Deep Learning Development Services

Experience the Potential of AI with
Deep Learning

Tensorway helps businesses revolutionize their decision-making and enhance their operations with Deep Learning models able to analyze data with precision and speed never seen before.

Almost every business dreams to boost results, without spending too many resources and finances. Sounds too good to be the truth? Not when Deep Learning is in the game.

Why should businesses care about Deep Learning?

Deep Learning is enabling advanced software capabilities, and adding tremendous value to the business and user experience. As more and more companies take advantage of Deep Learning capabilities, the market is growing rapidly, and is projected to reach $179.9 billion by 2030.

Deep Learning solutions open unimaginable opportunities known before in computer systems. Learning by example, just like a human brain, Deep Learning models understand information that was thought to be understandable by humans only. It opens new horizons for businesses to automate routine operations performed by people. Deep Learning can also identify trends and patterns, and enable powerful predicting algorithms.

Deep learning solutions for enterprise

Most enterprise problems that resist plain rules-based software come down to one thing: patterns too complex to write by hand. That is where deep learning earns its place. Tensorway builds and trains neural networks on your data, then ships them into production. Four types of solution cover most of what we deliver.

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Computer vision

Read images and video for quality inspection, object detection, or document capture.

Natural language processing

Pull meaning from text, classify documents, and power search over your own content.

Predictive models

Forecast demand, risk, or failure from historical and time-series data.

Recommendation engines

Match products, content, or actions to each user from behaviour data.

Use Cases

Deep Learning Applications: Manufacturing, Retail, Cybersecurity, Healthcare

Retail and eCommerce

Using Deep Learning will help you better understand your customers and differentiate your products by offering personalized content and aligning with customer interests.

Example: Dynamic Yield platform is based on Deep Learning models to analyze customer data and provide personalized recommendations to shoppers in real time.

Finance

Deep Learning solutions are used in finance to analyze financial data, such as customer data, stock prices, and trade patterns to identify trends and make predictions about future market movements.

Example: ZestFinance uses Deep Learning to analyze credit risk by processing data from credit reports, bank statements, and social media to assess creditworthiness.

Education and e-learning

Everyone from K-12 to mid-career students can experience DL’s effect on education. Models can summarize materials and generate open questions, quizzes, and identify each student’s strengths and weaknesses to build learning plans tailored to their needs.

Example: Knewton develops adaptive learning technology for higher education. It brings students back on track for college-level study by offering a relevant curriculum.

Customer support and automation

Chatbots, automatic extraction of documents, and auto-forwarding incoming messages to specific departments or persons reduce costs, boost business, and allow focusing on other tasks.

Example: Cogito uses Deep Learning to analyze real-time conversations. The tool provides details about customers’ feelings through volume, pitch, and mimicking.

Healthcare and medical research

Deep Learning enables better diagnosis and treatment plans for patients by analyzing trends, patterns, and behaviors.

Example: Aidoc uses Deep Learning to analyze medical images, helping radiologists detect critical conditions like strokes or hemorrhages faster and more accurately.

Manufacturing

Using AI-powered Deep Learning solutions, you can predict future trends and adjust production based on high or low demand.

Example: Vicarious uses Deep Learning models to enable robots to perform tasks such as picking and placing objects, sorting, and inspecting.

Cybersecurity

Deep Learning helps protect your system, as well as IoT devices, as companies and individuals need to stay one step ahead of hacker attacks.

Example: Deep Instinct developed a platform designed to protect against cyber threats, such as malware, ransomware, and phishing attacks.

Automotive

Deep Learning models can help build self-driving cars with advanced detection capabilities.

Example: Comma.ai develops an open-source driver assistance system that learns from images and sensor data to recognize patterns and adapt to various driving conditions.

How we work

Key stages of Deep Learning development

/01

Understand your business requirements

Our team analyzes your business and identifies your needs to provide a software solution that can solve your business problems.

/02

Data collection and preparation

Collect and prepare the data that will be used to train the model. This involves sourcing and collecting data, as well as cleaning and preprocessing it.

/03

Model design and architecture

This step involves selecting the appropriate type of a model, such as a convolutional neural network or a recurrent neural network, and determining the number and size of the model's layers.

/04

Model training

By training the Deep Learning model on a large and diverse dataset, we can ensure that the model is able to generalize well and perform accurately on a wide range of inputs.

/05

Model evaluation

We evaluate the model's performance on a separate dataset to identify any weaknesses or issues with the model and make necessary improvements.

/06

Model deployment

If the model performs well during evaluation, it can then be deployed in a production environment, where it can be used to make predictions or decisions based on real-world data.

Deep Learning Development Services

Why should businesses care about Deep Learning?