Company, People, and Partnerships

Decision

Present Memova to ODMs as a Memory OS and agent-action system spanning five hardware entry points, not as a buyer seeking a quote for one commodity recorder.

Primary message:

One software system, five hardware entry points. Each device captures a different part of real-world context, while Memova turns that context into durable memory and user-confirmed action.

The five current consumer price directions are:

These are retail price directions, not target BOMs or final commitments.

Recommended Partnership Ask

Frame early support as strategic co-investment rather than asking bluntly for 1,000 free units:

• reduced or shared NRE;
• sample, tooling, or certification cost rebated against later POs;
• strategic-cost support for the first 500-1,000 units;
• milestone-based pricing, capacity, and engineering support;
• a reusable reference design that can expand to a second and third form factor.

Memova contributes product definition, Memory OS, knowledge base, agent workflows, software updates, global user testing, brand/GTM, and a multi-SKU roadmap. The ODM contributes hardware reference designs, acoustics, antennas, low power, thermal/structural/DFM, firmware interfaces, certification, pilot production, and scale manufacturing.

External-Claim Guardrails

• “World first” may be used only for Memova's specifically defined Personal Superalignment platform category framing. Do not broaden it into claims that Memova invented personal AI memory, LLM Wiki, knowledge bases, or AI alignment.
• The earlier US$1.5M financing target is historical planning language and must be refreshed against completed financing before reuse.
• Founder revenue history, team roles, and academic credentials require approved public wording.
• Medical, legal, and accessibility use cases are research/target markets, not clinical or professional efficacy claims.

Approved Team And Capital Presentation

• Present Sun Mingyu, Du Tianwei, Chen Chen, and Cheng Yang as four equally important core founding-team members with equal visual and narrative weight.
• Sun Mingyu's prior company reaching US$30M annual revenue in year three is founder track record, not Memova revenue.
• Company-confirmed completed investment/support: Silicon Valley USD fund and MiraclePlus.
• Current outreach/relationship development: Sequoia Capital and Andreessen Horowitz (a16z). These two must never be placed in the completed-investor area or described as invested.
• Capital and ecosystem content appears after the growth-plan section in the ODM artifact.

Chen Chen: Memova Data Science Lead Role Plan

Purpose of the Material

This is a first-person role pitch presented by Chen Chen, not a third-party talent evaluation. Its purpose is to explain his education and professional background, his understanding of Memova, the responsibilities of a data science lead, and an execution plan spanning the first 90 days through the following 12 months.

Narrative Structure

Pages 1–5 introduce Chen’s background through a timeline: a Stanford PhD in Civil and Environmental Engineering with a Computer Science machine-learning minor, followed by work at Twitter, PayPal, and OpenAI beginning in 2025. The academic section includes three papers in Building and Environment and one paper presented at IBPSA Building Simulation 2021. The professional section emphasizes experimentation, causal inference, prediction, and product decision-making.

Pages 6–7 connect Memova’s company positioning with the mission of the role. Memova turns personal Context into action that can be understood, aligned, and executed. The data science lead is responsible for building a reliable loop from Context to action.

Pages 8–10 present a three-layer data-science framework:

1. Personal Context Intelligence
2. Alignment and Workflow Policy
3. Learning, Evaluation, and Governance

Pages 11–15 map prior experience to the role, define deliverables for days 0–30, 31–60, and 61–90, and outline a platform roadmap for months 6–12.

Page 16 closes in the first person with four decisions that require confirmation from the team.

Key Decisions

• Remove all Meta and Instagram experience, metrics, and associated claims.
• Consolidate five fragmented technical modules into three high-level framework pages centered on understandable Context, decision policy, and a trustworthy learning loop.
• During the first 90 days, prove one real end-to-end use case before attempting to platformize data, models, experimentation, and governance.
• Preserve human authority in automation by setting distinct thresholds for recommendation, drafting, and high-impact execution, with confirmation, undo, audit, and rollback.

Final Artifact

The final deliverable contains 16 pages, primarily in Chinese, with institutional logos used as supporting visual elements. It follows the visual direction of the provided reference PDF and has completed page-by-page visual review, canvas-overflow checks, and file-integrity validation.

For the Company and People chapter, the important evidence is the role design: a data-science function that does not merely optimize model metrics, but connects personal Context, policy, evaluation, governance, and user correction into one accountable product loop.