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Artificial Intelligence & Machine Learning Development Services

We use machine learning methods to create smart solutions that solve business problems.
What we do
What’s included

What we do

Integra Sources provides AI and machine learning software development services for companies and individuals looking to leverage machine learning techniques and algorithms for predictions, anomaly detections, automation, and other applications. We specialize in AI technologies and computer vision systems for IoT projects and embedded systems.


What's included?

We work on a wide variety of problems including big data processing, image recognition, video analysis, object detection, and tracking, face recognition, and emotion recognition to name a few. We build custom machine learning and AI-based software solutions for healthcare, manufacturing, and other industries. To deliver intelligent systems we design algorithms that can learn from data and make predictions.

Data analytics development

Our data science team will help you make the most of the data you have. We perform data mining, data classification, and analysis, and create predictive models using machine learning algorithms.

Optical Character Recognition (OCR)

We develop OCR engines for extracting data from documents, photos, images, and videos so we can train and optimize machine learning algorithms to become smarter over time.

AI-based chatbot development

AI-based conversational interfaces or chatbots understand context, empathize with users, and keep the language natural. We build both voice chatbots and messenger bots using machine learning tools and AVS (Alexa Voice Service).


Check out our recent case study

Machine learning Computer vision Mobile app development OCR

Optical Character Recognition Module for Invoice Capture Software

The OCR module makes it possible to extract information including the invoice date, number, the total sum of the purchase, and line-items.

Our clients include enterprise companies, research centers, and innovative startups from all over the world

How we work

Check out the table below to learn more about how we work and where your involvement is most needed depending on the type of collaboration model:

AREA OF RESPONSIBILITY

PROJECT-BASED OUTSOURCING

DEDICATED DEVELOPMENT TEAM

RESEARCH & DEVELOPMENT

PROJECT REQUIREMENTS

YOU

SHARED

YOU

UI DESIGN

SHARED

SHARED

SHARED

ENGINEERING

INTEGRA

INTEGRA

INTEGRA

DATA ANALYTICS

INTEGRA

SHARED

SHARED

DEVELOPMENT

INTEGRA

INTEGRA

INTEGRA

TESTING

INTEGRA

SHARED

SHARED

DELIVERY MANAGEMENT

SHARED

SHARED

Frequently Asked Questions

Read this information to better understand the process of AI and machine learning development.

What's the difference between artificial intelligence, deep learning, and machine learning?

In a nutshell, artificial intelligence is a way to make machines think like humans, and machine learning is an approach to achieve artificial intelligence. Machine learning development is about writing algorithms that can make sense of data – classify data, find patterns and correlations, and learn from data to make predictions – all without human intervention. Deep learning in its turn is one of the most widely-used techniques for implementing machine learning. It uses neural network architecture that consists of multiple layers to learn features from the data. To build a successful deep learning application we need a large amount of data to train the model and a powerful data processing system.

What technologies do you use to build machine learning systems?

For projects with advanced image processing needs, we use OpenCV library. We also use Amazon and Azure's Machine Learning services to build predictive models and automate data processing tasks. Other machine learning technologies in our tech stack include TensorFlow, Keras, Caffe/Caffe2, and OpenALPR.

What tasks can you solve with ML?

Machine learning tasks we're best at include:

  • Training neural networks to build classifications
  • Object recognition
  • OCR
  • Face recognition
  • Human detection
  • Predictive analytics
  • Natural Language Processing (NLP) for text and voice applications
  • Recommender systems

What does the machine learning development process look like?

First, we need to look at your problem and see if machine learning is the best method to solve it. If yes, then the next step is to collect meaningful data for training purposes. We will classify and analyze these data, and when our dataset is ready, we can start a learning model development. One of the most important stages in this process is training the model on the existing dataset. This usually takes a few design experiments and iterations. Once we get the proof of concept we'll integrate our model with your solution and launch it to production.

What if I don't have enough data?

Not enough data will lead to unsatisfying results. The more data the more accurate the algorithm. If you don't have the amount of data we need to build a training model, we will try to collect them from online sources if possible. There are a lot of open source resources that contain databases with categorized data that we can use to train our system. Also, there are some paid resources where we can create a database with the needed information for training purposes.