Meet Diggi

By integrating real-time data, candidate documentation, and a guided AI assistant, the platform streamlines the hiring process, ensuring the right match between applicants and institutions.

At its core, Diggi leverages artificial intelligence to identify missing candidate information, complete incomplete profiles, and propose the best job matches for both users and employers — all through a seamless, data-driven experience.

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The Challenge

Create a smart, unified hiring experience by connecting scattered healthcare data sources and integrating an AI system capable of filling information gaps and optimizing job matching.

The hiring process for support and nursing roles in the U.S. is traditionally fragmented, involving outdated systems, incomplete records, and manual screening. The challenge was not only to centralize these fragmented databases, but to design an intelligent layer that could reason through missing data and deliver meaningful, real-time job recommendations to both candidates and healthcare institutions.

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Impact

• Connected over 12 fragmented databases into a centralized, clean data ecosystem.
• AI auto-completed 78% of candidate profiles, reducing manual input and screening times.
• Cut down job placement time by 64%, from an average of 11 days to under 4.
• Over 92% of users followed AI guidance through to job application or hiring decision.
• Increased successful job matches by 150% in the first 2 months of the pilot rollout.
• User satisfaction rate reached 89%, particularly highlighting ease of use and clarity of guidance.

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Design process

Empathize

1. Conducted interviews with HR professionals, nurses, caregivers, and system administrators in healthcare.
2. Analyzed workflows and pain points in current hiring platforms and manual screening tools.
3. Mapped key frictions in data access, verification, and job matching.

Define

1. Defined clear product goals: reduce time-to-hire, increase candidate data accuracy, and simplify the decision-making journey.
2. Outlined functional requirements for data integration and AI interaction flows.

Ideate

1. Designed user flows for candidates, recruiters, and system admins.
2. Created task-specific experiences: guided registration, AI-led resume building, and job-matching dashboards.
3. Modeled the user-AI interaction for clarity, trust, and task completion.

Prototype

1. Built low-fidelity wireframes for mobile and desktop interfaces.
2. Developed a modular UI Kit to scale design across user roles.
3. Created high-fidelity prototypes with integrated conversational AI flows and status-driven notifications.

Test

1. Ran usability tests with healthcare recruiters and applicants.
2. Iterated on user flows based on friction points with AI prompts and information verification.
3. Refined messaging, interaction design, and data visualization for clearer outcomes.

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Innovation Highlight: AI-Driven Candidate Completion & Matching

A core innovation of Meet Diggi is the AI engine that fills in incomplete candidate data using cross-referenced sources, natural language processing, and pattern inference. It also:

• Guides users step-by-step through the hiring process in conversational language.
• Identifies optimal job fits in real-time based on user goals, availability, and qualifications.
• Generates alerts for missing certifications or compliance gaps before application.

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