Driving success with Data Science
At QFactorial we help you leverage your company's data by creating end-to-end Machine Learning-powered solutions to address your biggest challenges with advanced algorithms. You're sceptical about the potential of data-based solutions for your business? Get ready to change your mind!
Understand and organize
Defining the business case, desired results and their measurement. Understanding, collecting, and enriching the relevant data
Build and validate
Process the data, develop and validate tailor made machine learning models. Keep the business objective in sight
Integrate and monitor
Integrate ML output with existing tools in your organizations. We are committed to teach and grow your in-house data science capacity
It is often wrongly assumed that only the largest companies may profit from the use of Data Science & Machine Learning models while we've seen many SMEs reap the benefits of incorporating those techniques in their daily operations. We help companies get started with AI and machine learning, using modern tools and integration processes.
- Transition from data swamps to structured data
- Data enrichment and data imputation
- Assess your data potential for ML
- Building proof of concept prototypes
- Creating & implementing fully functional ML-based systems
- Setting up DS environment for future tests & improvements
- Developing the client's inhouse capacity
Together we can assess the readiness of your organization in terms of data and process to start utilizing statistical and ML models
Design & implement tailor-made solutions using modern AI techniques, solving complex tasks based on your data and needs
Internet Of Things
Developing custom or implementing of-the-shelf IoT solutions to gather real-time insights and make automated data-driven decisions
Analyze your data flows, identify key sources, digitize & organize into data lakes, transform to structured data that's available for BI & ML purposes
DataOps & MLOps
Create data pipelines, streamline data consumption, automate the process of running ML experiments and their productionization
Training & Capacity Building
Setting up the base infrastructure while transferring know-how to key individuals within your organization to enhance your DS experience or kick-start your in-house DS department
Our Work Process
Identyifing the business goal and translate it in Data Science language. Defining the scope and the goals of the project with all stakeholders and how their work process could change for the better.
Performing quantitive and qualitive analysis of the available data from existing internal & external sources. Doing a feasibility analysis.
Extracting and transforming the data in suitable format for ML. Data enrichment and imputation, identifying important properties and features.
Modelling & evaluation
Iteratively applying data modelling techniques & evaluating performance metrics, identifying the most promising models.
Deploying the model in production using a modern MLOps process, integrating it with existing systems & business processes. Building internal capacity to use & improve it.
Reaping the benefits!
Continuously monitor and improve the performance of the system.
Meet The Team
Demir TonchevMachine learning & Data science
Veneta Ilieva, CFABusiness analysis & Strategic planning
Lubomir VarbanovSoftware & Data engineering
We'd love to hear from you
Tell us about your project & your organization