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Adrian Leung Team Lead RSVP Approved
Product Manager at Votee
Adrian Leung (Lead — solo team)
Designed, scoped, and built the entire stack end-to-end:
• Product & scope — ran two rounds of Socratic deep-interview with Claude
Opus to crystallize 3 generative-UI skills, 6 use cases, and a 2.5-hour
build budget. Authored the 8-doc PRD pack in docs/gtm-intelligence/
(PRD, Use Cases, User Scenarios, System Architecture, ADRs,
Implementation Plan, Demo Script, Risk & Plan B).
• Frontend (Next.js 14 + Tailwind + CopilotKit) — built the canvas with
three useFrontendTool handlers (render_quadrant_map / render_pitch_card /
render_data_gap), the SessionSidebar with star + filter, html2canvas PNG
export, and the Supabase-backed session resume flow.
• Agent (Python LangGraph + Claude Sonnet) — wrote the GTM system prompt
with explicit anti-hallucination routing, tool registry, and the live
Beever Atlas MCP client wrapper (search-wiki + ask-atlas via fastmcp).
• MCP layer (TypeScript, mcp-use) — implemented the two local tools
(get_product_specs, get_pr_records) with Zod schemas + mock JSON.
• Live Atlas integration — pre-seeded the Beever Atlas docker stack
(MongoDB + Neo4j + Weaviate) with structured competitor docs in
Phase -1 (night-before prep), wrote the parser that converts free-form
Atlas content into the quadrant render schema.
• Persistence (Supabase) — schema design (sessions, messages, artifacts),
anonymous-user UUID flow via localStorage, RLS-disabled hackathon mode.
• Pitch deck — built the 16-slide HTML deck under
docs/gtm-intelligence/slides/ in white & blue theme, including the
vibe-coding journey timeline.
I am a Product Manager at Votee AI with a foundation in Product Design from global firms like EY and Viu. Our coverage, my work focuses on driving product-market fit for AI solutions, spanning enterprise agentic systems. I specialize in bridging the gap between complex AI engineering and user-centric design to solve real-world problems. I am passionate about go-to-market strategies that empower cross-functional teams to deliver research-driven, impactful products that scale.
How can we use AI to move beyond static components toward interfaces that adapt to user intent in real-time? I’m keen to connect with AI researchers, product designers, and full-stack builders who are interested in leveraging LLMs for automated code generation, dynamic frontend building, and trying to break through the barrier of AI engineering, Product Design, and business development.
- Task-based Agent(Skills, Planner, Executor, Memory)
- LLM Wiki(Channel-based Memory for enterprise knowledge base)
- AI Agent chatbot projects
- AI agentic compliance checking solution