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AI-based Web Solution: X2 Sales Rise for Custom Sports Clubs

AI-based Web Solution for Maximizing Sales

AI-based Web Solution: X2 Sales Rise for Custom Sports Clubs

The team developed an AI-based solution to automate the analysis of golf players’ positions and strokes, to boost sales for custom golf clubs manufacturing business.

#AI

#WebDevelopment

#ComputerVision

Client*

The client is a major manufacturer of custom golf clubs.

*We cannot provide any information about the client or specifics of the case study due to non-disclosure agreement (NDA) restrictions.

Project in numbers

duration

3 months

team

6 specialists

Team involved in the project

industry

Sport and Entertainment

solution

AI-powered analyzer for golf players

technologies

TensorFlow, Keras, Python, OpenCV, Mediapipe, LabelMe, MLFlow, NumPy, Colab, Matplotlib

1 x Lead Data Scientist

2 x Data Scientists

1 x Data Engineer

1 x Project Manager

1 x QA

HIRE THE TEAM

Challenge

Develop an AI-based product for analyzing golf players’ positions and strokes in order to design individually fitting golf clubs. The client was already using computer analysis software for analyzing the players’ movements and consistency of strokes and wanted to fully automate the process.

Solution & functionality

The team created an AI-powered solution capable of recognizing the golf club in a player’s hand and correctly estimating the angles of his joints (the posture).

Detection of player’s positions and golf club

Our team developed a model that successfully identifies the position of a person’s body, including their arms and legs, as well as the position of the golf club, using computer vision technology.

Robust pose estimation

Our developers enhanced the model to gather additional information by measuring the angles of specified body joints through computer vision technology. Data is captured either via a mobile device camera or via a pre-installed kiosk with a camera.

Collection, analysis and processing of advanced metrics

Timspark’s specialists developed and fine-tuned the classifier model to meet the project’s unique requirements. The model analyzes received images, determines the average class among all attributes of captured objects, and subsequently identifies the target audience for the advertisement.

Results and business value

The product was developed as an MVP. All the intended functionality operates with the help of computer vision and artificial intelligence technologies.

Benefits for client

The client remained highly content with the quality and speed of the team’s work. By successfully implementing the technology into their sales process, the client doubled their sales.

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    Computer Vision Solution for Effective Advertising Placement

    computer vision device

    Computer Vision Solution for Effective Advertising Placement

    Our team developed an on-premises device based on AI technologies for detecting individuals and showcasing advertisements on DOOH displays in transportation or outdoor locations.

    #AI

    #WebDevelopment

    #Ecommerce

    Client*

    The client is a world-leading provider of comprehensive visual technology solutions.

    *We cannot provide any information about the client or specifics of the case study due to non-disclosure agreement (NDA) restrictions.

    Project in numbers

    duration

    11 months

    team

    5 specialists

    Team involved in the project

    industry

    E-commerce

    solution

    Computer vision and artificial intelligence device for target audience analysis

    technologies

    Python, Pytorch, Cnvrg.io, AWS SageMaker, GCP Vertex AI, Fastdup, Pandas, Numpy, Scipy.

    1 x Lead Data Scientist

    2 x Data Scientists

    2 x Data Engineers

    HIRE THE TEAM

    Challenge

    The fundamental idea was to create a device harnessing AI technologies for analyzing captured images, discerning the audience’s average attributes, and enabling the presentation of targeted advertising.

    Related objectives

    Deploy the computer vision model on edge devices with limited GPU and RAM

    Train the AI computer vision model with labeled data

    Solution & functionality

    In collaboration with the client’s team, Timspark created a compact on-premises computer vision device that can capture and analyze visual data.

    Machine learning model training for successful object recognition

    The team developed and fine-tuned the classifier model to meet the project’s unique requirements. The model analyzes received images, determines the average class among all attributes of captured objects, and subsequently identifies the target audience for the advertisement.

    Deployment on Jetson Nano and Jetson Xavier

    To address challenges linked to the limited memory and slow data processing, the model was transformed into ONNX and TensorRT formats, ensuring its seamless deployment on edge devices such as Jetson Nano and Jetson Xavier.

    Results and business value

    Our specialists successfully developed a classifying model that detects and tracks individuals within the camera’s field of view. The model analyzes the captured visual data, accurately identifying selected class among all attributes, and displays targeted advertisements on nearby screens in accordance with the specified class request.

    Benefits for client

    The client successfully applies the computer vision software to show the advertisement effectively to the relevant audience.

    Based on the successful results, we are going to improve the model further in our ongoing collaboration.

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    We appreciate your interest in Timspark

      ML in banking: 5 times less fraud risk by spotting weird transactions

      Machine learning in banking
      ML in banking: 5 times less fraud risk by spotting weird transactions
      Timspark leveraged the power of machine learning in banking to keep an eye on digital transactions and catch any abnormal behavior with a new extension for the existing client’s system.
      #Fintech #Banking
      #MachineLearning
      #DataAnalytics

      Client*

      We have partnered with a major bank that has branches all over the US, providing loans, deposits, and more banking products.
      *We cannot provide any information about the client or specifics of the case study due to non-disclosure agreement (NDA) restrictions.

      Project in numbers

      duration
      19 months
      team
      13 specialists

      Team involved in the project

      industry
      Banking, Fintech
      solution
      ML-based system for fraud risk analysis and detection
      technologies

      Python, Scala, DVC, MLFlow, Comet, Apache Spark MLLib, Scikit-learn, LightGBM, XGBoost, Hyperopt, PySpark, Numpy, Pandas, Scipy, Docker, Docker Compose, Kubernetes, Jenkins

      2 x Frontend Developers
      2 x Backend Developers
      1 x Project Manager
      1 x Business Analyst
      2 x Data Engineers
      3 x ML Engineers
      1 x QA Engineer
      1 x UX/UI Designer
      HIRE THE TEAM

      Challenge

      The key American bank faced rising financial fraud threats, and traditional systems proved ineffective. We were picking the best ways to use machine learning in banking and finance against increasing fraudulent activities that endangered customer safety and the bank’s reputation.

      Related objectives

      Implement ML for fraud detection
      Upgrade the anti-money laundering system
      Increase customer safety
      Improve the bank’s reputation

      Solution & functionality

      We suggested adding an ML-powered extension to the banking system to scrutinize large data volumes and protect funds from malicious activities. It analyzes account holders’ transactions and raises alerts for any unusual, suspicious, or fraudulent behavior. With deep learning fintech algorithms, our team processed extensive data to spot irregularities signaling potential fraud risk.

      Aggregating data

      To begin, our engineers collected and unified all banking data, encompassing user identities, transaction histories, locations, payment methods, and other pertinent factors.

      Detecting anomalies

      We identified distinctive patterns like high transaction amounts or segmented transactions to avoid automated tax reporting, enabling ML algorithms to distinguish fraud from regular banking. Transactions are tagged as “good” or “bad”.
      We also accessed a vast dataset, efficiently spotting patterns and anomalies, and selected crucial features through data comparison and elimination techniques, improving fraud risk analysis and detection.

      Training the ML model

      Our ML team created algorithms to catch odd situations that slip past regular rules. This extension can predict even with less data, using smart machine-learning methods. So, our solution uses embedded representations, not typical features, to handle transactions.

      Implementing the ML model

      Once a threat’s spotted, the system shoots real-time data to the admin, who can stop or nix operations for further digging. Depending on the fraud chance, there are three outcomes:
      • If fraud odds are below 5%, the transaction gets the green light.
      • If the odds range between 6% and 70%, an extra check like an SMS code, fingerprint, or secret question is needed.
      • If the fraud chance tops 80%, the transaction’s axed, needing hands-on analysis.
      Plus, we set up good ML tools to explain models, making predictions clear and keeping things smooth for users.

      Results and business value

      Timspark’s top-notch ML extension spots fraud and takes action. Security’s solid — no breaches or financial crimes.

      x2.4 speedier in processing

      Our ML algorithms swiftly handle heaps of data, keeping up with the rapid transactions.

      99.3% accuracy of fraud detection

      Using these algorithms, we find tricky patterns that humans might miss. That means fewer mistakes and less unseen fraud.

      Less mundane tasks

      Our solution checks hundreds of thousands of payments per second, making the transaction process as painless as possible.
      The algorithms catch tiny changes fast, checking tons of payments per second. The bank gets tighter security, faster transactions, and less chance of missed fraud. It means smoother banking and peace of mind for the end customers.

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      We appreciate your interest in Timspark

        Health Management App: Cross-platform AI-driven Solution for Asthma Treatment

        AI/ML-based app

        Health Management App: Cross-platform AI-driven Solution for Asthma Treatment

        The team built a user-friendly asthma care app for iOS and Android platforms from the ground up, seamlessly integrating AI and ML algorithms.

        #healthcare

        #ai #ml

        #mobiledevelopment

        Client*

        A European company focusing on developing digital products.

        *We cannot provide any information about the client or specifics of the case study due to non-disclosure agreement (NDA) restrictions.

        Project in numbers

        duration

        12 months

        team

        6 specialists

        efforts

        1920 hours

        Team involved in the project

        industry

        Healthcare

        solution

        Asthma management application

        technologies

        Android, iOS, Python, Dart, Flutter, Django, PostgreSQL

        1 x Project Manager

        2 x Flutter Developers

        1 x DevOps Engineer

        1 x Python Developer

        1 x QA Engineer

        HIRE THE TEAM

        Challenge

        The main goal was to develop a cost-effective care solution harnessing AI and ML algorithms that would assist both medical practitioners and individuals with asthma in treatment goals.

        Related objectives

        Develop the app’s functionality and design

        Integrate AI and ML algorithms

        Implement a set of features (calendar, reminders, statistics)

        Solution & functionality

        Our team developed an innovative asthma management app that harnesses the power of AI and ML algorithms and helps users monitor their symptoms, inhaler usage, environmental triggers, and have a better control over their health.

        Two users mode

        The app offers two user modes: patient mode and administrator mode.

        The end-users of the application are patients who have asthma. They can actively interact with the platform to ease the management of their symptoms. This involves inputting details about their primary and emergency inhalers, setting up daily reminders, and other functionalities.

        Administrators are responsible for keeping the machine learning algorithm up-to-date with relevant data. They also ensure that the inventory of available inhalers within the application is consistently maintained. This guarantees that patients always have access to their prescribed medications.

        Personalization: tracking, reminders, recommendations

        In their personal profiles users can perform multiple functions that help evaluate and monitor the state of their health.

        • Users can fill in the information about the primary and secondary inhalers, customize reminders and notifications to create a personalized schedule.
        • Users get an overview of the day, like possible health risks, symptoms triggers, and the list of inhalations planned.
        • In the personal dashboard, users can access the Calendar section with the weekly and monthly statistics.

        AI/ML-driven analysis and predictions

        Thanks to artificial intelligence and machine learning algorithms implemented in the app, users get accurate analysis and predictions of their health state.

        • Based on various factors like weather, humidity, and others, the app can evaluate the risk of asthma attacks.
        • With submitted audios, the machine learning algorithm analyzes how patients use their inhalers and produces a summary and recommendations for further fine-tuning and usage.

        Results and business value

        Our team launched the MVP in 2 months, developing an innovative, user-friendly mobile solution for asthma management.

        Personalized care

        Symptoms control

        User-friendly interface

        Reduced hospitalization cases

        The application tracks the patient’s condition, analyzes the information about the inhalations the patient takes, and provides personalized guidelines, alerts, and reminders based on the information provided.

        Related cases

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        We appreciate your interest in Timspark

          Healthcare Data Management: Cut 40% of everyday tasks in 4 months

          Healthcare data management software

          Healthcare Data Management: Cut 40% of everyday tasks in 4 months

          We’ve developed a healthcare data management software that makes it a breeze to gather and manage patient data.

          #Healthcare

          #DataManagement

          #BusinessIntelligence

          Client*

          A European company, supplying healthcare data management software with operations in multiple centers throughout the EU.

          *We cannot provide any information about the client or specifics of the case study due to non-disclosure agreement (NDA) restrictions.

          Project in numbers

          duration

          4 months

          team

          17 specialists

          Team involved in the project

          industry

          Healthcare

          solution

          Healthcare Data Management Software

          technologies

          Python, Typescript, Kubernetes, AWS, Power BI, Redis, MongoDB, PostgreSQL

          1 x Team Lead

          1 x Project Manager

          1 x Business Analyst

          3 x Backend Developers

          2 x Frontend Developers

          3 x Data Engineers

          2 x ML Engineers

          2 x BI Developers

          1 x QA Engineer

          1 x AQA Engineer

          HIRE THE TEAM

          Challenge

          The client sought to enhance healthcare provider data management processes, demanding seamless integration, easy patient record access, and strict data protection compliance.

          Related objectives

          Evaluate the current data flow design

          Overhaul the data flow completely

          Automate routine tasks

          Design a secure, high-functionality solution

          Solution & functionality

          We crafted an architecture and data flow for the healthcare provider data management, empowering the client’s staff to gather, analyze, and use patient data for tasks like assessing treatment outcomes and sharing essential information with insurance companies.

          AWS

          Our healthcare data management software relies on Amazon Web Services as it’s secure, flexible, scalable, and cost-effective.

          Client staff input patient data in various formats, like images, videos, and text, which are sent to AWS and stored in a data lake. This data encompasses medical test results, appointment timestamps, and multimedia files from MRIs, CT scans, ultrasounds, and more.

          Extract, transform, load (ETL) pipelines

          We’ve devised and enacted ETL pipelines to automatically consolidate data fragments from client employees into cloud storage.

          Data warehouse & data lake

          All data gathered through ETL pipelines is funneled via Apache Airflow into the data lake for refinement. After refinement, it’s forwarded to the data warehouse, serving various functions, including patient treatment consultation, efficacy evaluation, in-depth data analysis, and furnishing necessary information to insurance institutions.

          Access control

          The healthcare data management software safeguards sensitive data with a smart access control system. This system checks employee statuses from the client’s database, granting access to patient data solely to the specialists working with the patient. Exceptions are made for substitutes during healthcare worker absences.

          When data sharing is necessary, like for medical consultations or insurance requests, employees can request permission, and the healthcare data management software automatically facilitates secure data sharing, preventing accidental or intentional inclusion of extra information.

          Results and business value

          We’ve built a healthcare data management software that empowers workers to efficiently collect, store, and manage patient data, ensuring robust security measures to prevent leaks. Our software engineers have automated mundane processes and optimized healthcare provider data management for maximum efficiency.

          MVP launched in 4 months

          This application keeps over 1.5M active and 8M passive users secure on a daily basis.

          40% of dull tasks automated

          The client highly praised our development team of Android, iOS, and QA engineers for their technical expertise and communication.

          The healthcare data management software allows workers to concentrate on essential tasks instead of dealing with error-prone data flow management. Healthcare provider data management is aligned with government regulations and designed with the latest business intelligence expertise.

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          We appreciate your interest in Timspark

            Let’s build something great together

              Let’s build something great together

                Let’s build something great together

                  Let’s build something great together

                    Let’s build something great together

                      Let’s build something great together