Most large organisations
Time to Value
POC in 8-12 weeks
Large Language Models
Kubernetes & Kubeflow
The hiring process is a time-consuming process that often cannot guarantee effective results. This means that the ability of an organisation to provide the right skills for the job at hand is based on a series of often manually conducted steps driven by the organisation’s Human Resources (HR) department. The steps become inefficient as the volume and nature of the jobs change over time.
Businesses are looking for ways to improve the speed and effectiveness of identifying, screening and pre-processing candidates. The goal is to provide the business with a shortlist of high quality candidates that have already been matched to the job and skillset requirements, organisational culture, candidate career growth alignment and salary expectations.
The use case we explore specifically focuses on impacting the following hiring steps:
Large organisations often receive a large number of job applications, making it difficult to screen and select the best candidates efficiently.
The hiring process can be lengthy and complex leading to delays in filling open positions, potentially with candidates that are not a good fit for the position.
Ensuring that the hiring process is consistent and fair for all candidates can be a challenge. With multiple people involved in the process, there is a risk of bias or inconsistencies in the evaluation of candidates.
The solution will incorporate a feedback loop from existing internal and available external data to generate baseline job profiles for the organisation. These profiles are based on existing positions that have been through the recruitment value chain.
The data is fed as inputs into a machine learning model including the use of a ChatGPT-like Large Language Model (LLM) to generate the profiles based on pre-trained and additional training on the model. The output is a job profile structured with the core requirements for the position.
What makes this solution unique is how as data collection improves, the entire value chain benefits.
The actual recruitment screening process will utilise the LLM to improve screening questions asked each candidate, with additional probing follow-up questions in order to surface more relevant insights for the screening and matching process.
Machine learning classification models can be developed to predict the fit of the candidate to the organisation and position advertised. This will take in more advanced features from the AI-driven screening process and train against the success placement of candidates within the organisation.
At Melio we apply our proven methodology FLUID4ML to ensure that the solutions we build are focused on solving business problems that are iteratively developed to release value early. We build with durability and long-term thinking in mind to ensure that our solutions are sustainable and extendable.
Improved candidate selection and screening turnaround times
Improved candidate quality and fit-for-role
Reduction in hiring costs
Consistency in screening and validation process
Improved job profile definitions
Contact us for more information on how we can assist you.