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.
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.
Our language simulation system is designed to promote human-machine dialogue and to understand and respond to human speech.
Inspired by the complexity of the human brain, we have developed algorithms for independent learning and growth.
We create artificial systems that can recognise and process visual data to provide enhanced analysis and results.
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.
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
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 Exploration & Cleanup
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
We seamlessly integrate and maintain AI models into your company's existing systems and processes.