7+ years
average relevant functional and industry experience
holding senior, lead, VP, Director,
and C-level positions
and C-level positions
4,000 senior-level+ members
Projects
1,500+ AI experts
including AI engineers, AI Product Managers, Data Scientists and etc.
Project Execution by Big Tech and Scaleup Teams
Custom-assembled teams of vetted senior-level+ experts led by executives with industry expertise to carry out |projects
Implement projects and build products with the industry knowledge you need
Access to teams that combine hands-on experience in Scaleups and execution at the Big Tech level across highly competitive industries
Executives
1,500 experts hold head, staff, principal, director, VP, C-suite positions
Software Engineers
3,200 experts
Product Managers
1,900 experts
ML Engineers
1,300 experts
Marketing Gurus
1,200 experts
Data Analytics
950 experts
DevOps
Engineer
200 experts
AI
25% of experts
E-commerce &
Retail
30% of experts
Mobility
8% of experts
Edtech &
Education
7% of experts
Fintech & Banking
12% of experts
Project Categories
GenAI implementation
- Enhance GenAI adoption with our solution, featuring: (i) easy-to-deploy infrastructure for experimentation, (ii) expert teams experienced in AI, (iii) an interface for accessing foundational LLMs, and (iv) employee training to maximize the value of AI tools.
Product Development & Launch
- Build and deliver market-ready products with our tailored teams, supporting every stage from ideation to launch, ensuring your team can implement new features post-launch while maintaining flexibility for future pivots.
Backend Performance Optimization
- Conduct a technology audit to identify bottlenecks in your architecture, infrastructure, and development methodology. Propose a prioritized backlog of improvements and implement them.
DevOps
- Conduct thorough DevOps maturity and DevSecOps assessments and implement scalable solutions. Our teams also facilitate the required migrations and integrations and adopt containerisation and Kubernetes, streamlining deployment, enhancing scalability, and improving system reliability.
Data Engineering and Analytics from scratch
- Build a comprehensive data analytics framework from the ground up by
- Implementing cloud-based data architectures for efficient data management.
- Leveraging data science for advanced models and automated A/B testing.
- Automating real-time data workflows for immediate insights.
- Creating intuitive dashboards for self-service reporting.
- Ensuring data security and consistency through governance and quality control.
ML Lifecycle
- End-to-end solution, featuring
- ML Infrastructure: model selection, data ingestion, and deployment,
- MLOps: repeatable and efficient workflow for model development and monitoring
- Data Labeling & Annotation: customized human-data labelling processes leveraging ready-made solutions from data labelling providers
- Industry and cross-industry ML solutions: address business problem with relevant ML technology (NPL, Computer vision and etc.)
GenAI implementation
- Enhance GenAI adoption with our solution, featuring: (i) easy-to-deploy infrastructure for experimentation, (ii) expert teams experienced in AI, (iii) an interface for accessing foundational LLMs, and (iv) employee training to maximize the value of AI tools.
Product Development & Launch
- Build and deliver market-ready products with our tailored teams, supporting every stage from ideation to launch, ensuring your team can implement new features post-launch while maintaining flexibility for future pivots.
Backend Performance Optimization
- Conduct a technology audit to identify bottlenecks in your architecture, infrastructure, and development methodology. Propose a prioritized backlog of improvements and implement them.
DevOps
- Conduct thorough DevOps maturity and DevSecOps assessments and implement scalable solutions. Our teams also facilitate the required migrations and integrations and adopt containerisation and Kubernetes, streamlining deployment, enhancing scalability, and improving system reliability.
Data Engineering and Analytics from scratch
Build a comprehensive data analytics framework from the ground up by
- Implementing cloud-based data architectures for efficient data management.
- Leveraging data science for advanced models and automated A/B testing.
- Automating real-time data workflows for immediate insights.
- Creating intuitive dashboards for self-service reporting.
- Ensuring data security and consistency through governance and quality control.
ML Lifecycle
End-to-end solution, featuring
- ML Infrastructure: model selection, data ingestion, and deployment,
- MLOps: repeatable and efficient workflow for model development and monitoring
- Data Labeling & Annotation: customized human-data labelling processes leveraging ready-made solutions from data labelling providers
- Industry and cross-industry ML solutions: address business problem with relevant ML technology (NPL, Computer vision and etc.)
Illustrative team for product development in fintech
Task-tailored teams of senior-level experts led by executives with industry expertise and relevant experience
10+ years of experience
CPO
Lead QA Engineer
10+ years of experience
Software Engineer
7+ years of experience
Lead Backend Developer
8+ years of experience
Senior Frontend Developer
7+ years of experience
Senior Product Designer
6+ years of experience
How it works
2
Review the team proposal and add or remove a team member
3
Kick-off and monitor task execution
4
Seat back and enjoy the result
2
Review the team proposal and add or remove a team member
3
Kick-off and monitor task execution
4
Seat back and enjoy the result
1
Sign up and describe the task
1
Sign up and describe the task