Understand human language regardless of format
Support users throughout their product journey
Bring intelligence to robotics by solving nontrivial tasks
Recognize key objects in a diverse and changing environment
Minimize human mistakes and speed up processes
Target users with personalized offers, content, and services
Secure AI training data, training pipelines, and ML models
Detect and prevent potential issues in advance
Augment AI behavior with human-like response
Unify collected data from various sensors
Start delivering personalization at scale
We applied computer vision and artificial intelligence development services to recognize objects around a car in real time to make automated driving safe for both pedestrians and drivers.
Our concept recognizes pedestrians on the road and applies machine learning algorithms to learn pedestrians’ behavior and predict their next move.
For an educational startup named Alphary, we designed and developed a language learning application with an AI assistant that analyzes students’ answers and accelerates vocabulary acquisition through recent and relevant feedback. The application relies on NLP algorithms with corpus statistics, semantic analysis, information extraction, and machine learning models. Intellias contributed to Alphary’s recognition by Oxford University Press for their breakthrough NLP solution in the educational sphere.
Learn more AI & NLP-driven language learning app
The Intellias team has built an innovative solution for an Android car keyboard application to recognize drivers’ handwritten text. Our experts have successfully conducted an R&D project that demonstrates our strong technological capabilities and background in neural networks, machine learning, and artificial intelligence. This solution is extremely intuitive, recognizing any natural handwriting: uppercase, lowercase, cursive, block, and even superimposed text. This solution is extremely intuitive, recognizing any natural handwriting: uppercase, lowercase, cursive, block, and even superimposed text. The application’s ability to capture handwritten content from scanned images and even suggest and predict words based on a user’s habits and writing style provides convenience to drivers and increases safety.
Learn more Handwriting recognition software for safer driving
Intellias team proved the feasibility of transition from RADAR/LiDAR sensors as the primary data source to stereo vision cameras for further development of a pseudo-LiDAR. This project proved that stereoscopic vision combined with object recognition AI-based algorithm can output data of comparable quality to that of actual Lidar/radar sensors. Moreover, simulated Lidar data has an advantage of higher signal density than actual Lidar data, so the strengths of these data modalities can be complementary.
Learn more Simulating Lidar signal with stereovision and algorithms
Intellias designed a contactless payment solution with client authentication based on computer vision and geofenced location tracking. We have provided automatic license plate recognition, car trajectory tracking, and automatic payment based on credit card information. Once the object detection analysis outputs a positive result, it triggers optical character recognition for the license plate detection. The AI&ML team has achieved 94.3% of recognition accuracy even at long distances, in poor weather conditions, and for dusty license plates.
Learn more License plate recognition for zero-click payments
A young Silicon Valley startup, our client is renowned for their ambitious fully electric hyper car concepts that are destined to define our future. Recognizing this, our client has invested lots of effort into developing cutting-edge electric vehicle prototypes equipped with advanced driver assistance systems (ADAS). We deployed a fully viable v3 implementation, entirely tested and adapted to our client’s requirements. Our team has become one of the first to implement this new standard in the automotive industry.
Learn more ADAS solution for electric vehicles
AI development services are mainstream. Most companies across industries are striving to adopt open-source and commercially available AI and machine learning solutions. Still, the market demands custom AI solutions for narrow needs. Adoption of AI development services goes hand in hand with the adoption of other technologies.
Integration of custom machine learning development and artificial intelligence software development with IoT and edge computing is expected to bring new possibilities for global businesses. Despite fears of AI stealing today’s jobs, people and machines should work together as a team to unleash the full potential of this technology. The AI augmentation of human engineering capabilities is expected to be key to the success of AI-integrated solutions.
When integrating machine learning and AI software solutions into business needs, you have to clearly define the deliverables you’ll get at each stage. Early adopters of AI development services may benefit from labeled data sets for evaluation and training of machine learning models, advanced analytics for data-driven predictions, and automation of routine tasks.
Moving forward, AI adopters can integrate already designed and trained neural networks into their machine learning solutions to increase efficiency at a lower cost. Another scenario is to introduce computer vision technology to recognize critical objects from video or another multimedia data source. When finalizing your AI implementation, you should approach a mix of machine learning solutions and models and integrate tools to visualize business outcomes.
After implementing AI and machine learning development services to optimize defined business functions, you may need to evaluate the benefits that AI software solutions bring. Some benefits will be common for all companies, such as automation of routine tasks, enhanced analytics, and increased efficiency thanks to better processing of large data volumes.
Other business outcomes may differ across industries. Retail companies may expect an increase in sales and reduced customer churn due to improved targeting of customers’ needs and tapping into new market segments. The automotive and urban mobility sectors will win from autonomous vehicles, financial services will gain improved security and fraud detection, and healthcare will benefit from automated medical diagnoses.