NetRom Software offers a comprehensive suite of AI development services tailored to the unique needs of your organization. From design and development to ongoing support—we guide you through the entire AI implementation journey. Our industry-specific, custom solutions for your business challenges are crafted by a team of experienced AI specialists.
With our Natural Language Processing (NLP) services, organizations can enhance customer communication, automate routine interactions, extract valuable insights from text, and improve the accessibility of digital content across various languages and dialects.
Our Generative AI services focus on integrating AI into SaaS solutions and are tailored for various industry-specific applications. This enables organizations to automate repetitive tasks, personalize scalable content, and enhance product innovation. The result? Your organization can operate more efficiently and creatively.
Conversational AI is transforming the way organizations interact with their customers by enabling intelligent and natural conversations. Our Conversational AI solutions combine NLP, Machine Learning, and speech recognition to develop chatbots, virtual assistants, and customer service systems that can simulate human dialogue and facilitate intelligent interactions.
Our Intelligent Automation services help your organization optimize workflows, reduce errors, and free employees from routine tasks, allowing them to focus on more valuable work. From document processing to quality control, and from data validation to reporting – we automate processes intelligently, adapting to changing conditions.
Machine Learning forms the foundation of AI applications by enabling systems to learn from data without explicit programming. By integrating Machine Learning, Deep Learning, and Analytics into IT solutions, organizations can gain actionable insights, perform predictive analysis, and make data-driven decisions. ML development services include designing, training, and deploying both supervised and unsupervised learning models that uncover hidden patterns and trends, from predictive analytics and anomaly detection to recommender systems and clustering algorithms that deliver real business value.
Automate routine tasks, analyze data at lightning speed, and optimize workflows—freeing up time for strategic priorities.
AI scans and analyzes data faster and more accurately than human teams, enabling better decisions based on complete insights.
Deliver unique user experiences with personalized recommendations and targeted communication that truly resonates with your customers.
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Partner with a company that combines technical excellence, a personalized approach, ethical practices, a proven track record, and a commitment to long-term success.
Whether a client needs advanced generative AI, sophisticated computer vision capabilities, nuanced NLP applications, cutting-edge machine learning models, deep learning solutions, or real-time analytics, NetRom has the specialized knowledge to deliver.
NetRom excels in developing bespoke AI solutions that are meticulously tailored to the unique challenges and objectives of each client. Understanding that every business’s needs are distinct, NetRom invests time in deeply understanding those needs and devising strategies that leverage AI to address them directly.
NetRom adheres to the highest standards of data protection and ethical AI practices, ensuring that all AI solutions are developed with transparency, fairness, and respect for privacy at their core.
NetRom’s extensive portfolio of successful AI projects across various industries serves as a testament to its ability to deliver innovative and effective AI solutions.
Understanding that AI development is not a one-time task but a continuous journey, NetRom offers ongoing support and evolution services for all AI solutions.
The journey begins with exploring the compatibility between your organization and NetRom, focusing on aligning our vision, values, and expectations for AI development. This initial exploration phase is crucial for establishing a foundation based on mutual understanding, trust, and clear communication.
Following the initial alignment, the next step involves a technical exchange where your team and NetRom’s experts dive into the specifics of your AI needs and the technical landscape. The goal is get a first impression of our technical expertise and to refine our understanding of your requirements.
With a solid understanding of the technical and business landscape, the partnership progresses to developing a Proof of Collaboration (PoC). This allows you to experience firsthand how we operate, building confidence and buy-in for a long-term partnership. The success of the PoC is a critical milestone, setting the stage for more extensive collaboration.
Upon successful validation of the PoC, the collaboration typically evolves into a hybrid model where NetRom begins augmenting your in-house AI development team with a selection of its expert AI developers and a QA engineer. A dedicated team approach is also a feasible option to be explored.
As the partnership matures and initial projects demonstrate success, the collaboration can be scaled to meet evolving needs. This scaling can take the form of moving towards dedicated teams entirely managed by NetRom or continuing the hybrid model with a balanced mix of in-house and NetRom AI developers.
Below are some examples of AI solutions you can build and integrate through software development:
– Chatbots and Virtual Assistants
With Large Language Models (like GPT-4), you can develop intelligent chatbots that automatically answer customer questions, make reservations, or support internal processes.
– Automated Code Generation and Review
AI tools such as GitHub Copilot, OpenAI Codex, and Code T5 assist developers by automatically writing code, detecting errors, and optimizing code.
– Automated Software Testing
AI-driven testing tools perform regression tests automatically, detect bugs faster, and improve software reliability before deployment.
– Predictive Analytics and Data-Driven Decision Making
Machine learning models analyze large datasets to predict trends such as customer behavior, inventory levels, or financial risks.
– Personalization of User Experience
AI systems tailor content, recommendations, or interface elements based on individual user behavior and preferences (think Netflix or e-commerce recommendations).
– Computer Vision Solutions
Systems for image recognition, defect detection in manufacturing, facial recognition, or automated document processing.
– Business Process Automation
AI can automate repetitive tasks such as invoice processing, email classification, or workflow management, freeing employees to focus on more complex work.
– Cybersecurity and Fraud Detection
AI models monitor network traffic and user behavior to immediately detect suspicious activity and prevent cyber threats.
– Speech Recognition and Natural Language Processing
Applications that convert spoken text to written text, perform sentiment analysis, or provide automatic translations.
AI is becoming increasingly essential in the software development process, offering solutions that boost productivity, reduce errors, and accelerate innovation. Below is an overview of the main AI solutions used in software development:
1. AI Code Generation and Assistants
Tools like GitHub Copilot, ChatGPT, and Microsoft Copilot automatically generate code, offer suggestions, and help debug. They support multiple programming languages and significantly speed up development.
2. Automated Code Review and Quality Monitoring
AI-based systems analyze codebases, detect potential errors, and provide improvement recommendations. This enhances code quality and shortens testing cycles.
3. Automated Software Testing
AI tools can automatically generate test cases, perform regression tests, and detect bugs, increasing software reliability and saving developers time.3.
4. Machine Learning and Data Analytics Platforms
Frameworks such as TensorFlow, H2O, and IBM Watson enable the development, training, and deployment of powerful machine learning models for diverse applications like predictions, image recognition, and natural language processing.
5. Conversational AI and Chatbots
Platforms like IBM Watson Assistant and ChatGPT allow developers to build intelligent chatbots and virtual assistants for customer service, support, and internal automation.
6. Generative AI for Design and Prototyping
Tools like Figma AI and Canva Magic Design automate UI and graphic design, allowing developers to focus on logic and functionality.
7. DevOps Automation
AI is used to optimize CI/CD pipelines, manage infrastructure as code, and perform predictive maintenance, leading to faster and more stable software deployments.
8. Natural Language Processing (NLP)
AI models analyze and understand text and speech, applied in search functionality, sentiment analysis, automatic translation, and document processing.
These AI solutions are widely applicable, making software development faster, more efficient, and more innovative. By integrating AI tools into the development process, organizations can strengthen their competitive edge and improve software product quality.
AI solutions are widely applicable and rapidly transforming the way businesses and consumers work and communicate. Here are some current and commonly used examples of AI solutions:
1. AI Agents
Autonomous systems that independently perform tasks, make decisions, and collaborate with other AI agents. They are used for process automation, customer support, and even complex strategic decision-making in sectors like finance, logistics, and e-commerce.
2. Voice AI
Voice-driven systems that combine speech recognition and text-to-speech. Examples include automated helpdesks, virtual assistants, automatic transcription services, and healthcare applications for patient support via voice.
3. Conversational AI
Chatbots and virtual assistants that understand and use natural language to answer customer questions, provide support, or automate internal processes. These systems are becoming increasingly advanced and can handle complex interactions.
4. Intelligent Automation
AI-powered automation of business processes such as invoice processing, inventory management, workflow optimization, and risk management. These solutions go beyond traditional automation by learning and adapting to changing conditions.
5. Hyperpersonalization
AI analyzes customer behavior and preferences to deliver personalized recommendations, content, or offers, for example in e-commerce and marketing.
6. Real-time Monitoring and Prediction
In healthcare: monitoring vital signs and early warnings of abnormalities. In logistics: real-time route optimization and inventory management.
7.Generative AI
AI that creates new content, such as text, images, or code, based on user input. Think of tools for automatic content creation, design, or even software development.
8. Warehouse Automation and Robotics
AI controls robots for sorting, picking, and packing in warehouses, leading to more efficient logistics processes.
These examples show that AI solutions are becoming increasingly autonomous, versatile, and accessible across a wide range of industries and applications.
The costs for developing AI solutions vary widely and depend on factors such as the complexity of the project, the chosen technology, the size of the development team, required data, hardware, and the desired level of customization.
Key Cost Factors
– Complexity of the AI solution: Simple AI tools are less expensive than custom solutions with advanced functionalities.
– Type of technology: Using open-source versus premium AI platforms affects the price.
– Developer hourly rates: The hourly rate depends on the developer’s knowledge and experience with AI technologies.
– Data requirements: More and more complex data increase costs for processing and storage.
– Implementation costs: Costs depend on the complexity and duration of the implementation process.
– Maintenance and further development: AI systems require regular updates and optimization to remain effective.
Proper preparation, a clear scope, and choosing scalable technologies help keep costs manageable and avoid unexpected expenses.
Real-time data analysis combined with AI is increasingly used across various sectors to make faster and smarter decisions. Here are concrete examples from different domains:
1. Traffic Management and Smart Cities
Cities like Rotterdam use AI-driven traffic systems that analyze real-time traffic data and automatically adjust traffic lights. This reduces congestion and optimizes traffic flow.
2. Fraud Detection in the Financial Sector
Mastercard employs AI systems that analyze millions of transactions every second in real time. Suspicious transactions are immediately flagged, preventing fraud before any damage occurs.
3.Healthcare: Patient Monitoring
In hospitals, AI systems continuously monitor patients’ vital signs. If abnormalities occur, the system immediately alerts medical staff, enabling faster intervention and saving lives.
4 Inventory and Logistics Management
Amazon uses AI and robots to monitor inventory levels in real time and optimize logistics processes. This ensures popular products are restocked on time and shelves stay stocked.
5.Industry: Predictive Maintenance
Siemens applies AI in smart factories. IoT sensors continuously send machine data to an AI system that predicts failures before they happen. This prevents costly downtime and increases efficiency.
6. E-commerce: Dynamic Pricing
Amazon constantly adjusts product prices based on real-time factors such as inventory, competition, and current demand. AI ensures prices remain competitive and profitable.
7.Energy Management in Buildings
AI systems, like those from BrainBox AI, analyze real-time building data to optimize energy use. This leads to lower costs and more sustainable energy consumption.
8. Marketing and Personalization
AI analyzes users’ real-time browsing and clicking behavior to provide personalized product recommendations or instantly adjust ad campaigns for higher conversion rates.
These examples demonstrate that real-time data analysis with AI not only enables faster and better decisions but also drives greater efficiency, cost savings, and improved user experiences across various industries.
Real-time AI analytics can provide immediate insights into business operations, customer behavior, and market trends, enabling rapid decision-making and operational adjustments. This technology supports dynamic pricing, fraud detection, customer experience personalization, and supply chain optimization, among other applications, by analyzing data in real time.
Computer Vision combines image processing with machine learning and deep learning to extract meaning from digital images, videos, and other visual inputs. Its goal is to enable computers to recognize objects, understand scenes, and detect visual patterns.
Key techniques:
– Convolutional Neural Networks (CNNs): The foundation of modern computer vision, specially designed for image recognition;
– Object detection: Identifying and locating specific objects within images;
– Image segmentation: Dividing images into different segments or regions;
– Feature extraction: Identifying important features in images;
– Deep learning architectures: Such as ResNet, YOLO, and transformer-based models.
Practical applications:
– Medical imaging: Detecting tumors in MRI scans or X-rays;
– Autonomous vehicles: Recognizing traffic signs, pedestrians, and other vehicles;
– Facial recognition: Security systems and smartphone unlocking;
– Industrial inspection: Quality control in manufacturing processes;
– Retail: Product recognition in cashier-less stores;
– Agriculture: Crop monitoring via drones;
– Augmented reality: Object recognition for AR applications.
Development process:
Developing computer vision systems follows a structured process starting with collecting and annotating image data that is representative of your specific application. Then, our specialists select the most suitable machine learning architectures and train models until they deliver reliable results for your context.
For technical implementation, we use proven frameworks such as TensorFlow, PyTorch, and OpenCV, optimizing the systems for your specific requirements. This ensures the developed computer vision solutions are not only technically robust but also practical within your existing IT infrastructure.
Challenges:
Variation in lighting, angles, and conditions;
Real-time processing requirements;
Trade-offs between accuracy and speed;
Large amounts of labeled data needed for training.
Computer Vision has become crucial for many AI applications because it allows computers to understand and respond to the rich visual information in our world.
Computer Vision Development is a technology that enables computers to interpret and understand visual information, much like the human eye and brain do. It involves developing algorithms and systems that can analyze digital images, videos, or real-time camera footage to recognize objects, detect patterns, track movements, or extract specific elements from visual data. For your organization, this means that repetitive visual tasks can be automated, reducing human errors and increasing efficiency.
The practical applications of Computer Vision Development are diverse and relevant across various industries. Examples include quality control in manufacturing processes where defects are automatically detected, document processing where handwritten or printed text is digitized and structured, and security systems that identify suspicious activities. In retail, computer vision is also used for inventory analysis, shoplifting detection, and optimizing store layouts based on customer behavior.
Computer Vision applications range from quality control in manufacturing to retail analytics, healthcare diagnostics, and security surveillance. This technology enables machines to interpret and act upon visual data, automating tasks like defect detection, patient monitoring, shopper behavior analysis, and threat identification.
Machine Learning involves algorithms that learn from data to make predictions or decisions. Deep Learning, a subset of Machine Learning, uses neural networks with many layers to analyze complex patterns in large datasets. While Machine Learning is versatile and widely applicable, Deep Learning excels in tasks requiring intricate pattern recognition, such as image and speech recognition.
Generative AI can automate content creation, enhance product design, and streamline decision-making processes. It’s particularly useful for marketing, design, and customer service applications, enabling businesses to create personalized content at scale, innovate in product development, and improve customer engagement.
NetRom provides a comprehensive suite of AI development services, including Generative AI, Computer Vision, Natural Language Processing (NLP), Machine Learning, Deep Learning, and Real-Time AI Analytics. These services encompass everything from creating systems that generate new content to developing sophisticated algorithms for data analysis and insight generation.
NetRom follows a structured approach with our AI development services, starting with a deep understanding of client needs and objectives. We leverage our extensive expertise across various AI domains, adopt best practices in project management and development, and maintain a strong focus on ethical AI and data security. Continuous collaboration and communication with our clients ensure that projects meet their goals and deliver real business value.
