How EdGE Networks is solving talent hiring and development challenges with AISindhu MV
Imagine a suite of products that can not only auto-source, auto-screen and auto-engage the best candidates and employees for a role, but also recommend the best career path and draw up a personalised learning path for each employee. Add to that recommendations to leaders as to where they need to reskill or invest, based on business and employee forecasts, and what you have is a solution entirely powered by Artificial Intelligence (AI).
This suite of products comes from HR tech startup, EdGE Networks, which started out in 2012 with an intelligent matching engine that helped customers quickly identify the best suited candidates or employees for a role.
Over six years, the team at EdGE Networks has transformed that intelligent engine from a single product to a suite of productivity tools that leverage AI to help businesses address challenges in the areas of talent acquisition, workforce optimisation, talent transformation, and workforce planning. The company has three enterprise solutions –Talent Analytics, Workforce Optimisation and HIREalchemy – that cover a gamut of HR requirements.
“When we started up, intelligence was the product, whereas today we have a suite of products powered by intelligence,” says Mridul Kumar, Head, Product Management at EdGE Networks.
The next-gen HR solution
EdGE Networks’ Talent Analytics solution helps businesses make the shift from transactional HR reporting to transformational people predictions – from co-relating reasons for attrition to employee engagement, from predicting the likelihood of a job requirement getting fulfilled internally, or the joining probability of candidates to demand and supply forecasting and resource optimisation.
The startup’s Workforce Optimisation solution forms an intelligent layer atop HR systems and empowers businesses in future-focused organisation building. The solution analyses structured and unstructured data to match the right employees to the right jobs, and helps identify the skill gaps and chalk out Intelligent Learning Paths (iLP) that enables employees to pursue their preferred career path via upskilling and reskilling.
HIREalchemy is their flagship intelligent Talent Supply Chain platform powered by Natural Language Processing (NLP), knowledge & skill graphs, and advanced ML and Deep Learning algorithms. The platform auto-sources employees or external candidates for a job, and stack ranks the best fits. This exponentially enhances the speed and accuracy of job fulfilment and has the potential to save millions of dollars for large businesses. After hiring or allocation, the same platform helps employees and leaders determine the skill gaps for current or future roles, and follow personalised learning paths.
“Today, we have consistently grown to become an intelligent talent supply chain platform,” says the Product Management Head.
Their innovative product suite has earned them numerous industry recognitions such as “The Best of Show” award at the Emerging Companies Summit (ECS), hosted by NVIDIA in 2016, and the NASSCOMEMERGE 50 Awards in 2016, among others.
Getting the product strategy right
Mridul says EdGE Networks’ product development journey focused on answering three key questions
- How to build an enterprise-grade product using a startup team?
- How to focus on the right problems to solve?
- How to balance short-term results and long-term priorities?
A critical area where startups often find themselves at the crossroads is in identifying the right problem to solve. And, the situation was no different for EdGE Networks in its early days. While the founder and the core team were clear that they would focus on HR tech, they found it challenging to decide which of the use-cases within HR tech would be most viable and suitable for EdGE Networks’ core search and match proprietary technology. “To find an answer, we worked very closely with our customers, to identify the use cases where we could deliver the maximum and immediate ROI.”
Talking about the how they addressed the “universal problem” that any product team can relate to – achieving the right balance between managing innumerable ‘immediate’ customer requirements and long-term scalability and manageability of a product, Mridul says, “We specifically focussed on the core Application and Data Science architecture of our platform. This allowed us to maintain the necessary agility that our customers need, while also ensuring the stability and future scalability.”
Explaining the rationale behind focusing on Application and Data Science architecture, the Product Head says, “For an enterprise-level application, a strong architecture ensures that we are able to enable new use-cases, new workflows, and new business rules relatively quickly. We are able to achieve this today because, right in the beginning, we prioritised long-term architecture over short-term instant gratification. In other words, when it came to implementing customer requests, we would opt to work on a robust holistic solution which could take three weeks to design and implement, instead of sticking to the client’s deadline of three days and working on a short-term solution, which most often ends in continuous back-and-forth loop and an unhappy client.”
Powered by Intelligence and Data
EdGE Networks’ unique value proposition is that they use natural language processing (NLP) along with advanced Machine Learning and Deep Learning algorithms to analyse jobs, careers, employees, candidates, trainings, certifications, and more. “Analytics are at the core of all our solutions. Our engine has the ability to interpret unstructured and structured data, and then apply advanced Data Science models on it.”
Talking about why EdGE Networks decided to integrate AI, which is still not widely used in the HR and hiring sector as much as in sectors like electronics or manufacturing and home automation, Mridul shares, “The best applications of AI are in places where a rule-based engine doesn’t work or gets too complicated to work effectively. HR as a sector is very contextual where every company is different, every job is different, and every employee/candidate is different. Such a diverse environment is perfect for AI to add value and deliver measurable impact. Using NLP, Machine Learning and Deep Learning technologies, we realised that we could help our customers accurately answer the key talent supply chain questions. For instance – who are the people who have the required skills and experience for an open job position? Which career paths are suitable for employees based on their skill sets? What are some of the skills which are seeing an increase in demand at an enterprise level?”
The result is that by helping businesses, especially large enterprises, answer these questions, EdGE Networks can help them increase the conversion rate of joinees, bring down the time taken to shortlist candidates, provide ready-to-deploy talent to businesses, recruit the best talent, keep a tab on supply-demand of talent and make on-time hiring a reality. And, by identifying skills gaps, upskilling or reskilling requirements, understanding career goals and aspirations of the talent and enabling organisations to meet those expectations, EdGE Networks helps businesses achieve better retention and a happy workforce. “All this eventually impacts both revenue and profits of companies, as a result of improved time, cost and accuracy of talent management. For instance, on an average we can see an increase of 33 percent in fulfilling external requisitions with internal people; a decrease of 58 percent in the time taken to shortlist candidates; a 36 percent increase in on-time allocations and 51 percent reduction in allocation failures.”
EdGE Networks raised $4.5 million in Series A funding led by Kalaari Capital with participation from Ventureast in October 2017. The team says they plan to use the capital to strengthen the company’s core product offerings and also grow geographically. And this is reflected in the product roadmap for FY19 as well. A key focus for the product team is to expand the product features to align with the needs of the US, UK, & Singapore markets. “While every enterprise customer has different requirements and priorities, every new geography requires us to additionally focus on three key aspects – the systems we need to integrate with, the security and compliance we need to adhere to, and the ROI models we need to support.”
The team is also looking to launch the Talent Transformation solution and simultaneously introduce a Talent Acquisition solution for small and medium businesses (SMB) customers. And, from a core technology point of view, the team is working on upgrading their Data Science engine from Machine Learning to Deep Learning. “Moving from Machine Learning to Deep Learning will increase the quality of our outputs manifold, will allow us to get ready for new verticals faster, and will reduce the deployment time for our customers,” explains Mridul.
In addition, there is constant work happening in the background to ensure that their products are future-ready. “We do that by focusing on three key areas –ensuring that our products can quickly adapt to our customers’ evolving priorities and processes, staying at the forefront of technological innovations, and keeping a close eye on all future developments related to the talent supply chain.”
Beyond AI and why IoT is not just a buzzword
Regarded as a nerve centre for technology, research, and investment, IoT is changing the prospect and predictability of the future. On the rapid proliferation of IoT and tens of billions of connected devices, Mridul says, “With such growth, we are looking at a hardware and software ecosystem that is exponentially more complex than the standard web system. Which means you will need a technology platform that has the capability to handle millions of device connections, maintain bi-directional real-time data streaming, manage scale, and ensure security and privacy. Such complexity is better handled through a managed IoT platform. Additionally, a managed platform will be better able to provide AI-powered capabilities by the virtue of anonymous learning from the collective.”
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