- Remove the delete-this-class class from the navbar15_component div. This will change the positioning of the navbar to fixed.
- Add the navbar-on-page class to the page-wrapper class. This will ensure that the navbar is centered on the page.
- Add the max-width-full class to the main-wrapper class. This will ensure all sections inside of the main wrapper are full-width.
Available for select product teams
Louie Sakoda
The latest model is an AI product designer who can think through the system, design the experience, and build the working product.
*Add to cart for pricing
Description
Most recently, I built Offboard, an AI-native career-transition platform using React, TypeScript, Supabase, Deno edge functions, and multi-model LLM workflows. Before that, I spent nine years at CK-12 designing AI tutoring and learning products used at scale.
Product Specs
- Solo builder of a full-stack AI product using React, TypeScript, Supabase, Deno edge functions, and LLM-assisted development
- 9 years designing K-12 learning products, including AI tutoring and teacher-facing tools used at scale
- Strongest in ambiguous product spaces where AI workflows, user trust, and hands-on prototyping all matter
- Experienced across product design, UX architecture, front-end implementation, design systems, and human-in-the-loop AI
Skills
Ask Louie AI
Skills
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Work
Check out some of my work below
Offboard - Jobseeker OS Application
I designed and built OffboardOS, an AI-native job-search operating system that helps laid-off tech workers move from overwhelm to action with guided workflows, resume tools, interview prep, and personalized AI support.
Designing Offboard, a Full-Stack AI Job Search OS
Designing Offboard, a Full-Stack AI Job Search OS
Offboard started from a pretty simple observation: Job seekers do not need another disconnected tool. They need one place where the work around a role can stay connected.
I personally designed and built Offboard as an AI-native career-transition platform for people navigating layoffs and job searches. The product helps users turn messy inputs like job URLs, resumes, company research, and interview notes into structured next steps.
The main workflow is the Job Packet. A user can paste in a job URL and Offboard builds a role-centered workspace around it. From there, the system can parse the job, check for ghost-job signals, save the application, generate company research, analyze fit, tailor a resume, draft a cover letter, and move the packet into review.
My role covered the full product surface, from product strategy and UX architecture to AI workflow design, front-end implementation, Supabase architecture, QA, and iteration.
Built with React, TypeScript, Vite, Tailwind, shadcn/ui, Supabase, Postgres/RLS, Deno edge functions, Stripe, Resend, OpenAI, Anthropic, ElevenLabs, Firecrawl, Claude Code, Codex, and Lovable.
Project: Offboard
Product type: AI-native career transition platform
Audience: Laid-off and actively searching professionals, with adjacent employer-sponsored outplacement workflows
My role: Solo product designer and technical builder
Work: Product strategy, UX architecture, AI workflow design, interface design, front-end implementation, Supabase architecture, QA, and iteration
Stack: React, TypeScript, Supabase, Deno edge functions, OpenAI, Anthropic, ElevenLabs, Firecrawl, Claude Code, Codex
Thesis: A job search should not be a pile of disconnected tools. Every role should become a connected workspace with context, artifacts, decisions, and next steps.
The Problem
Job search creates two kinds of pressure at once.
The first is practical. People have to track roles, read job descriptions, judge whether a posting is worth their time, tailor materials, prepare for interviews, follow up with contacts, and remember what happened across dozens of moving pieces.
The second is emotional. They are often doing that work while stressed, uncertain, and isolated.
Most job-search tools only solve one slice of the workflow. A resume builder helps with a resume. A tracker stores applications. A job board shows roles. Notes apps hold interview prep. Chatbots can generate advice.
The problem is that the job seeker still has to hold the whole system together.
That fragmentation creates a hidden tax. Users have to remember where everything lives, decide what to do next, and translate context from one tool to another while they are already under pressure.
The design challenge was not just to add AI to job search. It was to make the search feel less scattered.
Product Thesis
Offboard is designed around a simple idea.
Every meaningful job-search action should compound into better context for the next one.
If a user uploads a resume, that context should improve role match, resume tailoring, and interview prep. If they paste a job URL, the system should be able to parse the role, check whether the posting looks risky, save the application, generate company research, create tailored materials, and keep everything attached to the same opportunity.
The product centers on four connected layers:
- The user context: Profile, resume, goals, preferences, network, conversations, and allowed personalization signals
- The role/company context: Job posting, company research, ghost-job signals, role match, and application status
- The generated artifacts: Tailored resume, cover letter, interview prep, notes, and follow-up actions
- The guidance layer: LUMO, a context-aware AI assistant that can suggest actions, explain next steps, and ask for confirmation before changing user data
The goal was to move the experience from “use five tools and stitch the results together” to “start from one opportunity and build the right packet of support around it.”
My Role
I personally designed and built the product experience end to end.
My work included product strategy, UX architecture, AI workflow design, interface design, front-end implementation, Supabase-backed product logic, Edge Functions, QA, and iteration.
Key contributions included:
- Defined the product strategy and operating-system framing
- Mapped the job-search workflow from setup to application execution to interview prep
- Designed the core UX architecture across dashboard, applications, job packets, resume tailoring, ghost-job checks, LUMO, and interview prep
- Designed AI workflows around user control, confirmation-first actions, and risk gates
- Built front-end flows and integrated them with Supabase-backed product logic and Edge Functions
- Shaped the brand and interface direction around calm, practical, emotionally aware execution
- Used LLM-assisted development to prototype, validate, and iterate across a broad product surface
Key Workflow 1: Job Packet as the role-centered workspace
The Job Packet is the clearest expression of the product thesis.
A user can paste a job URL or job description, and Offboard builds a role-specific workspace around it. Instead of creating disconnected outputs, the system keeps the role, company, application status, generated materials, and next steps tied together.
The workflow can:
- Parse the job
- Run a ghost-job check
- Pause at a risk gate if the posting looks risky
- Save or reuse the application
- Generate company intel
- Analyze role match
- Path-to-a-Person search
- Find or request a resume
- Create a tailored resume session
- Draft a cover letter
- Move the packet into review
The important design decision was the risk gate.
If a posting looks questionable, the system pauses before asking the user to spend more time tailoring materials. That moment matters because job seekers do not just need more automation. They need help deciding where their energy is worth spending.
The Job Packet also gives the product a durable organizing object. Each role becomes a workspace with its own context, artifacts, and status instead of another loose item in a tracker.
Key Workflow 2: LUMO as a context-aware guidance layer
LUMO is the AI layer that sits across the product.
The goal was not to create a generic chat surface. LUMO needed to understand the user’s real search context, including profile, applications, interviews, network, reflections, saved chat memory, and any personalization signals the user allows.
LUMO can answer questions, suggest relevant tools, and propose actions like creating or updating an application or saving a contact. For actions that modify user data, the interface uses confirmation-first cards so the user can see exactly what will happen before approving it.
The key design decision was to keep context and control central to the AI experience.
The more the system understands about the user’s search, the more important it becomes to show what it knows, ask before changing data, and keep suggested actions grounded in existing workflows.
LUMO should feel less like a chatbot bolted onto the product and more like a guidance layer that helps the user move through the system with less friction.
Design Principles
1. Calm before clever
The product is for people who may be anxious, tired, or overwhelmed. The interface needs to feel clear and steady. That shaped the language, density, workflow rhythm, short next steps, visible status, quiet hierarchy, and practical calls to action.
2. AI should preserve agency
The system can automate analysis, drafting, and organization, but it should not make irreversible decisions on the user’s behalf. Confirmation cards, review flows, and risk gates make automation feel more trustworthy.
3. The role is the anchor
Most job-search tools produce disconnected outputs. Offboard’s workflows keep artifacts tied to the same role and company context. This makes the product feel cumulative instead of transactional.
4. Context should compound
Every completed action should make the next action easier. A resume improves tailoring. A saved role improves interview prep. A ghost-job check informs whether to invest more time. LUMO becomes more useful as the workspace fills in.
5. Progress should feel operational, not performative
The dashboard uses checklist progress, pipeline status, packets, and upcoming events. These are practical signals, not artificial achievement loops.
Design decision: I made context and control central to the AI UX. The stronger the system gets at understanding the user’s search, the more important it becomes to show what it knows, ask before changing data, and keep suggested actions grounded in existing workflows.
Outcome
Offboard is now a working AI-native career-transition product with shipped surfaces for dashboard guidance, applications, resume tailoring, ghost-job analysis, Job Packets, LUMO guidance, interview prep, documents, network, community, financial runway, and employer-sponsored support.
What is publishable now:
- The product is more than a concept. It is a real React and Supabase application with working routed workflows.
- The Job Packet pipeline connects parsing, validation, application creation, company research, role match, resume tailoring, and cover letter generation.
- LUMO is designed as a context-aware guidance layer with confirmation-first actions.
- The core design strategy is grounded in reducing scattered job-search work into a role-centered operating system.
What I would improve next:
- Add stronger usage analytics around which workflow steps create the most momentum
- Tighten the handoff between LUMO suggestions and deeper product surfaces
- Add clearer empty states and recovery states for partial packet generation
- Continue improving the trust model around AI-generated recommendations and user-controlled edits
AI Legal Consultant
'We Are Not Lawyers' is an AI-native platform that turns confusing legal problems into clear, step-by-step actions through guided workflows, documents, and optional attorney handoff.
Lumo - Jobseeker Companion
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Project Overview
Flexi is an AI-powered digital tutor designed for CK-12 Foundation’s educational platform, offering personalized, on-demand academic support for K-12 students. By delivering explanations, adaptive practice, and encouragement, Flexi addresses critical learning gaps, particularly in remote or hybrid classroom settings.
- My Role: Senior Product Designer (User Research, UX/UI Design, Prototyping)
- Timeline: January 2024 – September 2024 (Beta), Public launch Q1 2025
- Team: 1 PM, 3 ML Engineers, 2 Curriculum Specialists, 1 Front-End Developer
Problem & Opportunity
The shift to remote learning revealed significant gaps in personalized student support:
- Only 50% of students could consistently focus during remote lessons, with just 41% feeling motivated (YouthTruth, 2022).
- Teachers experienced burnout from repetitive student queries, averaging 54-hour workweeks, with less than half dedicated to teaching (Education Week, 2022).
- Effective 1:1 tutoring remains prohibitively expensive despite proven efficacy, delivering +0.20 to 0.23 standard deviation improvements in math (NBER, 2022).
Opportunity: Build a scalable AI tutoring tool integrated into existing classroom workflows, offering affordable, trustworthy, always-available learning support.
User Research & Methodology
We employed a variety of rigorous research methodologies to ensure our solutions directly addressed real user needs:
- Student Interviews (Grades 6-11, n=18): Conducted qualitative 1-on-1 video interviews to deeply understand students' emotional responses, frustrations, and expectations when seeking help.
- Teacher Diary Studies (n=12, two-week duration): Teachers documented daily interactions, highlighting repetitive support tasks and workload impacts.
- Competitive Analysis: Systematic assessment of 5 leading AI tutoring products, evaluating usability, transparency, teacher integration, and trustworthiness.
Key findings:
- 67% of students reported frustration without immediate help.
- Teachers spent ~7 hours weekly addressing routine clarifications.
- Competitors lacked transparency, teacher oversight, and trust-building features.
Design & Prototyping
Based on research, we developed a conversational, supportive user interface emphasizing transparency and accessibility:
- Confidence Indicators: Real-time display of AI confidence levels, increasing trust and clarity.
- Interactive Learning Loop: Answer → Micro-quiz → Stretch-prompt, promoting deeper learning and engagement.
- Teacher Dashboard: Real-time analytics highlighting common student misconceptions, streamlining teacher interventions.
Through iterative Figma prototyping and usability testing (n=31), we achieved:
- 22% faster task completion compared to traditional worksheets.
- 92% student satisfaction rate, measured by willingness to use the tool again.
AI Integration & Design Decisions
Strategic AI design choices were grounded in evidence and aligned with user needs:
- Transparency & Trust: Displayed model confidence and source citations, crucial for sustained trust (Meta-review of Intelligent Tutoring Systems, 2023).
- Promoting Metacognition: Implemented "think-aloud" checkboxes, shown to boost engagement and retention during remote learning.
- Teacher Empowerment: Added teacher-controlled toggles (e.g., "Pause Flexi," "Rephrase Response"), reducing teacher workload and enhancing control.
Pilot Results & Impact
Flexi’s pilot launch demonstrated significant improvements across key metrics:
- User Engagement: Weekly active users hit 61% of the target cohort within eight weeks.
- Session Duration: Average session length more than doubled, from 5m 22s to 10m 48s.
- Learning Gains: Accuracy on follow-up tasks improved significantly, rising from 48% to 72% correct responses.
- Teacher Workload: Support requests decreased by 33% per student per term.
Flexi’s initial success projects substantial long-term educational benefits:
- Estimated to save approximately 1.9 teacher-hours per class weekly, equating to nearly $2,400 saved per teacher each semester.
- Achieved an estimated learning improvement of +0.18 standard deviations, comparable to traditional human tutoring at a fraction of the cost.
Post-launch, Flexi recorded over 500,000 student sessions within 90 days, solidifying its value to CK-12’s expansive user base.
Reflection & Future Vision
The Flexi project reinforced critical lessons in designing responsible and effective AI educational tools:
- Successful: Confidence indicators, micro-quizzes, and teacher empowerment controls resonated positively.
- Areas to Improve: Early avatar designs felt overly juvenile to older students; future iterations will embrace a more universally appealing aesthetic.
Looking ahead, we plan to expand Flexi's capabilities:
- Multilingual Support: Broadening global accessibility.
- Adaptive Content: Adjusting complexity based on individual reading levels.
- Enhanced Analytics: Providing district-wide insights through API integrations.
The World’s Most Powerful AI Tutor
AI-powered math and science tutor designed to help students reason through problems instead of simply receiving answers. I led UX across conversational flows, guided practice, teacher-facing tools, and learning workflows at CK-12.
Flexi - AI Student Tutor
Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Project Overview
Flexi is an AI-powered digital tutor designed for CK-12 Foundation’s educational platform, offering personalized, on-demand academic support for K-12 students. By delivering explanations, adaptive practice, and encouragement, Flexi addresses critical learning gaps, particularly in remote or hybrid classroom settings.
- My Role: Senior Product Designer (User Research, UX/UI Design, Prototyping)
- Timeline: January 2024 – September 2024 (Beta), Public launch Q1 2025
- Team: 1 PM, 3 ML Engineers, 2 Curriculum Specialists, 1 Front-End Developer
Problem & Opportunity
The shift to remote learning revealed significant gaps in personalized student support:
- Only 50% of students could consistently focus during remote lessons, with just 41% feeling motivated (YouthTruth, 2022).
- Teachers experienced burnout from repetitive student queries, averaging 54-hour workweeks, with less than half dedicated to teaching (Education Week, 2022).
- Effective 1:1 tutoring remains prohibitively expensive despite proven efficacy, delivering +0.20 to 0.23 standard deviation improvements in math (NBER, 2022).
Opportunity: Build a scalable AI tutoring tool integrated into existing classroom workflows, offering affordable, trustworthy, always-available learning support.
User Research & Methodology
We employed a variety of rigorous research methodologies to ensure our solutions directly addressed real user needs:
- Student Interviews (Grades 6-11, n=18): Conducted qualitative 1-on-1 video interviews to deeply understand students' emotional responses, frustrations, and expectations when seeking help.
- Teacher Diary Studies (n=12, two-week duration): Teachers documented daily interactions, highlighting repetitive support tasks and workload impacts.
- Competitive Analysis: Systematic assessment of 5 leading AI tutoring products, evaluating usability, transparency, teacher integration, and trustworthiness.
Key findings:
- 67% of students reported frustration without immediate help.
- Teachers spent ~7 hours weekly addressing routine clarifications.
- Competitors lacked transparency, teacher oversight, and trust-building features.
Design & Prototyping
Based on research, we developed a conversational, supportive user interface emphasizing transparency and accessibility:
- Confidence Indicators: Real-time display of AI confidence levels, increasing trust and clarity.
- Interactive Learning Loop: Answer → Micro-quiz → Stretch-prompt, promoting deeper learning and engagement.
- Teacher Dashboard: Real-time analytics highlighting common student misconceptions, streamlining teacher interventions.
Through iterative Figma prototyping and usability testing (n=31), we achieved:
- 22% faster task completion compared to traditional worksheets.
- 92% student satisfaction rate, measured by willingness to use the tool again.
AI Integration & Design Decisions
Strategic AI design choices were grounded in evidence and aligned with user needs:
- Transparency & Trust: Displayed model confidence and source citations, crucial for sustained trust (Meta-review of Intelligent Tutoring Systems, 2023).
- Promoting Metacognition: Implemented "think-aloud" checkboxes, shown to boost engagement and retention during remote learning.
- Teacher Empowerment: Added teacher-controlled toggles (e.g., "Pause Flexi," "Rephrase Response"), reducing teacher workload and enhancing control.
Pilot Results & Impact
Flexi’s pilot launch demonstrated significant improvements across key metrics:
- User Engagement: Weekly active users hit 61% of the target cohort within eight weeks.
- Session Duration: Average session length more than doubled, from 5m 22s to 10m 48s.
- Learning Gains: Accuracy on follow-up tasks improved significantly, rising from 48% to 72% correct responses.
- Teacher Workload: Support requests decreased by 33% per student per term.
Flexi’s initial success projects substantial long-term educational benefits:
- Estimated to save approximately 1.9 teacher-hours per class weekly, equating to nearly $2,400 saved per teacher each semester.
- Achieved an estimated learning improvement of +0.18 standard deviations, comparable to traditional human tutoring at a fraction of the cost.
Post-launch, Flexi recorded over 500,000 student sessions within 90 days, solidifying its value to CK-12’s expansive user base.
Reflection & Future Vision
The Flexi project reinforced critical lessons in designing responsible and effective AI educational tools:
- Successful: Confidence indicators, micro-quizzes, and teacher empowerment controls resonated positively.
- Areas to Improve: Early avatar designs felt overly juvenile to older students; future iterations will embrace a more universally appealing aesthetic.
Looking ahead, we plan to expand Flexi's capabilities:
- Multilingual Support: Broadening global accessibility.
- Adaptive Content: Adjusting complexity based on individual reading levels.
- Enhanced Analytics: Providing district-wide insights through API integrations.
