked our preferred tools. We considered many aspects of each technology to make the most efficient core stack for our developers. The performance, ability to integrate with production architecture, the prevalence of the technology, and availability of qualified developers on the job market are just a few. Our core stack improves but doesn't make limitations on what we provide. Many of our clients have preferred tools that differ from our core stack, and we are happy to adapt to their needs. Clients know their business better than we do, and our mission is to unite our knowledge with their expertise to obtain a product that will serve them in the long term. High-performing and complex apps or massive scalable data infrastructure with well-developed machine learning ecosystems - CogniCode delivery will exceed your expectations. Our customer base stretches across many industries, stages of growth, and technical needs, so we are familiar with company-specific needments at every step or capacity.

Our Preferred Tech Stack

Our technical experts have extensive knowledge of numerous tech stacks and programming languages. From the tools we use to the finished product, the flexibility allows us to accommodate every unique requirement of our clients.

For application development, we use React with Next, Typescript, and WebAssemply. When it is machine learning, our preferred languages are Python, R for statistical research, PyTorch, and Spark ML/MLLIB. Our team relies on Rust for high-performance computing. We do infrastructure orchestration thanks to Luigi, and Airflow, and use message queues with webhooks. Using MongoDB, PostgreSQL, Spark with Hadoop, and Redis for data storage, give us the results we expect. FastAPI framework, Express.js, and Flask frameworks are our preferred choice for API development.

Why Work With Us?

Our finest strength lies in our capability to provide a high-quality product expeditiously. Fast delivery without the detriment of quality makes us stand out from our competition. Our heavy focus on standardization and infrastructure as code allowed us to use our resources efficiently and effectively. We have put a lot of effort into optimizing our tools and keeping them up-to-date.

We don’t want to give our clients a choice between a fantastic app or smart, data-driven features - we want to provide them both. This company was started with a vision to bridge the gap between high-quality apps and machine learning. We never stopped pursuing that vision and pushing our limits to provide our clients with a product that can grow alongside them.