AI system for Collective Intelligence at Team-Composer
Case Study Summary
Client: Team-Composer
Website: team-composer.com
Industry: Software Development, SAAS
Impact Metrics:
- 100% accuracy on 'project context' to 'individual profile' matching.
- 100% accuracy on 'project context' to 'collective intelligence' matching.
- < 1 second response time for team 'browsing'.
- Successfully deployed to Amazon AWS with fully automated delivery pipeline.
Team-composer helps create teams where members strengthen each other. Each team is precisely composed, matching individual strengths and characteristics to create mutual complementarity and robust Collective Intelligence. To make this happen a highly reliable mathing alghorithm must be in place, scalable to many teams and team members.
Challenge
Team-Composer's success depends on accurately predicting a team's collective intelligence for building highly performant teams. They need an AI expert system to match project context rules with a team member's personality traits, combined with the collected team profiles.
My Approach
I developed a smart multi-engine that supports in matching individual strengths and characteristics of team members to create mutual complementarity and robust Collective Intelligence. With a breakdown of the projects' context, with aspects as 'Impact', 'Level of Innovation' and 'Speed', and the match with a team member's personality on factors as 'Thinking Style' and 'Motivation' I can determine the team member's fit with the project.
Results & Impact
- Response time under 2 seconds.
- 100% accuracy on context matching.
- Fully automated delivery pipeline to AWS.
- Successful integration with web interface.
Tech Stack
- Java & Javascript
- Spring Boot
- Postgresql
- Amazon AWS cloud infrastructure
- EC2, Elastic Beanstalk and Load balancing
- Gitlab for CI/CD pipeline
Additional Context
- Timeline: 1 year
- Team Size: 3 people (AI Engineer, Psychologist, Talent Assessment Specialist)
- Role: AI Engineer
- Close collaboration with founding team
- Ongoing knowledge base expansion
- Future plans include implementing feedback mechanism
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