VeriComms: Designing a White-Label AI Platform for Public Health Communicators
Client
Science 2 People
ROLE
Product Designer, Research & Discovery


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Outcomes & Impact
This project reinforced that designing for trust, clarity, and adoption is as critical as solving usability challenges. Working with PHCC communicators confirmed that AI adoption in high-stakes public health environments depends on credibility, transparency, and human oversight.
Key lessons from this phase:
User-centered discovery drives design focus: Early research and audits (including the AKARI review) clarified which features were essential, shaping a high-value MVP.
Iterative prototyping uncovers real-world gaps: Usability testing highlighted feature discoverability issues and guided solutions like coach marks, contextual AI prompts, and translation assistance.
Collaboration is essential: Designing AI for public health requires balancing automation with human expertise, particularly for culturally and linguistically nuanced tasks.
Clear communication strengthens impact: The Loom walkthrough translated research insights into a tangible experience for stakeholders, demonstrating the rationale behind design decisions.
While this prototype is not the final product, this phase validated the core features users need and set the foundation for a trustworthy, efficient, and human-centered AI platform for public health communicators.
Team Achievements & Measurable Outcomes:
Client Satisfaction: 95/100. Our team was recognized for organization, curiosity, responsiveness, and a productive, flexible approach during the sprint.
Measurable Outcomes:
User personas and prototypes clarified audience journeys for Akari and were shared with design partners.
Deliverables informed early messaging and design strategy for the CommsCompanion (white-label) pilot launching 2026.
Insights strengthened VeriComms’ market positioning, supporting fundraising and partner conversations.
Qualitative Outcomes:
Journey mapping and prototype contributed to more intentional product scoping and feature prioritization.
Several UX recommendations from the sprint are being incorporated into the current design/build phase.
The process validated that communicators want AI to simplify science translation, not replace creativity, now central to the product narrative.
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