An AI-powered recruiting platform — a Chrome extension that collects LinkedIn candidate profiles, paired with a web dashboard to manage projects, rank candidates instantly, and build a structured pipeline. Designed to replace hours of manual screening with a fast, systematic workflow.
Explore the live siteAs the sole designer and frontend engineer on a two-person startup team, I run a compressed loop where prototyping happens in code, not Figma — eliminating the handoff entirely and letting the backend ship as soon as requirements are clear.
Conducted user research and continuously collected real feedback from shipped V0 users — design decisions are grounded in actual usage, not assumptions.
Rapidly implemented functional frontend via vibe coding. The output is real, runnable code — not a static mockup — so there is no translation loss when handing off to the backend.
Internal testing with the full team, including a market partner who works in the recruiting industry — validation is grounded in real domain knowledge.
The backend engineer connects APIs directly to the already-built frontend. Speed is possible because the frontend is real code from day one.
Used Figma MCP to generate a Figma file from the live codebase, then systematically refined the UI — design decisions are grounded in what actually shipped.
Collect real user feedback, make targeted adjustments, and rapidly prototype the next version — the loop restarts from a position of live data.
ConnectNova is built for anyone who finds people on LinkedIn — but two user types define the core problem space.
“I can get 500 results from a LinkedIn search. The problem is I still have to open every single one.”
“Sales Navigator tells me who's out there. It doesn't tell me who's worth my time.”
Whether searching for a candidate or a prospect, the workflow is structurally identical: run a LinkedIn search, get hundreds of results, then open each profile one by one — reading, judging, copying notes into a spreadsheet. Repeat for every role or segment, every day.
The frustration wasn't just the time — it was the lack of structure. No way to systematically compare people, no record of who had been reviewed, and no connection between this session and the next one for the same goal.
The gap was the same for both users: nothing existed between "run a LinkedIn search" and "have a ranked, actionable shortlist — ready to reach."
Build a tool fast enough to ship in 6 weeks, simple enough for recruiters and sales teams to adopt without training — and structured enough to grow into a full Find → Rank → Reach platform, not just a one-trick ranking widget.
Before touching any design, I mapped how our two core users actually work today — tracing every step from the moment they open LinkedIn to the moment they reach out to someone.
The same gap surfaced in both workflows: LinkedIn surfaces people but provides no way to rank or prioritize them. Everything after "find" is handled by a disconnected tool — or not at all.
The original ask was straightforward: collect profiles from LinkedIn using the Chrome extension, describe what you're looking for, get an AI-ranked shortlist.
I pushed back and advocated for introducing the Project concept — a container that groups collected profiles under a specific search goal. Three reasons:
“This shouldn't just be a ranking tool. It should be a pipeline management platform.”
The early structure was the textbook three-layer model: Project list → Rank list → Rank detail.
But after studying actual usage patterns, one thing stood out: every time a recruiter opens the dashboard, 90% of the time they only care about the latest ranking for that role. Forcing one extra click to reach the thing they came for is friction with no payoff.
The team had six weeks to go from zero to a shippable MVP. Design had to move fast without fracturing.
I used Stitch to rapidly explore direction and lock in a token system — color, type, spacing, radius — then built every screen on top of it. The Chrome extension and the web dashboard ended up speaking the same visual language, and engineers had a clean variable reference to work from.
Two tightly coupled products — an extension that lives inside LinkedIn, and a dashboard that turns collected profiles into a ranked, manageable pipeline.
ConnectNova is made up of two tightly coupled products — a Chrome extension that lives inside LinkedIn, and a web dashboard for managing, ranking, and reviewing candidates.
I designed the extension as an in-context collection tool for LinkedIn search pages, focusing on clear page recognition, flexible collection controls, and calmer feedback during long-running collection tasks.
To balance transparency, trust, and control in AI products — and to keep the ranking process from feeling like a black box — I designed an evaluation criteria layer that users can review, adjust, and apply before generating a new ranking.
Every candidate the extension collects lands here. Four surfaces carry the day-to-day work — from overview to individual profile.
Every hiring need appears as a Project card. Status is visible at a glance — which are ranked, which still have unprocessed candidates.
31 candidates · 2 rankings · 20/04/2026
Live in USA, female
Currently resides in the United States
Identifies as female (based on profile indicators such as pronouns, name, or gender-specific organizations)
Samik is a Game Engineer currently based in Pittsburgh, Pennsylvania at Schell Games, with portfolio signals aligned to hands-on game programming and studio leadership.
Jordan ships gameplay systems at scale in Los Angeles, California with Riot Games; residency signal is strong while role-title match is mixed for this ranking.
Priya is a Graphics Engineer at Epic Games in Cary, North Carolina, with strong engine-side signals and credible senior ownership patterns.
Opening a project lands on the latest ranking, no extra hop. Each candidate ships with an AI score, a dimension breakdown, and the rationale behind it. History is a version-switch away.
31 candidates · 2 rankings · 20/04/2026
Candidate pool
All people collected for this project - ranked and not yet ranked.
Every candidate in the project — ranked or not — in one view. Search, tag, annotate. The foundation for pipeline management down the road.
Clicking a candidate slides in their full LinkedIn profile — work history, education, skills — alongside any notes the recruiter has added.
Senior Product Manager · Roadmaps, discovery, and shipping cross-team outcomes.
Product leader focused on roadmap execution, cross-functional alignment, and turning ambiguous problem spaces into measurable outcomes. Experienced in growth-stage and large-scale orgs.
Senior Product Manager
Product Manager
Lyft
Associate PM
Airbnb
Stanford University
MS, Computer Science
2014 – 2016
Ongoing exploration for recruiter outreach workflows. This prototype tests messaging loops and follow-up orchestration on top of the current platform architecture.
A few honest notes on what worked, what I'd rework, and where the product is headed from here.
Prototyping in real code meant zero translation between design and engineering. What got designed got shipped — no handoff gap, no fidelity loss.
Used Figma MCP to generate specs from the live codebase. Design documentation caught up to the product — not the other way around.
Tracing both users' end-to-end journeys — before any interface decisions — made the shared gap obvious. The problem defined itself once the workflow was visible.
Recruiters and sales reps share one core JTBD: find and prioritize people on LinkedIn. Recognizing this let us design one platform instead of two separate products.
One expert partner gave us speed and depth. But a single perspective has blind spots. The tradeoff was velocity over breadth.
Project wasn't in the original spec. Advocating for it changed the platform from a ranking widget into the foundation for a full pipeline product.
A token-based design system established early meant every screen felt coherent at launch — not polished later, but right from the start.
V0 is live. Next priority: structured testing with real users to validate the two-layer IA and the Project concept.
Roadmap: outreach automation, pipeline tracking, and team collaboration — all of which the current architecture was designed to support.
Live and in production · Used by real recruiting teams
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