Visage.jobs
OVERVIEW
Visage.jobs is a talent acquisition platform that streamlines recruitment by leveraging AI and sourcing experts to deliver pre-vetted candidate lists to employers.
Hopward, our second product, reimagines the recruitment agency model by combining AI-driven talent sourcing with personalized hiring concierge services. It offers two service levels: 'Candidate Search' for hands-on recruitment, providing a shortlist of qualified candidates, and 'Hopward Agency' for a comprehensive approach, including candidate screening and interview setup.
ROLE
Sr Product Designer
User research, Interaction design, Product strategy, Discovery,
Prototyping & Testing.
April 2023 - September 2024
Background
The switch to Hopward was necessary to address gaps or challenges in the recruitment market that Visage.jobs couldn’t fully solve.
Expanding Target Audience:
While Visage.jobs focused on enhancing recruitment processes for employers with internal hiring teams, Hopward addressed businesses that lacked dedicated HR departments or needed more personalized, hands-on hiring support. This diversification allowed the Visage to cater to a broader range of clients.Market Demand for Comprehensive Solutions:
Employers often seek end-to-end hiring solutions, not just sourcing tools. Hopward bridged this gap by combining AI-driven talent sourcing with services like candidate screening and interview setup, mimicking a full-service recruitment agency.Enhanced Revenue Opportunities:
By offering premium, concierge-level recruitment services, Hopward could generate higher-value engagements compared to the subscription-based or tool-driven model of Visage.jobs. At the same time, our hands on service allowed easy access for any hiring manager to quickly get candidates in their inbox.Differentiation in a Competitive Market:
The recruitment tech space is highly competitive. Hopward’s model was designed to set the company apart by blending advanced AI capabilities with a human touch, appealing to businesses seeking a unique and efficient hiring experience.
From Tools to Transformation: Why Hopward Was the Next Step
Research
Customer and Market Research
To kick off Hopward's development, I conducted interviews with our existing customers to uncover pain points and identify areas for improvement in the current product. Additionally, I engaged hiring managers from companies without dedicated recruiting teams to better understand their unique hiring needs and current practices
Collaborative Workshops
I designed and facilitated in-person team workshops with cross-functional stakeholders. These workshops were structured with targeted activities to ensure we produced actionable artifacts, such as user personas, journey maps, and vision statements, to define Hopward's direction
Outcome of Workshops
The workshops enabled our product team to align on a shared vision and create a detailed roadmap for Hopward's design and development. This roadmap outlined the user experience, core functionalities, and visual design principles that would shape the product.
Empower users to make the right decisions
Its not enough to tell users that we found suitable candidates, with LLMS we could easily indicate matched requirements and candidate highlights
Customized Job intake
A key discovery was that agencies and hiring managers have a large variety of candidates to search for, we aimed to help identify key search parameters with as little information from the user
Create a sense of urgency
A key product feature is gaining candidate interest before sending the candidate to the user. Similarly we had to find ways create a sense of urgency for the user to keep the lead warm and move the candidate through the hiring process
Be transparent at all stages of the candidate search
Many hiring managers and recruiters dont often have a clear view of what is happening in the background. Where the search stage is and the work being done in the background. We wanted to be as transparent as possible to lift this veil.
Why a Chat-Centered Interface? Streamlined, Intuitive Experience
Conversational and Familiar
A chat-centered interface offers an intuitive experience, leveraging users’ familiarity with personal messaging apps. This reduces the learning curve and drives quicker adoption.
Centralized Communication Hub
All job-related updates, notifications, and messages are consolidated in one space, eliminating the need to switch between screens and ensuring seamless access to key information.
Real-Time Updates
Chat enables instant updates and interactions, fostering proactive workflows where users are notified of new developments immediately.
Action-Oriented Engagement
The interface guides users with optional next steps when no immediate tasks are required, ensuring continuous engagement and clarity on what’s next.
Personalized Support
With chat at the core, users can ask questions, request updates, and access tailored insights, enhancing satisfaction and reducing drop-offs.
Ideation & Prototyping
We sketched, wireframed, and mocked up all sorts of ideas to narrow product scope and build prototypes quickly. We tested our initial concepts with several stakeholders, industry experts, and current visage users. We iterated based on those learnings, refined our prototypes, and tested again.
Our design prioritized direct manipulation and instrumental interaction, enabling users to intuitively manage job listings and engage with features through real-time updates and responsive elements. Interactive notifications kept users informed and on track, while tools streamlined tasks to help them achieve their goals efficiently. By focusing on meaningful actions, we reduced friction and made the interface more engaging and goal-oriented.
Introducing Hopward!
Candidate search started. While we work with our sourcers to identify and get the interest of suitable candidates, users can refine the search, give feedback on found candidates and view the status of their search.
New job posting - Intake. The user is lead by the chatbot to define their candidate search. Our LLM integrated chatbot iterates with the user and validates their search requirements. The days of filling out long forms are replaced with conversational interactions.
Candidates found. Once users have interested candidates, they can review and proceed with next steps in the hiring process. Intuitive filters and status’ are automatically assigned based on user actions on the candidate profile.
Feedback Loop
Keeping alignment on profiles found is a main goal during the search. Regular feedback loops are offered to the user to review found candidates and give feedback. As an optional task, if users don’t give feedback we will continue on course, while any additional info is send to our sourcers to refine the search parameters. Our LLM integration analysis the profile and highlights the matched requirements as well as highlighting matched skills.
Candidate Profiles
Candidate profile review. Users get an informative email in which all candidate highlights are provided. On the platform candidate profiles have extensive highlights, resume details, screening results and our LLM candidate matching and highlights.
Usability Testing and Interviews
One of the main roadblocks we faced was getting our users into an interview with us. To counter this dilemma we established three experiments:
Unmoderated usability testing: We explored many software solutions and ended up with Useberry. This allowed us to find our target audience and conduct unmoderated usability testing with minimal investment for the most outcome. Combined with monitoring software such as Posthog, we were able to complete regular usability testing on new features
Incentivized interviews: We offered to extend the search and guarantee additional candidates if the user agreed and completed an interview with our product team.
PostHog User Analysis: We tracked feature adoption, providing actionable insights into user behaviour, enabling iterative improvements to the product. Here's how PostHog might have helped
This combination helped us validate the success of new feature launches as well as identify break points in the user journey.
Transforming Workflow autmation to AI agent partnerships
Through user observation, usability testing and interviews we discovered many experiments to further develop the hopward platform. The development and launch of Hopward was a departure from Visage’s original core product. Ultimately, it required a lot of buy-in from several key stakeholders and while the core functionality accomplished the goals we set out for there are some key experiments that resulted in a more robust product.
Continuous Learning and Adaptation
Current State: The LLM performs tasks based on static rules and data.
Evolution: Enable the AI to learn from each feedback loop and candidate review, adapting its algorithms to improve accuracy in candidate selection, process efficiency and impactful messaging to the user at key points of the user journey.
Implementation: Implement feedback loops where the AI analyzes outcomes of its recommendations to fine-tune its models for future decisions. The AI adapts based on user feedback and prompts the user to highlight top talent, match requirements and the cater to the unique hiring patterns of each client
Proactive Decision-Making
Current State: The LLM automates tasks such as candidate sourcing and communication.
Evolution: Develop the LLM into an AI agent capable of making proactive decisions, like suggesting optimal search parameters or identifying potential hiring challenges.
Implementation: Integrate machine learning algorithms that analyzes sourcer feedback, candidate matched requirements and volume of candidates to recommend actions, enhancing user’ decision-making capabilities.
Future Considerations
Integrations
Current State: Very little integration with user’s knowledge base’s and tools.
Future Implementation: Integrating with the user’s current scheduling tools and mail providers will enhance the AI agents ability to capture the user’s tone of voice and conduct autonomous tasks with escalation protocols. Connecting to users’ knowledge bases will allow us to analyze previous hiring trends and current employee qualifications to create benchmark profiles and enhance the quality of candidates while giving deeper knowledge to the hiring managers in order to make quick decisions rather than having to dig deeper into the user profiles.