Talent Intelligence Platform

Talent intelligence platforms help companies make smarter hiring decisions by turning complex data into actionable insights. They help HR assistants, recruiting specialists and hiring specialists identify high-potential candidates to map internal career growth opportunities. The platform enables HR teams to see patterns, predict needs, and design better employee experiences.

Goal

The goal was to build a data-centric, transparent talent intelligence platform that helps organizations and candidates find better, faster matches through skill-aligned recommendations. This two-sided experience serves recruiters, hiring managers, and candidates, evolving from automated, high-efficiency matching into a collaborative intelligence workflow where human insight guides and refines recommendations. The platform is designed to be scalable, flexible, and sold standalone or as part of a suite.

Problem Statement

Talent managers face inefficient workflows while managing a fragmented candidate pool across multiple systems. This makes matching candidates challenging and frustrating for both managers and candidates who lack the information needed to work effectively.

Challenges

  • Complex Matching & Transparency: Explain why a candidate matches a role

  • Two-Sided Experience: Balance needs of employers and candidates in one platform

  • Scalability: Design for organizations of varying sizes and volumes of roles

  • Data Volume: Manage and present large amounts of talent information effectively

  • Cross-System Integration: Ensure smooth interactions across existing systems

Final Outcome

Delivered a 4-phase release where each logical bundle that delivered immediate value. T foundation for future growth..

Phase 1: MVP

Foundational platform

3 of 4 personas were supported. The platform established the foundation for key workflows and interaction patterns. Information can be passed along as well as flow continue onto existing portal

Release 2

Robust Candidation Experince

More robust candidate experience

Release 3

Scalable Fourth personsa suppored with additional flows. appaian

Release 4

Appian and more robust features

Role-Driven Design Process

Discover

Define Roles

Ideate

Design

Similaties Existing Personas

We reviewed the personas from the existing platform as part of the new design work. We color-coded goals, responsibilities, and other focus areas to similar overlaps, role-specific needs, and areas we might or might not support. By examining these alongside the newly developed personas, we identified where the new product could also support existing users.

Product Discovery

Personas

New Personas

Because this product was highly role-centric, we designed specific flows for each persona, clarifying their needs, goals, and responsibilities, and prioritizing the most important features for development.

New Product First

New Platform

Although we reviewed the existing personas, we didn’t want to consider just yet how they’d fit into the user flows just yet. Our priority was to design the best possible experience for this product first. For this product, we designed multiple journey maps including existing personas. Each map defined the optimal experience independently, and by focusing on each persona in isolation, we designed experiences that addressed their unique needs and priorities. For this project, we designed individual workforce roles to optimize responsibilities and workflows, defining each role independently to provide clarity in tasks, goals, and priorities. Next, we examined how tasks and processes aligned across roles, helping define each role clearly while considering a scalable and consistent system. With the foundation in place, we examined how the product could align with the existing platform. This approach allowed us to identify potential connection points, some of which didn’t have a UI, without impacting either product, creating a more cohesive and scalable suite.

The end-to-end experience of new and existing candidates acted as a foundation to drive cross-functional alignment and define the scalable product roadmap.

Early discovery mocks to visualize similar high-level flows for one persona

Support Exist Platform

Although we reviewed the existing personas, we didn’t want to consider just yet how they’d fit into the user flows just yet. Our priority was to design the best possible experience for this product first. For this product, we designed multiple journey maps including existing personas. Each map defined the optimal experience independently, and by focusing on each persona in isolation, we designed experiences that addressed their unique needs and priorities. For this project, we designed individual workforce roles to optimize responsibilities and workflows, defining each role independently to provide clarity in tasks, goals, and priorities. Next, we examined how tasks and processes aligned across roles, helping define each role clearly while considering a scalable and consistent system. With the foundation in place, we examined how the product could align with the existing platform. This approach allowed us to identify potential connection points, some of which didn’t have a UI, without impacting either product, creating a more cohesive and scalable suite.

MVP Hypothesis

We drafted a potential MVP flow based on the personas and priorities we defined. This hypothesis would serve as a starting point to guide discussions with stakeholders, highlight gaps, and inform decisions about which features to prioritize first. It was intended to evolve as we refined personas, validated assumptions, and gathered feedback, so the MVP addressed the core personas’ needs we defined earlier.

Wireframing

Touchpoints

At each touchpoint, we analyzed both tasks and decision points to determine whether multiple personas were seeking the same information or performing similar actions. Overlaps were prioritized based on frequency and importance, with higher-priority overlaps representing areas where multiple users’ needs converged. We also considered dependencies between personas and the frequency of tasks to ensure that the analysis captured how workflows intersected across the system.

Interaction Design

We developed micro flows for each persona to map the granular steps of their tasks. This level of detail was important because it allowed us to understand what each user actually needs and prioritize the most important elements of their workflow.

After identifying and prioritizing overlaps, we began designing shared interactions using wireframes. For each touchpoint, we examined where personas’ needs converged, assessed the priority of each overlapping need, and noted areas where there was no overlap at all. This approach allowed us to address the requirements of multiple personas simultaneously and create shared workflows that were coherent and aligned with the priorities of the overlaps.

Non-Overlapping Flows

After designing the overlaps, we created basic wireframes of the remaining pages for each persona. These wireframes provided a clearer view of what each persona was doing at every step, and focused on interactions where it was relevant to the happy paths. They also showed how the non-overlapping pages connected to the shared workflows, providing completeness before moving on to high-fidelity mockups.

Hypothesis for MVP

Proposed and ulimately valdatied

Hypothesis for MVP (validated and used above)

Foundational MVP

Discovered potential reusable components designed for variation

Defined the priorities for each persona and ideated components to support them.

Created mini-screens to better visualize user flows.

Identified data-rich areas and potential challenges

Final Designs

Once we knew what we were releasing and when, we created high-fidelity mocks of multiple design options generated from the wireframes, designed to be as close to final as possible and nearly ready for development. The goal was to ensure that users had the information they needed readily accessible at each step, supporting their decision-making and task completion.

In developing these mocks, we carefully considered hierarchy, prioritizing the most important information for each persona so that critical content was immediately visible. We also evaluated interaction patterns to confirm that users could efficiently navigate the platform and complete tasks without confusion or unnecessary steps.

Areas that had data-rich sections while keeping returning candidates engaged.

Early exploration of ways to surface and organize key information.

Initial work targeted the areas of the site visited most often

Scannable cards highlighted new information and allowed for deeper exploration