It’s no secret that many organizations and job seekers find the hiring process exhausting. It can be time consuming, costly, and somewhat risky for both parties. Those are just some of the experiences we wanted to change when we started Gyfted, a pre-vetted talent marketplace for people who complete tech training or degree programs and are looking for the right career move. At the same time, we’re helping businesses save time and improve recruiting outcomes with our automated candidate screening and sourcing tools.

Our vision is clear: To take a candidate through one structured hiring process, and then put them in front of thousands of companies. It’s similar to the common app system in higher education. Sounds simple, but it is a herculean technical and UX task. To succeed we had to combine advanced psychometric testing, machine learning, the latest in behavioral design, and develop the highest quality structured, relational dataset to represent candidate and manager profiles and preferences on our network. Fortunately, we were cofounded by world-leading experts in these areas including Dr. Michal Kosinski, one of the world’s top computational psychologists, and Adam Szefer, a gifted young technologist. We’ve been joined by a group of equally talented employees, most of whom work remotely in Poland, US, Switzerland, UK, Israel, and Ukraine.

The influence of dating platforms and matching

When seeking inspiration, we were influenced by the success of dating platforms, especially Bumble with its focus on commitment. These platforms have done a great job using design to match people together.

We like to think we’re doing the same for recruiters and candidates in terms of not only role and culture fit matching, but also through a fundamental feature of Gyfted, which is that our job-seekers are anonymous. This helps recruiters meet one of their goals today, which is to minimize bias in the hiring process and enable objective, diversity-oriented recruiting.

Another of our unique selling points is that we conduct candidate screening that is gamified and automated, with a structured interview, where the interview remains with the candidate’s profile. We roughly estimate that just for the 15 million open jobs on LinkedIn, if companies fill in 50% of those via external recruiting, and conduct a screening interview with 10 candidates per job filled at $50/hour paid to an employee to do the screening, that’s $3.75 billion and 75 million hours in direct costs. This is on top of applying for jobs, selecting CVs, and coordinating the process, which takes an even bigger financial and time toll on both applicants and recruiters. Instead, it would be better to take one interview for 1000 companies. The impact of what we want to achieve with our vision is enormous.

We also offer career discovery and career search tools for job candidates. This includes free, personalized feedback for every job-seeker. Right now, we’re aiming the service at students, bootcamp graduates and juniors, helping them to land jobs in tech and the creative industry at large. Next, we’ll expand into mid and senior roles. In the long run we want to reshape how recruiting happens through a common app that saves everyone in the market significant time and resources, helping people find jobs not only faster, but jobs that truly fit them.

Developing advanced AI applications with Google Cloud

We obtained our original funding from angel and institutional investors, and we were selected into the Fall 2021 batch of StartX, the non-profit start-up accelerator and founder community associated with Stanford University. But like most startups our budgets are tight, and we need to find ways to operate as efficiently as possible, especially when building out our technology stack and developer environment.

That’s where Google Cloud comes in. It’s a lot more affordable and flexible than competing solutions, and our developers love it. We use Google App Engine for the hosting and development of our applications giving us enormous flexibility. Vertex AI enables us to build, deploy, and scale machine learning models faster, within a unified artificial intelligence platform. On top of that we use Google Vertex AI Workbench as the development environment for the data science workflow, which allows us to have everything that we need to host and develop innovative AI-based applications.

BigQuery, Google Cloud’s serverless data warehouse, is another stand-out solution for us. We use it to crunch big data from all our systems and the UX is very intuitive and easy to use, allowing us to use it across the business and get insights from a wide range of employees, not just technical experts.

Above all, Google Cloud helps us solve the main platform challenges facing Gyfted including scalability and identity management, so we are perfectly positioned for growth. Right now, we handle about 2 million candidate interactions, a volume we expect to grow exponentially. As that number grows, we rely on Google Cloud to help us scale securely and with reliability.

Eliminating bias from the hiring process

Our technology partners have also been integral to helping us get to an advanced stage of our beta program. MongoDB on Google Cloud takes the data burden off our teams and reduces time to value of our applications. We can stay nimble and can scale database capacity at the push of a button.

Our collaboration with the Google team has been fantastic. Our Startup Success Manager is an expert when it comes to Google Cloud solutions, and he also understands our business from his own experience as an entrepreneur and an investor. It’s great to have an internal point of contact who can help us navigate all of Google’s resources.

I’d also stress the extent to which Google Cloud values align with ours. For example, a key benefit for our customers is the ability to strip unconscious bias out of the hiring process. Google Cloud tools support this commitment to diversity, especially when we are building out our AI models.

On a team level, we also appreciate the support that Google Cloud has shown through its Google Support Fund for Start-ups in Ukraine. This has helped many Ukrainian businesses to continue to operate at a very challenging time, including startups with remote, distributed teams in Poland where most of us stem from.

If I had to sum up Google Cloud and our collaboration with Google for Startups in a phrase, I’d say that it adds enormous value to our business while removing much of the risk when scaling up a start-up. We’ve seen the addition of many new tools and features in the past two years and our Google mentors are always looking at the best way these can be integrated with Gyfted’s own roadmap. That means that we can continue to transform recruiting and hiring processes with the support of one of the world’s most advanced tech companies as a strategic growth partner.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

Google Workspace recently commissioned Economist Impact to complete a global survey (October 2021)* on the state of hybrid work, including its challenges and opportunities. We already knew that the pandemic had fundamentally changed the world of work, but the survey emphasizes the scale, reach, and longevity of those changes.. 

Over 75% of respondents believe that hybrid/flexible work will be a standard practice within their organizations in the coming three years. Given that 70% of respondents said they never worked remotely before the pandemic, it’s clear that hybrid has become the dominant model for work and that it’s here to stay. 

But it’s also clear, as we’ll see below, that hybrid has some serious gaps that need to be addressed if it’s going to be sustainable and successful in the long term.

Defining hybrid work

As part of our work on the hybrid work survey and a broader project with The Economist Group, we interviewed a group of experts across research, consulting, business, and the advocacy world to arrive at a definition of hybrid work that captures all ways work is changing.

As Brian Kropp, one of the interviewees and vice president at Gartner, puts it: “Hybrid work is not just about different locations, but also different timings and different schedules.”

Harriet Molyneaux, managing director at HSM Advisory, a future-of-work research and advisory group, adds to this notion by describing the time-location work spectrum: “At one end of the spectrum is everyone in the office, nine till five, so both restricted time and location. And then at the other end of the spectrum is anywhere around the world at any time. So no restricted time or location. A hybrid is anything that sits in the middle of that.”

I agree with Brian and Harriet’s views. Flexibility in both location and hours is a core part of our working definition of hybrid work: a spectrum of flexible work arrangements in which an employee’s work location and/or hours are not strictly standardized.

Given this definition and framework, how is hybrid work faring and what lies ahead? 

Individual wellbeing is coming at the cost of organizational connection

Early on in the pandemic, productivity remained steady or even increased for many organizations. But it came at a cost, with levels of burnout spiking as employees juggled caregiving, homeschooling, and other demands in their personal lives with work responsibilities.

Based on the survey data, wellbeing has made an upward shift, no doubt aided by things like students returning to schools in many regions and fewer demands being made of working parents. The majority of respondents said that hybrid work, based on their own experiences, can have a positive impact on the physical, mental, financial, and social wellbeing of employees.

But it appears that individual wellbeing is coming at the cost of organizational connection. The majority of respondents said they feel disconnected from their organization and co-workers (57%), that limited networking opportunities negatively impact career growth (62%), and that limited social interactions with co-workers has had a negative impact on their mental health (54%).

To be sustainable, hybrid work models must address this sense of disconnection in real and tangible ways. And it won’t happen just by increasing the number of virtual meetings. Although 72% of people say that virtual meetings improve inclusion and participation, 68% also say there are too many virtual meetings to begin with. We need new ways for people to connect spontaneously.

Hybrid workers are often using tools built for a bygone desktop era

When asked about the most important conditions needed to achieve the long-term success of hybrid work models, the number one choice globally was “new technologies that allow for time and location flexibility.” 

Naturally, it was gratifying to see this, since Google Workspace is built on a cloud-based platform that empowers collaboration from anywhere, on any device, but the survey answer also highlights how much organizations have had to scramble to meet the demands of the pandemic. Some had to take legacy, office-centric systems and make them work for a distributed workforce overnight, often leveraging less-than-ideal technologies like VPNs that introduce digital friction.

As a result, top technology concerns of respondents include:

• unreliable internet access (if only all our WiFi connections were hybrid-ready!)
• reliance on slow or outdated tools
• accessing and maintaining files in multiple places
• relying on too many applications in order to get work done

Hybrid work, it seems to me, doesn’t need more applications and collaboration surfaces. It needs deeper, more meaningful connections in the tools and surfaces we already have. 

As organizations assess their tech stacks, they should consider whether they can shape the behaviors they want from hybrid employees (e.g., real-time collaboration, ease of information sharing) with the tools they have in place. Or are the limits of the technology determining hybrid employee behavior?

The management and culture gap

In the same way that tools have often been built for a shared physical workspace, organizational culture seems to lag behind the hybrid moment. The majority of respondents said that a lack of face-to-face supervision creates a sense of distrust among managers and employees, and that they feel stressed by increased monitoring associated with flexible work. And more than 62% said that limited networking opportunities with senior employees and co-workers has a negative impact on career growth.

Strikingly, more than 70% of respondents indicated that the culture of trust between managers and employees needed improvement. Training and management best practices (60% of people want more of both) might help fill some of the gaps, but a manager can’t build trust with their hybrid teams by going through a training program. 

The role of manager must itself evolve to meet the demands of a hybrid model. How can we empower managers to be the bridge between the “office of one” and the “office of many”? And as I discussed previously on Forbes, driving towards impact rather than output is a more sustainable metric for team engagement and productivity. It also dispenses with monitoring as a core part of a manager’s role, freeing them up to be a coach-and-connector.

How Google Workspace can help bridge the hybrid work gaps

No one has all the answers for how to make hybrid work successful, and I suspect we will see iterations of the model into 2022 and beyond as organizations—including Google—experiment with the right mix of location, culture, processes, and tools. 

On the technology front, Google Workspace is uniquely positioned to bridge many of the emerging hybrid work gaps. Over the last year, we’ve delivered a set of innovations that are designed to deepen collaboration experiences while strengthening social connections within and across teams. 

For example, we launched smart canvas to bring new collaboration capabilities to the places where people are already working together, helping to keep them connected, rather than having them switch tabs or open new apps. And we delivered Spaces as a central place for collaboration, where teams can share ideas, work on documents together, and manage tasks from a single place. Because all their work is preserved for future reference, team members can easily jump in and contribute at a time that works best for them, seeing a full history of the conversations, context, and content along the way. Spaces helps people maintain individual wellbeing while preserving the health of a group project.

In Google Meet, we’ve introduced real-time captions in multiple languages (in preview now), the ability for people to customize their video tiles, including being able to turn off their own to help with meeting fatigue, and we’ve implemented automatic light adjustments and noise cancellation. These changes help ensure that everyone can be seen and heard.

Meanwhile, features like hand-raise, Q&A, and polls have helped make meetings more inclusive and companion mode (in preview) can bring these to life in hybrid settings, to help ensure that there’s one cohesive conversation between the people in the office and their colleagues working somewhere else. We view companion mode as a bridge between the office and “somewhere else.”

On the personal wellbeing front, we launched Focus Time, which lets people block out their calendars for uninterrupted focus work, and Time Insights in Google Calendar, where employees can look at how they’re spending their time and adjust as needed.

As hybrid work continues to evolve, Google Workspace is committed to supporting wellbeing and seamless collaboration with tools that can remove digital friction and help people maintain connections—to each other and the organizations they work for.

^{*Survey details: The survey, completed in October 2021, polled a total of 1,244 employees and managers in four regions (North America, Europe, APAC, and Latin America), from more than 15 industries, in every age group, and from both small and large organizations. The focus was on knowledge workers, though it’s important to note that some of those people have been working on the frontlines; 20% of respondents indicated they haven’t worked remotely at all during the pandemic. This includes people in hospitality, retail, transportation and logistics, and healthcare.}

Google Cloud introduced Document AI to automate document processing and to streamline workflows with state-of-the-art machine learning models. With the deep neural networks, the models generalize the learning from seeing hundreds of thousands variations of the documents. But when information is missing or ambiguous on a document – like a missing address or entity name – a human may need to search for it…often on Google.

With Document AI, we are bringing the power of this “Google search” to help customers understand their documents. This means that the same Google knowledge graph technology that helps you find the name, address or phone number of your favorite restaurant can now enrich your document extraction with the right name, fully qualified address, and updated phone number.

Here is a sample payslip…

Imagine a bank employee entering this to capture a customer’s income to qualify them for a loan. When extracting information from this payslip, what employer name should she key in? She might take the time to go into Google and find the right correct legal entity name; or she might just guess and move on, potentially creating data reconciliation headaches down the line.

With Document AI, there is a better way. Our native integration with the knowledge graph means that we can deliver both the specific text from the payslip as well as Google’s best understanding of the actual name of the company that operates at this address. This is an important step to translate from “what has been said” on a document to “what does it mean”.  By normalizing the value as you process millions of documents, you are improving accuracy and consistency at the beginning of the data processing workflow, making downstream integration, data analytics and business intelligence tasks at ease.

How EKG Enrichment Works

In a nutshell, Knowledge Graph is a knowledge base that uses a graph data model to integrate interlinked entities, including objects, events, processes or abstracted concepts. Google announced its Knowledge Graph in 2012 as a way to organize information from the Web and to enhance Search results. Different from Google Knowledge Graph, Cloud EKG (Enterprise Knowledge Graph) focuses on entities that are more relevant to enterprise customers, such as organization, product, people, locations, etc.

In EKG, every node is called an entity. Each entity in the graph represents an object, such as an organization. EKG aggregates all the information about a thing into a single entity, thus each entity represents a distinct and identifiable real world concept. The uniqueness of these entities in the graph is one of the reasons that make EKG useful. The edges between nodes are called relationships. When representing attributes, the relationships can be considered as properties, such as the name of a company, the price of a product, etc. When representing relationships, they connect entities in the graph, such as the CEO of a company, the seller of a product.

Entity linking, as its name suggests, is the task of assigning a unique identity to entities mentioned in text, images or videos. In the context of EKG, it connects the text mentions to entities in the graph. Under the hood, the recognition takes both the mention and its context information into consideration for linking to the best matching candidates in the graph.

Entity Enrichment, is essentially linking entities in EKG to documents, and using the attributes of linked entities to enrich the extracted information.  The entity linking on documents is based on the understanding of both documents and the entities in the graph. First the document parser annotates entity mentions, which provides semantic meanings to the content of the document. Then Entity Linking selects a list of entities from EKG based on the types of these mentions, and matches to the best entity by comparing the attributes found in the document with the the relationships of the selected entities.

How to leverage the enrichment result

Knowledge graph enrichment is a built-in feature for Lending DocAI , Procurement DocAI and Contract DocAI today, and we are actively working on expanding it to cover more document types and parsers on the platform. To use the knowledge graph enriched values, look out for the entities fields under normalizedValue, returned by the API.

  {
      entities: [
         {
          "textAnchor": {
             "content": "Google Singapore"
          },
          ….
           "normalizedValue": {
              "text": "Google Asia Pacific, Singapore"
          }
        }
      ]
    }

To learn more, check out the Document AI webpage, and EKG Enrichment page to see the list of supported parsers and fields.