Oleg Kadolka, our industrial designer, explains the nuances of his job and shares his personal experience at EnCata – a service company that specializes in design development and industrial prototyping for hardware and deep-tech startups. This interview is brought to you by EnCata’s content manager Vera Vasilevskaya.
Our business analyst Darya Medvetskaya gives a thorough overview of the Discovery Phase in IT and hardware projects. In this article, you'll learn about the processes that occur in both types of projects, as well as study their differences.
While developing a product, it is critical to determine a product development timeline and its key milestones, especially at an early stage. Authored by Vera Vasilevskaya, EnCata's content-manager.
Defining and specifying the future hardware or IoT product is crucial for your hardware engineers to estimate future R&D, design, and prototyping costs. Any professional engineering design consultancy can help you make the journey to market, but this guide lets you move faster and save some budgets.
Peter Dudin, the CBDO and Co-founder of EnCata, briefly describes EnCata's unique services, the story behind its inception, and other parameters responsible for the company's success. Check out how the commencement of EnCata (Engineering Catalyst) took place in the interview to the GoodFirms team.
Professional industrial designers, engineers often say POC, EVT, DVT, PVT, while business literature and your investors want you to have an MVP and your innovation commercialization consultants keep talking about TRLs. Let’s disentangle all these acronyms and tie them together in a “how-to” manner.
The COVID pandemic has dealt a serious blow to some industries from the outset, only growing in scale and impact. Many businesses are faced with the challenge of how to stay afloat, others started seeking various ways to contribute to the fight against the virus. EnCata took that challenge and found a way out!
Over the past weeks, EnCata design and manufacturing team has quickly adapted and shifted gears to developing and manufacturing several anti-COVID products. This is what EnCata does best: rapidly design, engineer, prototype and produce hardware products.
A high precision body scanner for fashion retail. The scanner design is a rotating platform with a set of digital cameras measuring body parameters with. Digital images are then transferred via Wi-Fi interface to a PC and undergo real-time conversion into a digital 3D model.
We have designed a humanoid robot “ADAM” for educational purposes. The Robot is paired with proprietary VR software that enables motion control. Robot is now introduced for robotics and programming classes in schools and universities.
An autonomous warehouse robot project with 50 kg payload. The robot has IP-45 protection mark and operates both indoors and for short periods outdoors. EnCata refined the startup’s concept and delivered the industrial design, DFM and produced the prototype with all documentation. The robot competes in the same space as KIVA robots from Amazon and QUICKTRON from Alibaba.
A fully automated drone hatch for Arctic and desert environments. The hangar was designed and manufactured from scratch. The hangar is equipped with the advanced drone positioning system, custom drone chargers and smart HVAC system.
Custom dust- and moisture-resistance IP66 enclosure for automotive application, accommodating a custom PCB. EnCata delivered the industrial design, DFM and manufactured in-house a steel mould along with the first batch and product assembly.
Hi-tech entrepreneurs and academics are concerned with where and how much investment they require to commercialize their lab technology while investors want to know “is that technology good to invest in?” If you have a good technology or idea and thinking of commercializing it, consider this text as a road map.
Modern deep-tech start-ups are not just guys making a piece of hardware in a garage for fun. These are advanced, cohesive teams of like-minded people, who are building market-ready products to solve specific customer problems. They’re using dedicated hardware designs with core technologies (IP) transferred from research labs and seamlessly integrated with cloud software.
For the past 25 years, I’ve seen thousands of times when a person makes errors — but never when a machine makes a mistake. Today, a blunder in the learning projects can cost companies millions and several years of useless work. For this reason, the most common errors in machine learning related to data, metrics, validation, and technology are collected here.
This is a guest post by Oleg Kondrashov, the CEO of EnCata, featured at TechStartups.com