Cogmi
Cogmi is a conversational AI platform that transforms real-time cognitive signals—like focus, memory, and cognitive load—into predictive workforce intelligence. Through short, engaging check-ins and g
Visit Cogmi
What is Cogmi?
Cogmi is a conversational AI platform designed to measure and predict workforce cognitive performance in real time. It uses brief check-ins and interactive games to collect data on focus, memory, and cognitive load, then translates these signals into practical advice for HR teams and managers. The tool helps organisations identify burnout and turnover risks early, improve training effectiveness, and build more resilient teams. By integrating with existing HR and people analytics systems, Cogmi gives leaders visibility into cognitive health alongside traditional performance metrics.
Key Features
Real-time cognitive assessment
Captures focus, memory, and cognitive load data through short interactive check-ins and games
Burnout and turnover prediction
Uses cognitive signals to identify employees at risk before problems escalate
Training optimisation
Measures cognitive readiness and retention to improve the effectiveness of learning programmes
HR dashboard integration
Connects with existing people analytics platforms to centralise cognitive health data alongside other workforce metrics
Conversational interface
Delivers assessments through natural dialogue rather than formal questionnaires
Pros & Cons
Advantages
- Objective measurement: Moves beyond self-reported wellbeing surveys to capture actual cognitive performance data
- Early warning system: Identifies burnout and turnover risk before they become serious problems
- Non-intrusive: Brief check-ins fit into normal working routines without significant time burden
- Integrates with existing systems: Works alongside current HR and analytics tools rather than requiring replacement
Limitations
- Data privacy considerations: Collecting cognitive performance data raises questions about surveillance and employee consent that organisations need to carefully manage
- Requires engagement: Effectiveness depends on employees regularly completing check-ins and participating honestly
- Limited independent validation: The science basis cognitive signal measurement and prediction accuracy would benefit from independent research confirmation
Use Cases
Identifying high-performing employees at risk of burnout before they leave the organisation
Optimising onboarding and training programmes by measuring cognitive load and readiness to learn
Supporting manager decision-making with objective data about team cognitive health alongside productivity metrics
Predicting which employees may struggle with new responsibilities or role transitions
Building a data-driven approach to employee resilience and wellbeing programmes