Work

‍

Project: Offboard
‍
Product type: AI-native career transition platform
‍
Audience: Laid-off and actively searching professionals, with adjacent employer-sponsored outplacement workflows
‍
My role: Founder, product strategy, UX architecture, AI workflow design, interface design, frontend implementation, brand/product direction, rapid prototyping
‍
Status: Working product / beta. Final public launch language should be verified before publishing.
‍
Stack: React, TypeScript, Vite, Tailwind, shadcn/ui, Supabase, Edge Functions, Stripe, Resend, LLM model integrations
‍
Core product thesis: Job seekers do not need another isolated job-search tool. They need a system that helps them decide what to do next and keeps every action, artifact, and decision tied to the same role/company context.

The Problem

‍

Losing a job creates two problems at once.

‍

The first is practical: people suddenly need to update resumes, search for roles, validate postings, track applications, prepare for interviews, manage follow-ups, and understand their financial runway.

‍

The second is emotional: they are doing all of that while stressed, uncertain, and often isolated.

‍

Most tools only solve a slice of the workflow. A resume builder helps with a resume. A tracker stores applications. A job board shows roles. A spreadsheet can hold status. Notes apps hold interview prep. Chatbots can generate generic advice.

‍

That fragmentation creates a hidden tax. The job seeker has to remember where everything lives, decide what to do next, and translate context from one tool to another while 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 as a career-transition operating system.

‍

The system starts from a simple idea: every useful job-search action should compound into better context for the next action.

‍

If a user uploads a resume, that context should help tailor materials. If they paste a job URL, the system should parse the role, check whether the posting is worth time, save the opportunity, generate role-specific materials, and keep those artifacts attached to the application. If they schedule an interview, preparation should build from the same resume, company, role, and application history.

‍

The product therefore centers on a few connected objects:

‍

• The user context: profile, resume, goals, preferences, network, conversations, and allowed personalization signals
• The role/company context: job posting, company intel, risk 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 layer 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 led the product and design direction end to end:

‍

• 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, context visibility, and risk gates
• Built frontend flows and integrated with Supabase-backed product logic and Edge Functions
• Shaped the brand and interface direction around calm, practical, emotionally aware execution
• Used rapid AI-assisted development to prototype, validate, and iterate across a broad product surface

Key Workflow 1: Job Packet As The Role-Centered Organizing Object

‍

The Job Packet workflow is the clearest expression of the product thesis.

‍

A user can paste a job URL or job description. Offboard then builds a role-specific packet through a connected pipeline:

1. Parse the job
2. Run a ghost-job check
3. Pause at a risk gate if the posting looks risky
4. Save or reuse the application
5. Generate company intel
6. Analyze role match
7. Find or request a resume
8. Create a tailored resume session
9. Draft a cover letter
10. Move the packet into review

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 should understand the user's real search context: profile, applications, interviews, network, reflections the user allows for personalization, and saved chat memory.

‍

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 sees exactly what will happen before approving it.

‍

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.

Design Principles

‍

1. Calm beats 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, and 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 are designed to 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 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 has a real React/Supabase application surface with working routed workflows.
• The Job Packet pipeline connects parsing, validation, application creation, company intel, 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.