Celito
From concept to completion!
Discover how the Celito platform came to life without the hassle of hiring a designer!
Web, Mobile
Healthcare
AI
Growing Business
Boston, MA
Company Overview
Celito’s mission is to accelerate drug discovery by reducing the manual burden on researchers. Their vision: use LLMs to automate knowledge discovery in pharma, where research cycles are slow, fragmented, and buried in dense documentation. Their team brought deep domain knowledge and NLP models - but needed a partner to translate that into a usable, intuitive product.
Challenges
Celito’s core challenge was translating powerful AI outputs into a simple, intuitive experience for non-technical users. The platform needed to organize complex clinical data in a way that felt clear and usable - without overwhelming researchers. We also had to streamline decision-making workflows, minimize cognitive load, and deliver a polished MVP that was ready for investor demos and real-world testing within weeks.
Our Design Approach
Discover
- Discovery workshops
- User Research
UI/UX Design
- Wireframes and prototyping
- Usability testing
- UI Design
Deliver
- Design System
- Dev ready UI Screens
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User Research
- Design an interface that helps non-technical users interact with AI confidently.
- Structure large-scale clinical data in a way that feels accessible, not overwhelming.
- Create workflows that reduce noise, improve focus, and guide researchers to action.
- Deliver a working, high-fidelity MVP ready for demo, investor meetings, and beta testing.
Ideate - Prototype - Test
Based on the insights, we held brainstorming sessions with stakeholders, generating 50+ ideas focused on simplifying workflows and enhancing scalability. We created wireframes to establish a clear information architecture that minimized redundant steps in claim registration and approvals.
Solution
MVP Highlights
Structured Source Selection Flow
- We designed a guided interface that allows users to customize AI responses by filtering source types (e.g. clinical trials, meta-analyses).
- Improves UX by building trust in AI outputs and aligning results with user intent.
Enhanced Topic Discovery
- Users can explore AI-suggested subtopics or questions through a card-based layout. Each card surfaces key highlights and supporting citations.
- Improves UX by reducing cognitive load and turning dense research into bite-sized insights.
Contextual Suggestions
- Introduced related topic suggestions for each query, helping researchers uncover additional relevant insights.
- Enhanced data exploration, reduced research time, and broader dataset utilization.
Integrated References in AI Answers
- Added direct links to references within AI-generated answers, ensuring easy validation and credibility of insights.
- Increase trust in AI outputs, streamlined research validation, and reduced manual cross-referencing effort.
The Impact
The platform was launched in just 7.5 weeks - from zero to clickable MVP.
Founders used MVP to run live demos for pharma innovation teams and investors.
Built a component-driven design system that scale easily across new modules.
Improved accuracy in AI responses with a user-friendly, intuitive interface.
“
Kreeya helped us bring Celito from concept to MVP with a user-first design approach. They structured complex clinical workflows into a clean, intuitive interface that made AI outputs easier to trust and use.
James Brown
Co-founder, CTO at Celito
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