Neural Networks Development


Transform vast data into actionable strategies, ensuring your business not only keeps pace but sets the benchmark in your industry: neural networks development by Devox will help propel your operations, enhance decision-making, and drive unparalleled growth.

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How Can They Help My Business?

What are Neural Networks and How Can They Help My Business?

As the implementation of artificial intelligence, neural networks are programs imitating the human brain: they employ its biological neural network organization and functioning principles. Basically, a program like this thinks, acts and makes decisions like a human, as it repeats the connections and structure of the brain, facilitating it with appropriate mathematical models.

They consist of layers of interconnected nodes or "neurons," which process and transmit information by dynamically adjusting connections based on input data. This structure allows neural networks to learn from vast amounts of data, identify patterns, and make decisions with minimal human intervention: consequently, neural networks as a service are a self-sufficient subsection of AI-based solutions for businesses.

Services We Provide

Neural Networks Services We Provide

Devox creates NN solutions with different architectural approaches: we can deploy a system to your servers, provide neural network cloud service, or cater to your technological convenience in any different way.

  • NN Solutions for eCommerce and Retail

    Transform your shopping experience by offering personalized recommendations that adapt to user behaviors and preferences. Through our predictive analytics, Devox optimizes inventory management, forecasting demand with unprecedented accuracy. Additionally, we can develop AI-powered chatbots for customer service providing instant, personalized support that boosts satisfaction and loyalty.

  • NN Solutions for Online Cinemas and Streaming

    Personalize content recommendations, keeping viewers engaged by analyzing their viewing habits. We enhance content discovery through advanced media tagging and classification, making navigation intuitive. Additionally, our technology optimizes streaming quality in real-time, ensuring an optimal viewing experience regardless of bandwidth or device constraints.

  • NN Solutions for Finance and Banking

    Leverage accurate fraud detection capabilities, identifying suspicious transactions with remarkable precision. Devox can develop a neural network for the banking sector, where you’ll get personalized banking experiences with tailored financial advice and product recommendations for the users’ side. As for the internal operations, you’ll tackle risk management, predicting market trends and credit risks with enhanced accuracy, thus protecting assets and optimizing financial strategies.

  • NN Solutions for Automotive Industry

    Power autonomous driving technologies, allowing vehicles to make informed decisions in real-time. Devox streamlines manufacturing processes with predictive maintenance, preventing equipment failures. Our solutions enrich the driving experience through predictive navigation and personalized in-car assistance, coupled with intelligent safety systems.

  • NN Solutions for Healthcare

    Revolutionize your patient diagnostics and care, utilizing advanced image analysis for early disease detection with James Webb-level accuracy. Devox creates solutions catering to personalized medicine by predicting individual treatment responses and refining therapy plans. Additionally, you’ll optimize operational burden in healthcare settings, from managing patient flows to optimizing staffing predictions.

  • NN Solutions for Logistics

    Redefine supply chain management, using predictive analytics to anticipate demand shifts and automate warehouse operations. Our engineers ensure efficient route optimization for deliveries, considering real-time traffic and weather conditions, thus cutting delivery times and costs. Furthermore, our approach to predictive maintenance for vehicles and equipment minimizes downtime, elevating the reliability and efficiency of logistics operations.

Development Process

Our Neural Networks Development Process

Among our neural network development tools, there are Python libraries (TensorFlow, PyTorch, Keras, Theano), IDEs and editors, specialized hardware like GPUs and TPUs and development platforms such as Google Colab, Microsoft Azure Machine Learning and Amazon SageMaker. Armed with these instruments, we then follow a thorough SDLC:


01. Problem Definition

The first step involves clearly defining the problem you want the neural network to solve. This could be a classification problem, a regression task, prediction, or any other task that can be addressed with a neural network. Our team collects all of these requirements, shapes the team and milestones, and defines the tech stack to be used.


02. Data Collection and Preprocessing

Once the problem is defined, the next step is to gather and prepare the data needed for training the neural network. Our engineers start collecting a sufficiently large and representative dataset that reflects the complexities of the problem, cleaning and formatting it aftwerwards. This is when we’re dealing with missing values, normalizing or scaling data, encoding categorical variables, and splitting the data into training, validation, and test sets.


03. Model Design and Training

We’re choosing the type of neural network (e.g., feedforward, convolutional, recurrent) and designing its architecture (selecting the number of layers and neurons in each layer, activation functions, and any regularization methods to prevent overfitting). The neural network is then trained on the preprocessed data: our engineers feed the data through the network, using a forward pass, calculating the error with a loss function, and then adjusting the weights of the network through backpropagation. Training continues iteratively until the model achieves a satisfactory level of performance on the training dataset.


04. Evaluation and Validation

The model's performance is evaluated using the validation set (and later, the test set) to ensure that it generalizes well to new, unseen data. Metrics such as accuracy, precision, recall, and F1-score for classification tasks or mean squared error for regression tasks are commonly used.


05. Hyperparameter Tuning

Based on the performance of the validation set, the model's hyperparameters (e.g., learning rate, number of epochs, batch size) may be adjusted to improve performance. This step might involve techniques like grid search or random search.


06. Model Testing

Once the model is trained and validated, it is tested with the test set to assess its final performance. This step is crucial for understanding how well the model is expected to perform in a real-world scenario.


07. Deployment

After testing, the model is deployed in a production environment where it can start making predictions or classifications on new data. Deployment might involve integrating the model into existing systems or applications.


08. Monitoring and Maintenance

After deployment, the model's performance is continuously monitored to ensure it remains effective over time. As new data becomes available, the model may need to be retrained or fine-tuned to maintain its accuracy and relevance. Incorporating feedback from the model's performance in the real world can provide insights that lead back to the problem definition or data collection steps, creating a cycle of continuous improvement.

  • 01. Problem Definition

  • 02. Data Collection and Preprocessing

  • 03. Model Design and Training

  • 04. Evaluation and Validation

  • 05. Hyperparameter Tuning

  • 06. Model Testing

  • 07. Deployment

  • 08. Monitoring and Maintenance

Key Features

Key Features of Custom Neural Networks System


Layers of Neurons

A neural network consists of an input layer, one or more hidden layers, and an output layer. Each layer contains units or neurons that process incoming data, transform it, and pass it on to the next layer. The complexity and depth of the network can vary depending on the task it is designed to perform.


Connections and Weights

Neurons in one layer are connected to neurons in the next layer. Each connection has an associated weight that is adjusted during the learning process. These weights determine the strength and direction of the influence one neuron has on another, essentially encoding the knowledge of the network.


Activation Functions

Activation functions are mathematical equations that determine whether a neuron should be activated or not, based on the weighted sum of its inputs. They introduce non-linear properties to the network, enabling it to learn complex patterns and perform tasks beyond mere linear data processing.


Learning Algorithm

Neural network service learns from data through a process that involves adjusting the weights of connections to minimize the difference between the actual output and the desired output. The most common learning algorithm is backpropagation combined with an optimization technique such as gradient descent. This process iteratively reduces prediction errors, improving the model's accuracy over time.


Loss Function

The loss function measures the network's performance by calculating the difference between the predicted output and the actual output. During training, the goal is to minimize this loss, which guides the adjustment of weights in the network.


Bias Neurons

Bias neurons are added to layers to help the network better fit the data. They allow the activation function to be shifted, which can be crucial for learning patterns in data where the best fit does not go through the origin.


Regularization Techniques

To prevent overfitting, which occurs when the model learns the noise in the training data instead of the actual signal, neural networks may employ regularization techniques. These techniques, such as dropout, limit the complexity of the model, making it more generalized and robust to unseen data.


Feedforward and Feedback Mechanisms

In feedforward neural networks, data moves in only one direction, from input to output. In contrast, recurrent neural networks (RNNs) have feedback mechanisms, allowing them to process sequences of data and maintain a form of memory.



Neural networks can adapt to changing input, making them suitable for dynamic environments. As they are exposed to new data, they can adjust and improve their performance, making them highly flexible and scalable for various applications.


Parallel Processing

Neural networks inherently support parallel processing, which means they can handle and process multiple inputs at the same time. This feature is particularly advantageous for handling large datasets and complex computations.


Benefits of Implementing Neural Networks

Neural networks can help resolve problems and provide solutions related to many business objectives, pains, and tasks. As a subsection of artificial intelligence, you can expect to reap the following from using NN:

  • Tackle Data Overload

    Salvage a solution that will work ideally in a data-rich environment in the era of big data, making sense of the vast amounts of information collected. Neural networks thrive on big data: they can analyze and interpret large datasets quickly and efficiently, identifying meaningful patterns and insights that humans might miss.

  • Predict What You Have to Know

    If you face uncertainty regarding future trends, customer behavior, and market dynamics, artificial neural network development can help. You’ll leverage historical data to make accurate predictions, anticipating future demands more clearly, optimizing inventory, and tailoring marketing strategies.

  • Reach Maximum Automation

    Automating repetitive tasks is a common pain point for businesses looking to reduce labor costs and errors. Neural network services can automate a range of tasks, such as customer service inquiries through chatbots, document classification, and even complex decision-making processes, freeing up human workers for more strategic activities.

  • Boost Quality Control

    Reach premium quality in your production even at scale: neural network solutions provide diligent quality control processes by detecting defects or anomalies in real-time, ensuring that only products meeting the highest standards reach the market.

  • Strengthen Cybersecurity and Fraud Detection

    Neural networks can identify complex patterns and anomalies in transaction data indicating fraudulent activity, preventing losses and protecting your clients’ sensitive information. You’ll detect and respond to security threats more quickly and accurately than traditional methods, protecting sensitive data and infrastructure from cyberattacks.

  • Provide Individual Experiences and Win Customer Loyalty

    Get the analytics from individual customer data and summarize it, painting a clearer picture of what will make each of your clients happier. You won’t just deliver personalized recommendations, content, and services, but also expand the abilities of your product if geared up with AI, experiencing its power not just within your internal team but also sharing its potential with the customers.

Case Studies

Our Latest Works

View All Case Studies
Social Media Screening Platform Social Media Screening Platform
  • Backend
  • Frontend
  • Cloud Services
  • DevOps & Infrastructure

Social Media Screening Platform

The project is a web-based AI-powered platform for comprehensive social media background screening. Its supertask is to streamline potential employee background checks for companies, tackling employment risk management.

Additional Info

Core Tech:
  • .NET Core
  • Angular
  • Azure
  • Docker
  • GitLab CI/CD
  • Selenium Web Driver


Nabed Nabed

Bridging MedTech and MarTech for Enhanced Patient Engagement

Nabed is a SaaS platform at the crossroads of MedTech and MarTech. It enables caregivers to engage with patients using comprehensive, personalized educational content for better healthcare outcomes.

Additional Info


Lebanon Lebanon

ActivePlace ActivePlace
  • health
  • fitness
  • marketplace

Health - and Community-Oriented Social Platform

ActivePlace is a wellness social network and media platform with a marketplace feature, dedicated to health, fitness, and an active lifestyle. It connects people with like-minded individuals, mentors, and businesses.

Additional Info

Core Tech:
  • Jenkins
  • Angular
  • Ruby
  • Figma


and over 200 our featured partners and clients

Awards & Certifications

Industry Contribution Awards & Certifications

Check Devox Software Awards on rating & review platforms among top software development companies and Certifications our team members holds.

  • Awards
  • Certifications
  • UpWork


  • Clutch


  • The Manifest

    The Manifest

  • DesignRush


  • MC.today


  • Clutch


  • Clutch


  • AppFutura


  • Clutch


  • GoodFirms


  • DesignRush


  • UpWork


  • Professional Scrum Master™ II (PSM II)

    Professional Scrum Master™ II (PSM II)

  • Professional Scrum Product Owner™ I (PSPO I)

    Professional Scrum Product Owner™ I (PSPO I)

  • ITIL v.3 Foundation Certificate in IT Service Management

    ITIL v.3 Foundation Certificate in IT Service Management

  • ITSMS Auditor/Lead Auditor of ISO Standard 20000

    ITSMS Auditor/Lead Auditor of ISO Standard 20000

  • Microsoft Certified: DevOps Engineer Expert

    Microsoft Certified: DevOps Engineer Expert

  • Microsoft Certified: Azure Administrator Associate

    Microsoft Certified: Azure Administrator Associate

  • Quality Assurance ISTQB Foundation Level

    Quality Assurance ISTQB Foundation Level

  • Microsoft Certified Solution Develop (MCSD)

    Microsoft Certified Solution Develop (MCSD)

  • Java Development Certified Professional

    Java Development Certified Professional

  • JavaScript Developer Certificate – W3Schools

    JavaScript Developer Certificate – W3Schools

  • Certified Artificial Intelligence Scientist (CAIS)

    Certified Artificial Intelligence Scientist (CAIS)

  • Oracle Database SQL Certified Associate

    Oracle Database SQL Certified Associate




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Carl-Fredrik Linné
Tech Lead at CURE Media
Darrin Lipscomb
United States

We are a software startup and using Devox allowed us to get an MVP to market faster and less cost than trying to build and fund an R&D team initially. Communication was excellent with Devox. This is a top notch firm.

Darrin Lipscomb
CEO, Founder at Ferretly
Daniel Bertuccio

Their level of understanding, detail, and work ethic was great. We had 2 designers, 2 developers, PM and QA specialist. I am extremely satisfied with the end deliverables. Devox Software was always on time during the process.

Daniel Bertuccio
Marketing Manager at Eurolinx

We get great satisfaction working with them. They help us produce a product we’re happy with as co-founders. The feedback we got from customers was really great, too. Customers get what we do and we feel like we’re really reaching our target market.

Trent Allan
CTO, Co-founder at Active Place
United Kingdom

I’m blown up with the level of professionalism that’s been shown, as well as the welcoming nature and the social aspects. Devox Software is really on the ball technically.

Andy Morrey
Managing Director at Magma Trading
Vadim Ivanenko

Great job! We met the deadlines and brought happiness to our customers. Communication was perfect. Quick response. No problems with anything during the project. Their experienced team and perfect communication offer the best mix of quality and rates.

Vadim Ivanenko
United States

The project continues to be a success. As an early-stage company, we're continuously iterating to find product success. Devox has been quick and effective at iterating alongside us. I'm happy with the team, their responsiveness, and their output.

Jason Leffakis
Founder, CEO at Function4

We hired the Devox team for a complicated (unusual interaction) UX/UI assignment. The team managed the project well both for initial time estimates and also weekly follow-ups throughout delivery. Overall, efficient work with a nice professional team.

John Boman
Product Manager at Lexplore
Tomas Pataky

Their intuition about the product and their willingness to try new approaches and show them to our team as alternatives to our set course were impressive. The Devox team makes it incredibly easy to work with, and their ability to manage our team and set expectations was outstanding.

Tamas Pataky
Head of Product at Stromcore
Stan Sadokov

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Product Lead at Multilogin
United Kingdom

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Technical Director at M3 Network Limited


  • What is a neural network?

    A neural network is a computational system inspired by the structure, processing method, and learning ability of the human brain. It consists of layers of nodes, or “neurons,” each designed to perform specific computations. These networks can learn from data, making them highly effective for tasks such as pattern recognition, data classification, and predictive analytics. Neural networks adapt their structure during the learning process by adjusting the connections between nodes based on the input they receive, which allows them to improve their performance over time.

  • How do neural networks learn?

    Neural networks learn through a process called training, where they are fed large amounts of data and the desired output. They use algorithms to adjust the weights of connections between neurons to minimize the difference between their prediction and the actual outcome. This process is often facilitated by backpropagation and optimization algorithms like gradient descent, which help the network iteratively reduce errors in its predictions. Over time, the network adjusts its weights to patterns in the data, effectively learning from it.

  • What are the differences between supervised, unsupervised, and reinforcement learning in neural networks?

    In supervised learning, the neural network is trained on a labeled dataset, which means each input comes with the correct output. The goal is to learn a mapping from inputs to outputs, making it suitable for tasks like classification and regression. Unsupervised learning involves training the network on data without explicit labels, aiming to find underlying patterns or distributions in the data, useful for clustering and dimensionality reduction. Reinforcement learning is a type of learning where an agent learns to make decisions by performing actions in an environment to achieve some goals; the network learns from trial and error, guided by rewards or penalties.

  • Can neural networks make decisions on their own?

    Neural networks can make decisions based on the patterns and relationships they learn from data. While they don’t “decide” in the human sense, they can autonomously generate outputs, classify data, or predict outcomes based on their training.

    Such a capability enables applications like autonomous vehicles, which can make real-time navigation decisions, or financial systems that decide on stock trades. However, the quality of these decisions heavily depends on the training data and the network’s design.

  • What are some common challenges in neural network development?

    Common challenges in neural network web development include overfitting, where the network learns the training data too well, including its noise, making it perform poorly on new data. Underfitting is another challenge, where the network doesn’t learn the underlying patterns well enough. The complexity of designing the network architecture, choosing the right hyperparameters, and ensuring sufficient and quality training data are also significant challenges. Additionally, computational resources and processing time for training large models can be substantial.

  • How can neural networks be applied in small businesses?

    Small businesses can leverage neural networks in various ways, such as customer segmentation, predicting sales trends, optimizing inventory levels, and personalizing marketing efforts.

    Neural networks can also enhance customer service through chatbots or recommendation systems, improving customer engagement and satisfaction. By adopting cloud-based AI services, small businesses can access neural network capabilities without significant investment in hardware and expertise, making AI more accessible and applicable to their operations.

  • What ethical considerations should be taken into account when deploying neural networks?

    When deploying neural networks, it’s crucial to consider issues of bias, privacy, and accountability. Ensuring that the training data is representative and free from biases is essential to prevent discriminatory outcomes. Privacy concerns arise from using sensitive or personal data for training neural networks, necessitating robust data protection measures. Finally, accountability in decision-making processes involving neural networks is vital, especially in critical applications like healthcare or law enforcement, where decisions can significantly impact individuals’ lives.

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