Overview
Focused on onboarding, data ingestion, and insight exploration to transform messy qualitative input into structured, decision-ready outputs.
Univrs is a platform designed to help product teams turn qualitative user feedback into structured, decision-ready insights tied to business impact.
I contributed to early product definition and led design across onboarding, data ingestion, and insight exploration. My focus was on shaping a coherent system that transforms messy, real-world input into clear outputs that teams can act on.
My Role
- Defined the product name and brand design language
- Led onboarding and data ingestion flows from early wireframes through refined interaction models
- Co-designed core product surfaces including data mapping, dashboards, system administration, and insight exploration
- Drove UI quality and consistency across the v1 product experience
- Led a refresh of the marketing website using agentic development workflows
I partnered closely with a product collaborator who led much of the underlying modeling, scoring, and data processing logic, while I focused on the product experience, interaction model, and interface coherence.
The Challenge
Univrs needed to bridge a difficult gap: turning noisy, disjointed customer data into clear product priorities tied to measurable business outcomes.
The core challenges were:
- Input ambiguity: Feedback came from many sources, including support tickets, surveys, CRM notes, sales calls, interviews, analytics, and app reviews
- User uncertainty: Users often didn't know what good input looked like or how to prepare it for analysis
- System opacity: AI-generated insights needed to feel explainable, trustworthy, and grounded in underlying evidence
- Business relevance: Insights needed to connect to revenue, retention, conversion, and roadmap decisions
- Workflow fragmentation: Existing processes required manual synthesis across tools, making it difficult to separate signal from noise
The design challenge was to create a system that guides users from raw input to confident decision-making, helping them understand what matters, why it matters, and where to act.
Design Strategy
I approached the product as a single continuous loop:
Input → Structure → Insight → Decision
To support this, I focused on four principles:
- Guide, don't block: Provide structure without over-constraining messy real-world data
- Keep users in context: Avoid forcing users into separate modes or disconnected steps
- Make the system legible: Help users understand how input maps to output
- Connect insights to impact: Surface business relevance, not just patterns
These principles shaped decisions across onboarding, ingestion, mapping, and insight exploration.
Onboarding
Onboarding is designed to establish context quickly and reduce friction, helping users reach meaningful output as early as possible.
Organization setup introduces key dimensions that shape how insights are structured and interpreted across the system.
Data Ingestion
The ingestion flow guides users through uploading and validating their data, providing clear feedback and reducing the likelihood of errors.
The experience balances simplicity with transparency, ensuring users understand what has been uploaded and how it will be used downstream.
Data Mapping
The data mapping interface transforms raw input into structured data. It balances flexibility and clarity, allowing users to classify fields while maintaining context.
Evolving the Mapping Model
Early exploration treated mapping as a direct translation between uploaded fields and system-defined fields, using a two-column layout.
While straightforward, this approach required users to mentally reconcile two separate representations of the data, increasing cognitive load and making it harder to validate mappings with confidence.
This led to a shift toward a unified table model, combining classification, real data preview, and validation into a single continuous view.
Key Tradeoffs
Designing the mapping interface required balancing:
- Flexibility vs. simplicity: Allowing custom mappings while preventing users from getting lost
- Automation vs. control: Using auto-detection to accelerate setup without removing user validation
- Density vs. clarity: Showing enough information to validate mappings without overwhelming the interface
These tradeoffs led to decisions like inline classification, real data previews, and progressive disclosure of mapped fields.
Key Decisions
Because input data was often inconsistent and partially structured
→ Auto-detection was visible and editable, allowing users to validate and override system suggestions.
Because mapping required comparing classifications with real input
→ Example data remained visible alongside field definitions, reducing errors and increasing confidence.
Because switching between separate modes would increase cognitive load
→ Classification and validation were handled inline, keeping the workflow continuous and in context.
Product Structuring
Users define product-level context, enabling the system to connect insights to meaningful business dimensions.
Insight Exploration
The platform enables users to move from raw feedback to prioritized insights. The interface is designed to support both rapid scanning and deeper investigation.
A tabular layout allows users to quickly compare insights across metrics such as impact, revenue at risk, and theme. A persistent detail panel provides immediate access to supporting context without breaking flow.
Key Decisions
Because users needed to identify what matters quickly
→ Insights were structured for high-density scanning rather than narrative browsing
Because trust depends on context
→ A persistent detail panel allows users to inspect evidence without losing their place
Because insights must drive action
→ Business impact metrics are surfaced alongside qualitative signals
Key Design Decisions & Tradeoffs
Designing Univrs required balancing flexibility, structure, and trust in AI-assisted outputs. The following decisions shaped how the system works in practice:
Inline classification vs multi-step workflows
We embedded classification directly in the mapping interface to keep users in context and reduce cognitive load. A separate workflow increased friction and slowed analysis at scale.
Structured mapping vs flexible tagging
We chose a structured model so outputs can be aggregated and tied to business metrics. While tagging offers more flexibility, it makes downstream analysis inconsistent and less reliable.
Transparency vs full automation
We prioritized user trust over full automation by keeping key parts of the system reviewable and controllable. Rather than fully abstracting the process, users can validate and adjust how insights are formed.
Outcome
The resulting experience created a more structured and approachable path from raw input to insight:
- Reduced ambiguity during onboarding and data ingestion
- Improved clarity and confidence in field mapping
- Enabled faster movement from uploaded data to actionable insights
- Created a cohesive product loop connecting input, structure, and decision-making
While early-stage, the product reached a level of clarity and polish that supported demos, stakeholder alignment, and continued iteration toward production readiness.
Reflection
This project was built as a scrappy, fully bootstrapped effort alongside full-time work, with progress unfolding in cycles as our team balanced professional and personal commitments.
These constraints forced a focus on what mattered most: establishing a clear product model and getting to a working, demonstrable system quickly. Rather than over-optimizing individual screens, the priority was creating a coherent end-to-end flow from input to decision.
The v1 product is now live and able to demonstrate a core capability that is often missing in product teams: connecting qualitative feedback directly to measurable business impact.