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Vbrain

An AI-powered orchestration platform for R&D

Co-founded Vbrain, which connects domain experts, AI agents, and structured playbooks in one workspace so R&D teams move faster, spend less, and ship with more confidence. I led discovery, MVP definition, and founder-led GTM to five enterprise pilots.

Co-Founder & Head of Marketing2025 – Present

ProductGTM

Outcomes

enterprise customer pilots
5
Within 3 months of launch
expert profiles
20K+
Human + expert layer
role-specific R&D agents
6
PI to Compliance, on shared project state
AI Acceleration program
VietLeap
Selected by Vietnam’s Ministry of Planning and Investment
Vbrain logo with the tagline “The AI-powered R&D partner: Faster R&D. Lower cost. Better outcomes.”

Product demo

Open in Google Drive
A walkthrough of the Vbrain platform. I created this demo to show prospects and pilot customers how it works.

Inside the platform

Vbrain’s Collaborative R&D OS architecture: R&D cognitive agents (PI, Scoping, Research & Retrieval, Matching, Validation, Compliance) working across five layers (project runtime, agent orchestration, composable skills, knowledge and memory, and human and expert review) on top of a scientific memory substrate linking problems, hypotheses, experts, experiments, decisions, and papers.
The Collaborative R&D OS: role-specific agents work against shared project state, experts validate their reasoning, and every project adds to a versioned scientific memory. Click to open full size.

Context

Vbrain is an early-stage enterprise AI startup building a collaborative R&D operating system: a project-scoped workspace where AI agents speed up research and analysis, domain experts provide judgment and validation, and structured playbooks keep the work consistent. As co-founder I lead product strategy, business model development, commercialization planning, and GTM execution.

Challenge

R&D teams stitch their work together from tools that each solve one piece. AI research tools such as Perplexity, NotebookLM, and Google Co-Scientist help with scoping and knowledge but don’t bring in experts. Expert networks such as GLG, AlphaSights, and Guidepoint supply expertise but nothing else. Collaboration platforms such as Notion, Confluence, Microsoft 365, and Slack hold the work but don’t reason about it. We needed to find which teams felt that gap most, define an MVP they could trust, and explain a complex multi-agent system as plain business value.

Approach & key decisions

  1. Start from enterprise discovery

    Step 1

    I led customer discovery and market research to find pain points in R&D workflows, prioritize target segments, and shape positioning, differentiation, and pricing.

  2. Position against the whole stack, not one tool

    Step 2

    I mapped AI research tools, expert networks, and collaboration platforms against four jobs: tailoring R&D scoping, accessing the right expertise, evolving institutional knowledge, and collaborating in one workspace. Each competitor covers one or two; Vbrain is built to cover all four.

  3. Keep experts in the loop

    Step 3

    I turned the product vision into an MVP roadmap for a multi-agent system with human-in-the-loop validation: role-specific agents (PI, Scoping, Research & Retrieval, Matching, Validation, Compliance) work against shared project state, and experts review and improve their output.

  4. Make every project make the system smarter

    Step 4

    We designed the product so projects persist for weeks or months, hypotheses link back to evidence, decisions keep their provenance, and finished projects produce reusable skills and playbooks.

  5. Explain value, not technology

    Step 5

    I created demos, sales presentations, website messaging, and webinars that frame the platform around three outcomes: faster (from ambiguous ideas to validated products), lower cost (fewer wasted resources), and better outcomes (innovation delivered with confidence).

Execution & collaboration

  • Ran founder-led sales, industry events, and educational webinars.
  • Created the product demo video used with prospects and pilot customers.
  • Built the competitive analysis and positioning across AI research tools, expert networks, and collaboration platforms.
  • Defined MVP requirements, user stories, and success criteria with the founding team.
  • Selected for the VietLeap AI Acceleration program by Vietnam’s Ministry of Planning and Investment.
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