F1
F1 · Frame
Discovery Configuration
Niche, location, fast vs. deep search mode, coverage radius, and which data sources are searched in parallel.
Product Design Case Study
LeadRx Insights
Turning fragmented business data into an AI-assisted lead intelligence workflow.
LeadRx Insights was designed to help users search territories, discover local businesses, evaluate digital presence, identify opportunity signals, and prioritize outreach with AI-assisted scoring.
Role
Product Designer / Founder
Focus
AI-assisted UX, dashboards, lead scoring, workflow design
Platform
Web app / SaaS dashboard
Status
Product concept and active build
Why this project matters
From raw data to clear decisions.
Most lead discovery workflows are messy. Users jump between Google, directories, review sites, spreadsheets, CRMs, and manual notes just to understand whether a business is worth contacting. LeadRx was designed around a simpler question: how can we turn scattered business signals into a clear, prioritized workflow?
01
The Data Problem
Businesses can have incomplete, duplicated, outdated, or inconsistent information across sources.
02
The Decision Problem
Users do not only need more leads. They need to know which leads are worth pursuing.
03
The Workflow Problem
Discovery, qualification, scoring, and outreach are usually disconnected.
User and use case
Designed for teams that need to find opportunity faster.
Primary Users
AgenciesSales teamsLocal service providersB2B growth teamsContractors and niche service businesses
User goal
Find businesses in a target area that show signs of opportunity, then prioritize outreach based on quality, urgency, and fit.
Pain Points
- Too many low-quality leads
- Manual search takes too long
- Duplicates and bad data create confusion
- It is hard to compare leads quickly
- No clear explanation for why one lead is better than another
- CRM handoff can be messy
Design principle
The interface should not make the user inspect every detail manually. It should surface the most important signals first, then let the user go deeper when needed.
The core workflow
The product workflow.
The workflow was designed to move users from search to decision without forcing them to interpret raw data manually.
Workflow Diagram
End-to-end map: search → signals → cleanup → evaluation → AI scoring → review.
UX challenge
The challenge was not more data. It was better prioritization.
Problem statement
Lead discovery tools often overwhelm users with tables, filters, and raw records. That creates a false sense of productivity while still leaving the user unsure which business to contact first.
Design goal
Create an experience where every lead has context:
- What is this business?
- Why is it relevant?
- What signals matter?
- How confident is the recommendation?
- What should the user do next?
“Good AI UX should not only provide an answer. It should explain why the answer is useful.”
Wireframe story
Wireframing the decision path.
Drag through the four wireframes that defined the workflow: configuration → live results → enrichment → action.
F2
F2 · Frame
Live Search Results
Split map and live feed view. Hot leads surface on the map and rise to the top of the list as scanning runs.
F3
F3 · Frame
Enrichment & AI Scoring
Pipeline of website checks, social signals, duplicate detection, and AI scoring with HOT/WARM badges per lead.
F4
F4 · Frame
Qualified Action Layer
Toolbar that turns a qualified set into outreach: generate copy for hot leads, view, enrich, or find emails.
Drag or scroll4 frames
Final product experience
A dashboard built around clarity, confidence, and action.
Product surfaces · live captures
The same workflow story, rendered across three product entry points.
Each surface frames the same idea: a lead moves through research, scoring, and outreach in one continuous loop. The platform view shows the live automation; the agencies view shows the same loop scoped to a multi-client workflow; the sales-team view focuses on territory coverage and ready-to-work hot leads.
Platform · live automation
Agencies · client campaigns
Sales · territory coverage
AI Score with Reasoning
Each lead can be scored and explained so the user understands why it matters.
Signal-Based Filtering
Users can filter by website status, reviews, location, category, digital presence, and quality flags.
Lead Detail Drawer
A detail-first pattern keeps users in context without forcing page reloads or workflow breaks.
Quality Flags
Important issues like missing websites, weak digital presence, duplicate data, or low confidence can be surfaced clearly.
Operational States
The design accounts for loading, empty results, failed API cells, warnings, and rate-limit style issues.
CRM-Ready Actions
The interface is designed to eventually support export, assignment, outreach, and CRM sync.
AI Score Panel
Score with plain-language reasoning, signal list, and confidence.
Mobile View
Stacked lead cards with score, signals, and quick actions.
AI design layer
Where AI improves the user experience.
AI was designed to support
- Lead prioritization
- Plain-language reasoning
- Pattern detection
- Opportunity explanation
- Suggested outreach direction
- Faster review of large lead sets
Guiding principle
AI should reduce ambiguity, not create blind trust.
Every AI surface in the product had to be paired with an explanation, a signal list, or a clear way to disagree.
01
AI as a Scoring Layer
AI helps prioritize which leads deserve attention first.
02
AI as an Explanation Layer
Users should see why a lead was scored highly, not just receive a number.
03
AI as a Workflow Layer
AI can recommend next actions, outreach angles, and CRM notes.
“The goal was not to make AI feel magical. The goal was to make decision-making feel faster and clearer.”
Design decisions
Key design decisions.
01
Summary before detail
Users see high-level score, location, and opportunity signals before deeper data.
02
Explainability over black-box AI
AI reasoning is shown in plain language to build trust.
03
Drawer-based review
Users can inspect leads without losing the results list or map context.
04
Filters around real decisions
Filters are based on how users qualify leads, not just database fields.
05
Warnings instead of silent failure
When data is incomplete or an API source fails, the UI should communicate that clearly.
06
Action-first dashboard
Every screen should help the user move toward save, export, outreach, or decision.
Before and after
Before: scattered research. After: guided intelligence.
Before
- Manual Google searches
- Spreadsheet tracking
- Duplicate businesses
- No consistent lead quality score
- Hard to know who to contact first
- No explanation layer
- Disconnected outreach workflow
After
- Guided search setup
- Clean dashboard
- AI-assisted scoring
- Lead quality signals
- Explanation and confidence layer
- Detail drawer for review
- Action-ready workflow
Relevance to complex data products
Why this matters for AI-assisted product design.
LeadRx is not just a lead generation interface. It is a design system for turning noisy data into structured decisions. That same product design challenge applies to any industry where users need to understand complex information quickly, trust the system, and take action.
This type of workflow thinking is especially relevant for products that rely on
APIsLive dataData visualizationAutomated content generationHuman reviewDecision supportMulti-platform output
Reflection
What I would carry forward.
The biggest lesson from LeadRx is that AI is most valuable when it improves the user’s decision path. A product should not simply generate more information. It should help users understand what changed, why it matters, how confident the system is, and what they should do next.
AI should accelerate workflow, not replace judgment.
Trust comes from clarity, explainability, and transparent system states.
The best dashboards turn complexity into confident action.
Designed for complexity
Designed to make complexity actionable.
LeadRx Insights represents the kind of product design work I enjoy most: complex systems, AI-assisted workflows, strong visual hierarchy, and interfaces that help users make better decisions faster.