It's great when you have a lot of applicants, but more CVs means more time reading.
PLATO can analyse CVs. Fast. In fact, by the time you've finished reading this sentence PLATO could have already scanned and assessed over 1,000 CVs.
PLATO uses advanced Natural Language Processing to read and understand CVs at a rate far higher than even a large team of people.
You can use PLATO to find the top candidates for any role in a fraction of the time your current process takes.
Bias Analysis and Mitigation
Every organisation has a hiring pattern caused by unconscious bias.
These patterns maintain organisational culture, but sometimes the right candidates get overlooked.
PLATO mitigates bias while keeping your organisation's personality. It examines your hiring patterns and identifies key biases within the process and removes them.
Losing a team member is always unfortunate, but it also costs money.
PLATO uses several metrics to predict when an employee is likely to want to move on, and prompts you to take action to keep the team together.
Every employee is different, and PLATO treats every one of them as an individual, however patterns from one employee may allow prediction of other employees' behaviour.
PLATO follows your employees through their careers.
Each employee is added to the model where their performance and actions are compared to others in your organisation. Knowing how employees have behaved before can provide useful insights into how other similar employees will behave in future.
PLATO’s insights will allow you to easily identify key areas for development and training, as well as identifying potential future leaders from your talent.
Thank you for taking the time to read about PLATO.
We'd be happy to take any questions or schedule a demo whenever is convenient for you.
Unstructured CV Data
PLATO connects candidates based on their similarities and features.
PLATO recognises groups of candidates, for example all those with accounting experience.
Identified Key Candidates
PLATO chooses the clusters relevant to your job role and highlights the most suitable candidates.
Move the slider to see how much time PLATO could save you
PLATO profiles the types of bias present in your existing hiring methods and uses it to mitigate any biases in the machine learning model.
The chance of an employee leaving varies over time and is affected by many factors. PLATO uses past data to predict when employees will be thinking about leaving and suggests how they could be motivated to stay.
PLATO assesses employees based on their potential future performance, not their current performance.
Actual average score: 4.75
Target score: 4
Employee is performing above average and current incentives are working well
This year: 2.75
Ideal maximum: 8
PLATO indicates that this employee is likely to cross the threshold soon
Sales this year: £1,548,400
Employee is performing below their sales targets but there is at least a 50% chance they will exceed it before the end of the year