Custom Solutions

Well Defined Success Criteria

1 - Exploration

Initial Call & Exploration of the problem to be solved.

2 - Workshop

Live whiteboard seminars for you and our team of experts.

3 - Plan

The first delivery is a prototype plan with measurable acceptance criteria.

1 - Prototype

Working Prototype is a matter of 1—3 months, depending on complexity.

2 - Full Solution

Results from prototype are refined by expert team & input from you.

3 - Launch

Commercial deployment and ongoing maintenance & updates.

Machine Learning

Our self-sustaining system only learns and improves through pattern recognition and data analysis.

Natural Learning Processing

Our smart application can interpret and analyze spoken language and speech.

Dialogue System

Our language simulation system is designed to promote human-machine dialogue and to understand and respond to human speech.

Deep Learning

Inspired by the complexity of the human brain, we have developed algorithms for independent learning and growth.

Computer Vision

We create artificial systems that can recognise and process visual data to provide enhanced analysis and results.

Speech Processing

We have developed the process of converting spoken phrases into digital commands and converting voice control applications.

Flat classifier into 1,000 classes

Simple and clear methods are often used by machine learning teams. A lot of training data has to be processed. Quite a static form, where adding new items requires retraining. The accuracy of this method decreases greatly as the number of projects increases.

Taxonomy of categories

Categories and subcategories allow the construction of hierarchical classification systems. Each classification step is simpler and more accurate than a flat classifier, but if any successive classification steps are incorrect, the overall recognition is incorrect.

Recognize attributes of the items

For example, length, diameter, head type, material, type of drive using fasteners. This method is very robust and you can easily add new items. Correct selection of the items included in the training set is very sensitive.

Visual search

Extract some hidden features from the image and search for images with similar features in the collection. This may be rough, but in some use cases it may shrink the view.

Solid Training Data

We have processes, teams and tools to obtain and prepare high-quality training data for this task. Our team is composed of dozens of annotation experts, and your knowledge can directly guide the work. We know that more data will usually help, but the correct selection and preparation of training data is more important than the amount of data. During the labeling process, we use AI tools to help/assist our team. By using intelligent AI tools for annotation, we can speed up the entire workflow while obtaining highly accurate models.

How intelligent development works

Our development process enables you to build products that use artificial intelligence to make better use of data


We will analyze your business to find out which AI model can solve current problems, add value and improve performance.

  • Requirements Management Plan

  • Development Process Overview

Data Preparation

We extract, explore, visualize and transform data for use in AI algorithms. Artificial intelligence can analyze information, learn from it and make informed decisions based on past experience.

  • Data Collection

  • Data Exploration & Cleanup

Data Modeling

We specifically create customized AI models for your business projects and needs. The training system then performs analysis to create meaningful predictive models.

  • Picking Learning Tasks

  • Selecting Algorithms

  • Cross-Validation


We seamlessly integrate and maintain AI models into your company's existing systems and processes.

  • Deployment

  • Maintenance