Wing-It is a limited liability company (LLC) established in Washington state in 2014 by Javier Andres Usandivaras. The company focuses on research and development (R&D) of cutting-edge technology in the Unmanned Aerial Vehicles (UAVs) industry. Wing-It has developed secure communication protocols for drone swarms, internet services supported by a solar-recharging UAV infrastructure, and visual recognition software for agricultural drones, among other innovations. It operates from a Research & Development facility equipped with advanced machinery and tools for electronics, prototype manufacturing, and radio frequency (RF) work, located in the Seattle metropolitan area.

My professional background is in software engineering, with experience at distinguished companies in both the technology sector (such as Microsoft and Google) and the finance industry (including Morgan Stanley, Credit Suisse, and Citigroup). I reached the highest levels of technical expertise, holding titles such as “Principal Engineer” and “Senior Staff Engineer.” These roles involved leading organizations by defining technological visions, implementing mission-critical software systems, and managing the human resources necessary to bring these projects to fruition. 

While working in the finance industry, I primarily focused on software systems for trading and proprietary financial research, specializing in cybersecurity and encryption. The emphasis on data security in financial institutions, which is even more fundamental than in technology companies, laid a strong foundation for building robust security measures later at Wing-It. This expertise proved invaluable in developing defenses against hacking and jamming attacks. However, during this time, my passion was drawn to Artificial Intelligence (AI), specifically Predictive AI, which analyzes trends and makes actionable predictions. Unlike Generative AI, which has gained popularity in recent years, Predictive AI serves entirely different purposes. I began exploring this emerging field as a hobby, dedicating years to learning and experimentation before transitioning towards professional engagements with the technology. 

In the mid-to-late 2000s, Artificial Intelligence was not a major investment area for financial institutions, as evidenced by its relatively low popularity at the time. For example, Google Trends indicates that between July 2008 and July 2013, searches for “Artificial Intelligence” in the New York City region barely grew from 12 million to 15 million, while the San Francisco region saw a much bigger increase from 67 million to 179 million in the same timeframe. Recognizing the need to immerse myself in an environment more invested in AI, I relocated to the West Coast in 2012 to work for technology companies such as Microsoft and Google, where I contributed to software solutions leveraging Predictive AI research and productization. 

Just as AI had captivated me in the early 2000s, the emergence of drones (or Unmanned Aerial Vehicles, UAVs) in the early 2010s caught my attention as another transformative technology. At the time, the UAV industry consisted of a small community of hobbyists experimenting with early prototypes in their garages. My first drone project involved repurposing a gyroscope-accelerometer sensor from a Nintendo Wii controller, soldering it to a basic processor to detect position, orientation, and acceleration, which allowed me to control the motors. 

Collaborating with this growing community, I contributed to open-source projects that eventually became foundational to the UAV industry. As the technology matured, interest expanded from tens of enthusiasts to thousands, leading to early commercial ventures. Recognizing the market potential, I formally established Wing-It LLC in 2014 to research UAV-related intellectual property and technology assets. And from this community of early-founders, I hand-picked a team of colleagues who became engineers at Wing-It and helped me develop its technological assets. 

Over the years, Wing-It made significant investments in equipping its facilities with cutting-edge tools and developing numerous projects. Many of these projects reached advanced stages, including beta and pre-production prototypes. Key initiatives included: 

• Agricultural Drones: Leveraging Predictive AI and image recognition to replace labor-intensive agricultural tasks, such as harvesting ripe fruit, which accounts for 60% of fruit farming costs. 

• Internet Service Using Drones as Infrastructure: Developing UAVs equipped with solar panels to provide internet coverage, especially in rural areas, disaster zones, and military contexts. This project required secure communication software resistant to hacking and jamming, drawing heavily on my expertise in cybersecurity. Though the project reached beta testing, it lacked the funding for large-scale deployment. 

• Stealth Drones: Exploring stealth UAV designs using shape optimization, radar-absorbing materials, and noise minimization. Operating under the assumption that RaDAR technology, which has been in use since the late 1930s was advanced enough to detect, identify, track, and target UAVs with ease, we initially focused on designing a stealth UAV to test these principles. However, after realizing the limitations of existing radar technology, we pivoted to developing systems for UAV detection, identification, tracking, and targeting. 

These foundational projects laid the groundwork for NIMBUS, Wing-It’s flagship system for UAV detection and defense. 

Networked Interceptor for Malicious Bots and UAV Swarms (N.I.M.B.U.S.) is an advanced system designed to detect, track, identify, and target Unmanned Aerial Vehicles (UAVs) with exceptional precision. It can detect group 1 (smallest) drones at up to 20km away, track up to 256 simultaneous targets, classify different UAV types, and infer potential intent (threats). The system is entirely composed of custom hardware and software, developed and built 100% in-house to meet the unique challenges of UAV detection and defense. 

In early late 2019 and early 2020, as the COVID-19 pandemic was gaining momentum and just before the stay-at-home mandates, we began exploring the concept of a stealth drone. At the time, we assumed that modern radar systems could already detect and track small UAVs with high accuracy. To our surprise, we discovered that even the most advanced radar technologies struggle to detect small UAVs, let alone identify, track, or target them. This realization marked the beginning of our independent journey into the fundamentals of radar technology. 

The COVID-19 stay-at-home mandate provided a unique opportunity to focus intensely on this area. By the end of this period, we had built our first iteration of a radar system that would eventually become N.I.M.B.U.S. Not long after, the outbreak of the Russia-Ukraine war highlighted the pivotal role of small and medium-sized UAVs in modern warfare. The widespread use of drones exposed vulnerabilities in even the most advanced militaries, confirming what we had independently discovered years prior: traditional radar systems were ill-equipped to handle the threat of small UAVs. 

Since then, we have focused on leveraging our extensive knowledge of UAVs, combined with the resources and assets we’ve developed over the years, including a Predictive AI engine, cybersecurity software, a set of over 500 unique UAVs, along with our expertise in Radio Frequency (RF) and radar technology. These efforts culminated in the creation of the world’s premier anti-UAV and anti-swarm system: N.I.M.B.U.S.. 

N.I.M.B.U.S. offers advanced capabilities for detecting, tracking, identifying, and targeting UAVs with precision and reliability. Its core functionalities include: 

• Detection: Tracks up to 256 UAVs simultaneously at ranges determined by environmental and atmospheric conditions, as well as the distance and location of the target. On average, for small (group 1) drones, the detection range is between 15-20km. Larger drones (group 4-5) could be detected up to 40-45km away. 

• Tracking: Monitors the altitude, heading, and flight paths of multiple UAVs, including swarms. 

• Targeting: Provides precise altitude (0-90°), azimuth (0-360° from magnetic north), and distance measurements (accuracy within 50cm) to external kill systems. Standard output is through PWM signals for automated turret control, with support for custom integrations of third-party systems. It can also predict a trajectory of the target and calculate an interceptor’s ballistic path. 

• Ballistic Projection: Calculates the optimal ballistic impact trajectory given the observed drone flight behavior and details about the specific kinetic solution. Using its onboard set of sensors it integrates all relevant information including wind speed and direction, humidity, air density, temperature, and more to minimize rounds for maximum interception efficiency. 

• Real Time Updates: Can provide push based or pull based information updates in real time in order to integrate with interceptors, command and control software, sensor fusion solutions, and other systems. 

• Classification: Differentiates UAVs based on several criteria: 

• Type: UAV versus non-UAV (e.g., airplane, helicopter). Non-UAV objects can be excluded. 

• Size: Small (less than 2 ft/60 cm), medium (2 ft/60 cm–4 ft/120 cm), or large (4 ft/120 cm–9 ft/270 cm). 

• Design: Multi-rotor or fixed-wing. 

• Make/Model: for more popular brands, it recognized specific make and models 

• Intent Inference: Utilizes a custom algorithm that combines map data, real-time event information, and flight path analysis to infer intent (benign or malicious) with a confidence level (0%-100%). Note: Internet connectivity is required for this feature. 

N.I.M.B.U.S. consists of three layers of custom solutions working together in a feedback-loop: custom-built hardware, in-house-developed software, and self-trained AI models. All three were designed and constructed from the ground up by Wing-It to meet the unique challenges of UAV detection and defense. 

The Hardware 

N.I.M.B.U.S.’s hardware features a custom radar system with the following components: 

• Antennas: Two antennas arranged in an array configuration (size and type configurations are flexible depending on the specific deployment goals, including parabolic or patch array, 600m, 900mm 1200mm or 1800mm in size). The antennas and RF signals are custom-made and precisely calibrated using our own equipment to maximize reflections of commonly used drone materials. 

• RF Chain: Designed entirely in-house and assembled using common and foundational RF components such as oscillators, attenuators, amplifiers, modulators, capacitors, resistors, and inductors. The transmitter and antennas are tuned to specific RF frequencies optimized for detecting small and medium-sized UAVs, frequencies not typically used by existing military radars. 

• Signal Processor: Signal digitalization and processing are performed with the latest widely available ADC, FPGAs, and GPUs/CPUs in order to maintain a simple and robust supply chain. 

• Control Unit: Motion control and basic UI functionality is controlled by traditional computer hardware and software, using secure version of commonly available operating systems. 

• Display: Touch-enabled display for user interaction and system management. 

The Software 

N.I.M.B.U.S.’s software is composed of multiple proprietary modules developed over years of research and engineering: 

• Signal Processing Software: This module processes raw analog radar signals to create a digital representation of reflected objects. While initially based on traditional Doppler, 3D, and Synthetic Aperture Radar (SAR) algorithms, it has been extensively customized to prioritize reflections from small and medium-sized UAVs. Moreover, the software works in a continuous feedback-loop with the RF hardware, utilizing a suite of sensors in order to optimize the algorithms for the current conditions at the time and place where the radar is deployed. 

• AI Layer: The post-processed signal is analyzed by a Predictive AI engine trained on thousands of hours of radar imaging data from approximately 500 unique UAV designs of various types and sizes, captured under diverse meteorological and geographical conditions in order to analyze flight path, reject false positives, predict trajectories, assess threat levels, and classify targets. 

• Output: The software provides several output features including simple PWM signal outputs for altitude, azimuth, and distance, enabling seamless plug-and-play compatibility with turrets and other targeting systems. 

• Integration: Additional integration with third-party kill-chain equipment is supported through Software Development Kits (SDKs) and Application Programming Interfaces (APIs). 

In short: N.I.M.B.U.S. outperforms the competition by orders of magnitude because it is purpose-built with custom hardware, software, and AI, designed entirely from the ground up for this specific application. More fundamentally, true innovation requires a deep unification of expertise across multiple domains. For N.I.M.B.U.S., this means combining profound knowledge of electromagnetic waves and radio frequency, unmanned aerial vehicles (UAVs), and Predictive AI in a single tight-nit team of experts in those fields, working without restrictions or bureaucracy. This multidisciplinary approach enables unique insights and solutions, overcoming challenges that would otherwise remain insurmountable. 

Attempting to distribute this work across multiple teams would also work but it would be inefficient. Each would see only part of the problem and, therefore, only part of the solution. A significant amount of collaborative time would be spent on knowledge sharing rather than productivity, and even after years of collaboration, each would retain only as much of the shared knowledge as they found interesting or relevant. 

Comparison with Competitors 

Large Corporations 

Large corporations, such as Raytheon, Lockheed Martin, and Boeing, rarely employ experts with simultaneous mastery of multiple disciplines. Their organizational structure inherently prioritizes specialization within teams, organizations and divisions. For instance, it is highly unlikely that Raytheon employs an engineer proficient in radar systems, UAVs, and Predictive AI simultaneously, if for no other reason because of the difficulty in finding such an individual. Moreover, given how these companies are organized, simultaneous mastery of multiple areas is completely unnecessary and wasteful. However, this compartmentalization slows innovation and inflates costs, often requiring years and millions of dollars to develop a system comparable to what a small, agile startup can create faster and more efficiently. This is why large corporations frequently opt to acquire smaller companies with specialized innovations rather than build new systems from scratch. (Further discussion of the challenges large corporations face is provided later in this document.) 

Other Startups 

While it is possible for other startups to develop a system like N.I.M.B.U.S., the odds are extremely low. Developing a radar from the ground up is a lengthy process. That is something that would translate to a very high cost with very high risks for any startups. N.I.M.B.U.S. benefited from Wing-It's original R&D focus for many years before becoming mature enough to be productized. 

While I may be the primary driver of innovation at Wing-It, I am supported by a dedicated team of engineers who have worked on our projects since its inception. Together, we’ve developed the building blocks that make N.I.M.B.U.S. possible. However, the foundation of this accomplishment lies in a combination of vision, expertise, and the dynamics of high-impact engineering, which can often outperform even the largest corporations. 

To explain, consider this analogy: In the late 1980s, before the internet became ubiquitous, technology companies recognized the need to organize vast amounts of unstructured information. Retrieving relevant data was seen as the next “holy grail” of technology solutions, and companies like Microsoft poured billions of dollars into solving this problem over two decades. Yet, it wasn’t these tech giants that cracked the code—it was two students, Larry Page and Sergey Brin, who developed an algorithm so effective it transformed how the world retrieves information. Starting with a few salvaged servers from their university’s IT department, their work became the foundation of Google. Despite Microsoft’s attempts to “squash” Google, including enormous financial commitments into the company’s search engine, Bing remains a distant competitor to this day. 

This phenomenon highlights a critical truth: true innovation often comes from small, agile teams or individuals with deep, multidisciplinary expertise. As Joel Spolsky eloquently puts it, “A good engineer can outproduce five, ten, or even more average engineers.” Exceptional engineers stand out not just for their productivity but for their ability to solve complex problems creatively, write maintainable code, and avoid costly mistakes. They amplify team output through leadership, mentorship, and superior design decisions. In contrast, even a large team of average engineers may struggle with inefficiencies, technical debt, and subpar solutions. 

At Wing-It, we embody this spirit. Over the past several years, we’ve combined our expertise and innovation to build the N.I.M.B.U.S. system, achieving a level of engineering prowess that even the largest corporations struggle to match. 

The superiority of our product over similar offerings from large, well-established corporations stems less from who built it and more from how it was built. As discussed earlier, pouring immense financial resources into a project doesn’t necessarily guarantee success. A powerful example of this principle can be seen in the contrasting approaches of Boeing, NASA, and SpaceX between 2011 and 2024: 

• NASA, with an annual $4 billion budget, developed three rockets for its lunar missions, but only one has flown so far, and it faced multiple issues. 

• Boeing invested $6 billion over 13 years in developing a capsule for ferrying astronauts to the space station but ultimately failed, requiring SpaceX to rescue the stranded astronauts. 

• Meanwhile, SpaceX, with only a few million dollars on its first year, and a fraction of the budget in its subsequent early years, achieved groundbreaking milestones: the first private orbital program, reusable rockets, a crewed commercial program, and the construction of the largest rocket in history, surpassing even NASA’s Saturn V. 

While we may be a small startup without the financial clout of larger corporations, we have what truly matters: technical expertise, passion, grit, and the freedom to innovate without the constraints of bureaucracy or legacy systems.  

At Wing-It, we approach engineering differently: 

• Exceptional Team: We have a small, highly skilled team of engineers with top-level experience at world-class companies. Each team member is deeply invested in the project, bringing passion and dedication that cannot be matched by the "assigned" teams at larger corporations. 

• Years of Focused Expertise: For over nine years, we have worked exclusively with drones and symbiotic technologies, building intellectual property, developing software assets, and exploring their applications in potential markets. This specialized focus gives us an unmatched understanding of the underlying technologies that make up N.I.M.B.U.S.’ subsystems. 

• Purpose-Built Hardware and Software: Unlike larger corporations that retrofit existing systems, we built N.I.M.B.U.S. from the ground up. Its hardware is custom-designed and specialized for UAV detection and tracking, while our software leverages years of prior development to achieve exceptional performance. 

• Flexibility to meet your requirements: The UAV and counter UAV landscape moves extremely fast. What was cutting edge 6 months ago is irrelevant today. Large corporations are too rigid to be able to adapt quickly enough to these changing environments. Being a smaller company, we can modify and customize our offerings to fit your needs today, next week, six months from now, and for a long time to come. 

Ultimately, clients should choose our product because it represents a combination of deep expertise, focused innovation, a passion for excellence, and desire to solve their unique problems—qualities that cannot be matched by the bureaucratic and fragmented approaches of larger corporations.