May 19, 2020

JamieAi – Recruiting AI to disrupt an industry

Artificial intelligence
Adrian Ezra
7 min
JamieAi – Recruiting AI to disrupt an industry

Adrian Ezra, the founder and CEO of JamieAi is out to change the face of an industry that, in his mind, is in dire need of a new approach. Halfway through the interview he stops to say: “Actually I love this story. Let me… It’ll take 30 seconds… A hiring manager at a very large investment bank is approached with a stack of 100 CVs to evaluate. He takes half of the CVs and throws them in the bin. The HR business partner turns to him and says: ‘Why did you do that?’ And he says ‘we don't hire unlucky people.’ That virtual bin does exist. CVs that get sent in don't get read.”

With almost 20 years as CEO of headhunting company Execuzen, Ezra understands better than most the advantages, disadvantages and inefficiencies of the recruitment space. “You look at what you get, and you look at what it costs, and you figure that there's a disconnect. Something is wrong, something has to give.” Business Chief sat down with Ezra to find out how his new passion project, London-based recruitment tech startup JamieAi, is disrupting the recruitment space using a mixture of human expertise and AI-driven automation.

“I've always thought that recruitment as an industry, or human capital as an industry, was ripe for some sort of disruption,” says Ezra. In its current state, the bulk of spending in the recruitment industry goes towards contingency recruitment: companies use multiple recruitment agencies, job boards, job-hunting sites like LinkedIn and Indeed, and internal recruiters to source new employees. “Because recruiters are competing against a whole other range of people, their interest is not finding the right fit for a role, the interest is getting the CV in first,” Ezra explains. “As a result, recruiters don't really engage with a candidate unless they’ve known them for a very long time. They don't have that much knowledge of the individual. You're just sending their CV in because, if you don't, somebody else will. As a result, the industry gets a bad name.”

Examining the recruitment process as it exists, Ezra explains: “If you actually look through the recruitment process, what does a recruiter do? When you get a job from a company, a recruiter goes out, sources the role, sources the candidates, filters the candidates, presents the candidates, engages and organizes the interview process, and closes the successful candidate into the job.” The nature of the recruitment industry until now means this process can be made inefficient by its competitive nature, as individuals receive offers from recruiters whose matches aren’t accurate, which leads to distrust and further inefficiencies.

“You wouldn't want to open a cab company today when you're looking to compete with the likes of Lyft and Uber, but you would still open a recruitment company,” says Ezra. His goal, he explains, for the recruitment sector in five years’ time, is to disrupt and reshape the industry to the same degree that startups like Uber and Lyft have changed the face of ride-sharing. “If I do my job right, and other people like me do their job right, opening a recruitment company would be the equivalent of opening a cab company, and you wouldn't want to do that.”

This is the space into which JamieAi is injecting AI analytics solutions and focused human expertise. “I believe almost all of that process, if done correctly, can be automated,” says Ezra. “I think there are certain parts of recruitment that automation, or artificial intelligence, can definitely take over. There are certain parts of it from which you just can't remove the human element; there are parts of the recruitment process where relationships will continue to be a very important piece of the attraction process. I don't think you replace that. But I think you can replace a very large chunk of what is left, which is the majority of the recruitment process.”

JamieAi’s mission centers around using artificial intelligence to automate the processes of an effective, focused recruiter with extensive and deep knowledge of the applicant’s industry. The behaviour of the platform will be informed by the startup’s current staff of experienced data science recruiters. “Part of JamieAi’s goal is to be the most accurate job matching platform. So, it's all about that filter. How do I find that accuracy for you? If I'm able to be accurate with what I am doing, if instead of working on bulk and instead of working on ease of process, I'm actually going to be able to identify a really good fit consistently.”


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Ezra confirms that JamieAi will build its AI platform entirely in-house and the process is still very much under way. “I don't want to give you the impression that we are all singing and dancing AI. AI is not making our decisions today, but the AI is being built alongside the human process,” he says. Although pre-build AI software is cheaper, and would be available faster, Ezra is committed to creating a platform specifically tailored to his company’s needs to ensure maximum accuracy. “You can buy an off-the-shelf stack, but I don't get the point. You kind of defeat the purpose because then you're trying to get the data to fit the line as it were.”

The service, Ezra says, will create efficiencies for employers and job seekers in terms of both time and money. “We believe that what we're building will save significant amounts of time. And it's the time, in addition to the lower fee, that is the real seller for me.” While most traditional recruitment services charge a percentage of a new hire’s annual salary, (recruitment solutions company Top Echelon reported that, in 2016, the average annual salary for a data processing professional was $93,319, which lead to average recruitment fees of over $19,000) JamieAi charges around $1,280 for a single job posting, a significant economy when Ezra takes into account the fact that  “if I put up a job on Indeed, and I reach out to 100 people, my open rate is between 10% and 12%. On my platform, our open rate is 90%.” Of the potential hires matched with open jobs by JamieAi, 80% are selected for interviews.

From the perspective of job-seekers, JamieAi handles applicant data and CVs differently than a traditional recruiter. “We looked at the feedback that we got from our years in recruitment and what people don't like about recruiters, and we said let's turn it around a little bit. As opposed to showing all these CVs to the employers, and in keeping with GDPR, our model works like this,” Ezra explains. “We get a job description, we filter, we take those filtered, and then we send them a message on the app.” Prospective employees then either answer ‘yes’ or ‘no’. JamieAi guarantees that uninterested individuals’ information will not be passed on to the employer, or to other companies. “We do not allow companies to go into our database,” says Ezra. “We try to base the business on a level of trust and transparency with the employee and, as a result, what we have done with almost no marketing at all is attract people to our platform.”

Currently, JamieAi caters to the data professionals’ sector, working with global corporations like Monzo, Barclays, Oracle, CitiBank and “I believe that my best recruiter is always the most focused recruiter. So, if I'm building my AI for accuracy, it has to be accurate in a space, it cannot be too wide,” Ezra maintains. “Once we develop something that we're comfortable in, we will expand.” Once JamieAi’s technology and due process reaches a stage that satisfies Ezra, he says the company has plans to expand rapidly into new careers and regions, starting with other technology professions and expansion into the United States, Germany and India over the course of 2019, picking up a large New York-based multinational client in January. Currently, 8% of JamieAi’s client base is located in the United States, with that number expected to skyrocket over the coming year. “We have a lot of warm relationships with large companies that we believe will support us when we get to the US,” says Ezra. “If we're not in the US by early 2020, we're not doing a great job.”

When asked about the future of JamieAi, an animated Ezra answers that “we're a very young company. We want people to test us; we want people to give us the hard stuff, which is what they do. If they haven't heard of us and they don't know us, they give us the hardest recruitment tasks. They give us the stuff that other people can't do. We've been tested by the big companies, we've been tested by the small companies. We don't shy away from the grind, and we're very happy to mix it up with everybody else. And I believe that what we do will eventually shine.”

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Jun 18, 2021

Intelliwave SiteSense boosts APTIM material tracking

3 min
Intelliwave Technologies outlines how it provides data and visibility benefits for APTIM

“We’ve been engaged with the APTIM team since early 2019 providing SiteSense, our mobile construction SaaS solution, for their maintenance and construction projects, allowing them to track materials and equipment, and manage inventory.

We have been working with the APTIM team to standardize material tracking processes and procedures, ultimately with the goal of reducing the amount of time  spent looking for materials. Industry studies show that better management of materials can lead to a 16% increase in craft labour productivity.

Everyone knows construction is one of the oldest industries but it’s one of the least tech driven comparatively. About 95% of Engineering and Construction data captured goes unused, 13% of working hours are spent looking for data and around 30% of companies have applications that don’t integrate. 

With APTIM, we’re looking at early risk detection, through predictive analysis and forecasting of material constraints, integrating with the ecosystem of software platforms and reporting on real-time data with a ‘field-first’ focus – through initiatives like the Digital Foreman. The APTIM team has seen great wins in the field, utilising bar-code technology, to check in thousands of material items quickly compared to manual methods.

There are three key areas when it comes to successful Materials Management in the software sector – culture, technology, and vendor engagement.

Given the state of world affairs, access to data needs to be off site via the cloud to support remote working conditions, providing a ‘single source of truth’ accessed by many parties; the tech sector is always growing, so companies need faster and more reliable access to this cloud data; digital supply chain initiatives engage vendors a lot earlier in the process to drive collaboration and to engage with their clients, which gives more assurance as there is more emphasis on automating data capture. 

It’s been a challenging period with the pandemic, particularly for the supply chain. Look what happened in the Suez Canal – things can suddenly impact material costs and availability, and you really have to be more efficient to survive and succeed. Virtual system access can solve some issues and you need to look at data access in a wider net.

Solving problems comes down to better visibility, and proactively solving issues with vendors and enabling construction teams to execute their work. The biggest cause of delays is not being able to provide teams with what they need.

On average 2% of materials are lost or re-ordered, which only factors in the material cost, what is not captured is the duplicated effort of procurement, vendor and shipping costs, all of which have an environmental impact.

As things start to stabilise, APTIM continues to utilize SiteSense to boost efficiencies and solve productivity issues proactively. Integrating with 3D/4D modelling is just the precipice of what we can do. Access to data can help you firm up bids to win work, to make better cost estimates, and AI and ML are the next phase, providing an eco-system of tools.

A key focus for Intelliwave and APTIM is to increase the availability of data, whether it’s creating a data warehouse for visualisations or increasing integrations to provide additional value. We want to move to a more of an enterprise usage phase – up to now it’s been project based – so more people can access data in real time.


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