Zunou
Japan-Led GTM Strategy
Internal — for review Marco Avila · 2026-05-11 · v1

Zunou works inside.
Now we make it work outside.

The product is alive because the density is there — every meeting on the same calendar, every chat in the same workspace, every decision routed back to the right person. The AI is useful because it has context. Take the context away and the AI is a chat box.

The 12-month question, then, is not "how do we sell an AI Chief of Staff" — it is "how do we manufacture that density inside small, defined groups in our home market, fast enough to matter, before the global incumbents close the window?"

Jump to the 17 decisions Read the story
¥30M ARR
Aspirational KGI
Within 12 mo of synchronized launch
4 + 4,000
Communities × TAI alone
Synchronized launch — sympathetic detonation
~6 months
To stop-or-go decision
Pre-committed PMF stage-gate

Unfamiliar with KGI KGI 重要目標達成指標 Key Goal Indicator — Japanese-standard top-level board commitment. The single number the company is held to. , NSM NSM North Star Metric — the single product metric that proxies for healthy growth. Tracks weekly. , PMF PMF Product-Market Fit — the moment the product visibly pulls users in rather than being pushed at them. , MCP MCP Model Context Protocol — open standard for AI systems to read/write external tools and data (Slack, Notion, Linear, etc.). Industry-default since early 2026. , Ringi Ringi 稟議 Japanese consensus-based written approval process. Documents circulate bottom-up through hierarchy. , Keigo Keigo 敬語 Japanese honorific speech. Required for any AI output that becomes external-facing. , or APPI APPI Act on the Protection of Personal Information — Japan's primary data privacy law. 2025–26 enforcement-focused regime. ? Hover any underlined term for an inline definition, or jump to the full glossary.

00 · Background

Zunou is an AI Chief of Staff company, based in Japan.

The opening: the AI productivity stack is fragmenting, not consolidating. Personal-tier exec assistants — alfred_, Klaio, Alyna — solve email + scheduling for one person, not teams. AI workspace tools — Glean, Mem, Lindy — solve search and agents for English-speaking enterprise. Meeting tools — Otter, Fireflies, Granola — transcribe but don't act. The category's most-funded team-focused AI Chief of Staff, Xembly, shut down in 2024. The platform incumbents — Notion AI, Slack AI, Microsoft Copilot — have each shipped AI surfaces bounded to their own walled gardens.

What no one ships today: a unified workspace — chat that rivals Slack + task management + AI native — that lands via integrations rather than asking teams to migrate cold, AND speaks fluent Japanese ( Keigo Keigo 敬語 Japanese honorific speech. Required for any AI output that becomes external-facing. , Ringi Ringi 稟議 Japanese consensus-based written approval process. Documents circulate bottom-up through hierarchy. ). That's the gap Zunou is positioned to close — provided we ship + distribute before any incumbent decides to unify their own stack. The window isn't a fixed countdown; it closes when an incumbent breaks out of its walled garden.

The proposal you're about to read answers one question: how do we manufacture density inside small Tokyo communities, fast enough to matter, while that window is still open?

Zunou's site has two use cases · neither is exactly the launch target

zunou.ai use case · 1 Not enough alone

For Founders

"Routine tasks are now automated."

Intelligent agents run in the background — route tasks, approvals, follow-ups. Approve / review only when human input is needed.

Founders alone don't give us critical mass. Tokyo has too few of them, and they're too spread across companies to hit the magic-number density threshold by themselves. The launch goes broader — see resolution below.
zunou.ai use case · 2 Too early

For Enterprises

"Strategic decisions are now real-time."

Live exec dashboard — progress, workload, and outcomes across teams, projects, priorities. No more chasing updates.

Out of scope until the platform is proven. Enterprises buy validated tools, not waitlist betas. They need paying logos, operational maturity, APPI APPI Act on the Protection of Personal Information — Japan's primary data privacy law. 2025–26 enforcement-focused regime. compliance posture, referenceable customers. Realistic open: Phase 5a (post-stage-gate). Pre-building advisor relationships earlier is fine — selling is not.
What the launch actually targets

Tokyo's broader builder + operator + founder community.

The four launch communities cover that whole population — not just founders. TAI (4,000+ AI engineers / researchers / PMs — mostly builders) · AI Tinkerers Ginza (~200 builders shipping in production) · Tokyo Founders (~150 operator-founders) · Venture Café Toranomon (the mix, every Thursday). The magic-number mechanic applies wherever there's a small team + many meetings + decisions to track — which is true across the segment, not just at the founder layer.

01 What this is

A proposal, not a verdict.

Every number here is a hypothesis. Every choice a defensible best-guess. If you finish reading and have no objections, we haven't written it well enough.

What this is

  • Evidence-grounded

    Every numerical claim is sourced and verifiable. The References section at the end is exhaustive.

  • Stop-or-go-able

    A pre-committed PMF PMF Product-Market Fit — the moment the product visibly pulls users in rather than being pushed at them. stage-gate at ~6 months. Three explicit outcomes, agreed in advance.

  • Instrumented week one

    The KPI KPI Key Performance Indicator — leading metrics we manage weekly. They roll up to NSM, which rolls up to KGI. tree is the implementation contract. If we can't measure it, we don't claim it.

What this isn't

  • A final answer

    Strategy is a living document. We refit the magic number monthly against real cohort data.

  • A request for consensus

    Explicit objections are more useful than reluctant nods. Use the vote chips on each decision.

  • A sales pitch

    This is internal alignment. The external-facing version comes after the 17 decisions are ratified.

02 Three things changed

The window is open right now. It won't be in 12 months.

A category leader exited. Japan's adoption tipped past 30%. Every productivity app is racing to embed agents.

Xembly
US category leader exited
June 2024 — $20M raised, customers Salesforce / Qualtrics / Twilio.
Source: GeekWire
34.6%
Japanese firms now use generative AI
March 2026 · Yomiuri Shimbun / Teikoku Databank · n = 23,349 firms · +14.2% considering.
Source: BigGo Finance summary
40%
Of enterprise apps will embed AI agents by EOY 2026
Up from < 5% in 2025.
Source: Gartner press release

The counter-pressure: Gartner reports 88% of AI agent pilots fail to graduate to production — evaluation gaps (64%), governance (57%), reliability (51%). Most buyers have been burned. Our positioning is the response to that wound: not another agent pilot — the morning briefing your team will actually use tomorrow.

03 The architectural strategy

MCP-native. Land in their stack while it gets replaced.

Zunou is a full workspace — chat that rivals Slack, task management, AI assistant. People won't migrate from their existing tools overnight. MCP is the bridge.

10,000+
Public MCP servers (Mar 2026)
Slack · Notion · Linear · Salesforce · GitHub · Google Workspace · M365 · Stripe · Sentry · Vercel · Supabase · Figma.
Source: DigitalApplied adoption stats
4,750%
MCP SDK download growth in 16 months
2M / month at launch (Nov 2024) → 97M / month (Mar 2026).
Source: Pento — Year of MCP
92%
Of new agent frameworks ship with MCP built-in
LangGraph · CrewAI · AutoGen + OpenAI / Microsoft / Google all on board.
Source: The New Stack — Why MCP won

What Zunou actually competes with: Slack at the chat layer · Notion / Linear at the task layer · alfred_ / Klaio at the personal-AI layer · Otter / Fireflies at the meeting layer. Zunou does what all of these do, in one place, with AI native. That's the end state.

The starting state is different. Most teams already have years of context in Slack, Notion, Asana, email, calendar. Telling them "switch to Zunou" on day one loses every conversation. They won't migrate overnight — and we shouldn't ask them to.

MCP solves this. We adopt the protocol as a host and inherit the entire 10,000+ server ecosystem on day one. Day-1 users keep their existing tools and use Zunou's AI on top of them. Day-90 users find themselves opening Zunou first because the context is already there. Day-365 users use Slack only for external comms — because internal happened in Zunou.

Land · day 1

Integrate, don't ask

Connect Slack + Notion + calendar + email via MCP. Zunou's AI works across them immediately. Zero migration friction. The user keeps every habit they have.

Compound · day 30–90

Context lives here now

Decisions, action items, follow-ups all surface in Zunou. The morning brief becomes the first surface opened. Slack becomes the second.

Expand · day 180+

Zunou is the workspace

Internal chat happens in Zunou. Tasks live in Zunou. Slack stays for external comms; Notion becomes the public-doc archive. The team's center of gravity has shifted.

MCP is the bridge — not the moat. The moat is the unified workspace that compounds once we're landed.

Same posture · the AI layer itself

Model-agnostic by design.

Zunou is not built on one provider. The product routes between Anthropic (Claude), OpenAI (GPT), Google (Gemini) — and others as they prove competitive — based on cost-per-task, latency, and quality benchmarks. Cheap models handle high-volume work (summarisation, classification); premium models handle heavy reasoning. The user never sees which one ran their query.

Why agnostic?
One provider outage doesn't down the product. We're not locked to a roadmap we can't see.
Why this matters for cost
Routing per task can cut inference spend 40–70% vs single-provider — material at the scale Zunou is targeting.
Why this matters for Japan
Future-proofs against bringing JP-domestic providers (Sakana, Rakuten 7B, ELYZA) into the stack for sovereign-data customers.
04 The category right now

No one builds exactly Zunou's product. Many build pieces of it.

Honest audit: who's a direct competitor, who's adjacent, who's a platform threat. Not everyone with 'AI' in their tagline is in our lane.

Lane / Competitor What it actually does Threat to Zunou
Direct · team-focused AI Chief of Staff
Xembly ↗
US · $20M raised
 Was the closest direct competitor: meeting recording + action items + follow-ups for teams. Shut down June 2024 None — exited the market. Cautionary tale, not a threat.
Klaio ↗
EU · early
 "AI Chief of Staff" branded; chat-style assistant for ops + tasks. Small team, individual + small-team tier. Low — no JP presence, no MCP-native land-and-expand story, no full workspace surface.
Alyna ↗
US · early
 AI productivity assistant marketed as "AI Chief of Staff" for individuals + small teams. Low — individual-first, no team workspace, no JP.
Adjacent · personal exec assistant (1 user, email + calendar)
alfred_ ↗
$24.99/mo · individual
 Email triage + scheduling + voice-matched drafts for one executive. Personal CoS. Low — single-user tool. Different category. Doesn't address team density.
Adjacent · AI workspace tools (search + agents, enterprise lean)
Glean ↗
$7B+ valuation
 Enterprise AI work assistant — unified search across all company apps, custom agents. Sells top-down to large enterprises. Medium long-term — but they sell to 1,000+ seat enterprises through procurement. Different motion. No JP focus.
Mem ↗
Personal-first AI
 AI-native note-taking and knowledge management. Personal use evolving to teams. Low — knowledge-base oriented, not workflow/operations.
Lindy ↗
Agent builder
 AI agent platform — users build custom agents for email, calendar, follow-ups. DIY composability. Low — power-user tool, not a turnkey workspace. Different ICP.
Adjacent · meeting AI (transcribe + note, don't act)
Otter ↗ · Fireflies ↗ · Granola ↗
Mature category
 Meeting transcription + AI summaries. Granola is the most loved by execs (notepad-style, no bot in room). Medium — Granola in particular is a feature competitor for Zunou's meeting-AI surface. We compete by going broader (chat + tasks + AI in one).
Platform incumbents · the long-run threat
Slack AI ↗
Salesforce · APAC +19% YoY
 AI summaries + search inside Slack. Distribution = every Slack workspace. High long-run — but bounded to Slack. Can't see calendar / Notion / Linear. No Keigo Keigo 敬語 Japanese honorific speech. Required for any AI output that becomes external-facing. / Ringi Ringi 稟議 Japanese consensus-based written approval process. Documents circulate bottom-up through hierarchy. nuance.
Notion AI ↗
JP language ✓
 AI inside Notion — write, summarise, find. Distribution = every Notion workspace. High long-run · bounded to Notion. Limited cross-app reach.
MS Copilot ↗
M365 + Teams
 AI woven through M365 + Teams. Strong in JP enterprise on the Microsoft stack. High long-run for the Microsoft-stack share of JP enterprise. Limited reach on Slack-native + Google-native teams.

Notably not in this table: Ashley AI (askashley.com) — a retail/customer-service conversational AI, not exec ops; different category entirely. Other "AI Chief of Staff" branded tools that emerged in 2023 have either pivoted to consumer or gone quiet. Audit refreshed monthly; flag additions to Marco if a new entrant launches in this space.

Japanese AI companies = partners, not competitors

Sakana AI ($135M Series B, Nov 2025), LayerX ($100M Series B, Sep 2025), ELYZA (KDDI-backed), Rakuten AI 3.0 — all sell foundation models or back-office automation. None compete with the exec CoS surface. The right move is co-marketing (joint PR Times release, joint AiSalon Tokyo demo) — not competing.

What we can credibly own

The category is competitive but no one has built exactly Zunou's product: a unified workspace that lands via MCP integrations before asking for migration. Defensibility comes from three things the incumbents have weak incentives to build: the land-and-expand strategy via cross-app integrations (see §03), Japanese-localized affordances like Keigo Keigo 敬語 Japanese honorific speech. Required for any AI output that becomes external-facing. and Ringi Ringi 稟議 Japanese consensus-based written approval process. Documents circulate bottom-up through hierarchy. , and the community-distributed habit loop. The window stays open as long as no incumbent decides to break its walled garden.

05 The mechanic

Density manufactures product-market fit.

Not features. Not virality. Density — the threshold past which a small group's behavior changes.

Precedent · the threshold pattern

Slack
~2,000 team messages

Past this threshold, retention jumps to 93%. Below it, teams churn. The product itself doesn't change — the team's behavior does.

Facebook
7 friends in 10 days

Chamath Palihapitiya's growth team identified this exact number. Cross it and the user retained for life. Miss it and they churned. Every product roadmap decision was filtered through it.

Zunou's hypothesis · derived from how the product creates value

5
Colleagues from the same community, active
1
Connected calendar
3
AI actions accepted within 14 days

Why these numbers — the derivation

5
Colleagues from the same community. Zunou's value compounds when cross-context exists — when your AI knows what others in your circle decided this week. Below ~5, the AI has thin context and reads as a chat box. At 5+, the cross-references start producing insights you couldn't get elsewhere. Why not 3 or 10: Slack's network-effect studies cluster around 4-7 as the activation band; we set 5 as a defensible mid-point to refit.
1
Calendar connected. Without the calendar, Zunou can't surface meetings, prep, decisions, or follow-ups — and 80%+ of the product surface is dark. Calendar is the single integration that unlocks daily utility. Why exactly 1: empirical — every PLG study on calendar-adjacent products (Cron, Calendly, Reclaim) ties activation to first OAuth connection. There's no fractional version.
3
AI actions accepted. One accepted action is a fluke. Two is a coincidence. Three within 14 days is a habit. Acceptance (rather than impression / view) is what tells us the AI's output is actually trusted. Why 14 days: matches industry SaaS-onboarding studies showing the first 2 weeks predict W4 retention with ~80% confidence (Mode, Amplitude). Why 3: below this users haven't internalized the value; above this they reach for Zunou unprompted.

These specific numbers are a starting hypothesis, not a commitment. We instrument them on day 1 and refit monthly against real cohort data. If at month 2 the actual threshold is 7/1/4 — we update. If at month 3 only 5/1/3 in 21 days correlates with W4 retention — we update the window too. The contract is the framework; the numbers are an iteration.

The launch mechanic that produces this density is the part of the strategy that sounds unusual. Instead of launching one Tokyo community at a time (the Eventbrite playbook — city-by-city, the "campus model"), we light four overlapping communities in the same week, picked specifically because their members already see each other.

The physics term for this is sympathetic detonation — adjacent explosive charges igniting each other through shockwave coupling. The growth-theory term is percolation threshold — the moment a sparse graph flips from disconnected clusters into one giant connected component.

In plain terms: an attendee at AI Tinkerers Ginza on Tuesday sees three people at Venture Café Toranomon on Thursday. By the end of the launch week, one sentence becomes literally true inside the Tokyo English-speaking founder graph —

The sentence that signals we won
"Everyone I know
is on Zunou."

When this becomes literally true for one person inside a launch community, the percolation threshold is crossed. From there it spreads passively.

One launch leaks. Four overlapping launches chain-react.

06 The launch shape

One pilot first. Learn. Double. Scale.

We don't sympathetic-detonate four launches before we know the platform is ready. We onboard one community deeply, take the operational learnings, fix what breaks — then double, then scale to the rest.

Pilot · stage 1

One community

Deep onboarding. Marco + Malek hand-hold the first ~50 users. Every friction point gets logged. Platform readiness becomes a known.

Learn · stage 2

Fix what breaks

Onboarding gaps, integration friction, support questions. Real magic-number data replaces the hypothesis. Stickiness mechanics validated or rebuilt.

Double · stage 3

Adjacent overlap

Add 1–2 communities that share members with the pilot. Validates the cross-community percolation thesis before scaling further.

Scale · stage 4

Sympathetic detonation

Now the multi-community simultaneous launch from a position of knowing the platform works. The original §05 mechanic, but earned not assumed.

Why pilot-first, not synchronized

A simultaneous 4-community launch only works if the platform is ready to handle 200 cold signups with no operational drag. We don't know that yet. Falling flat across 4 communities at once damages 4 relationships at once and the recovery cost is brutal in the Tokyo founder graph — everyone knows everyone. A messy pilot in 1 community recovers; a messy launch across 4 doesn't. The sympathetic-detonation play in §05 stays — we just earn the right to run it before we run it.

Pilot candidates · ranked by adjacent overlap with our ICP

P1 · PILOT TAI (Tokyo AI) ↗
4,000+ members · AI engineers / researchers / PMs · monthly meetup + Slack

Why first: the broadest overlap with every other community we'd target (every AI-tech-founder Slack in Tokyo has TAI members in it). Mostly builders, not founders — which matches Zunou's day-1 user (people who run lots of meetings + AI-fluent + curious about new tools). Open membership = lower bar to access than private founder lists.

Pilot mechanic: Marco gives a 15-min demo + Q&A at a monthly TAI meetup. Onboard the first 30–50 sign-ups with white-glove support. Six weeks of measured cohort behavior before deciding what comes next.

P2 · DOUBLE AI Tinkerers Ginza ↗
~200 builders · monthly demo nights

Highest-signal builders — the people who write tools other engineers adopt. Heavy overlap with TAI. If pilot data validates, this is the natural "double" community.

P2 · DOUBLE Venture Café Toranomon ↗
Weekly Thursday gathering · CIC Tokyo

Highest-frequency repeated touchpoint in Tokyo's startup graph. Weekly recurring presence = compound exposure. Builders + founders + investors mix.

P3 · SCALE Tokyo Founders Group
~150 operator-founders · private list

Operator-founders — closer to Zunou's marketed "For Founders" persona. Private list, warm-intro access. Reach them after the platform is proven via builders.

P3 · SCALE Startup Grind Tokyo ↗
Monthly fireside chats · founders + investors

Established founder community with monthly event rhythm. Overlap with Venture Café audience. Useful for credentialing speaker spots.

P3 · SCALE Le Wagon Tokyo alumni ↗
300+ alumni · technical founders + builders

Tech bootcamp alumni network in Tokyo. Slack-active, tools-curious. Strong builder overlap with TAI + AI Tinkerers.

P4 · LATER Headline / Coral / Genesia portfolios
VC portfolio cohorts · post-platform-proof

JP VC portfolios as a community (Headline runs IVS · Coral runs YC-like programs · Genesia Ventures). Wait until Zunou has paying logos to warm-intro at this layer.

P4 · LATER IVS (Infinity Ventures Summit) ↗
Annual · Kyoto · ~13,000 attendees

Japan's largest startup conference. Anchor event for the year. Aim to demo / pitch only with a working platform + early logos in hand.

Why this order: P1 (TAI) is broad + open + adjacent to everything else. P2 (AI Tinkerers, Venture Café) are the natural double — heavy member overlap with P1. P3 (Tokyo Founders, Startup Grind, Le Wagon) widen the audience to operators + technical founders. P4 (VC portfolios + IVS) needs proof to access. We pick P1, run it deep, and decide P2 based on what we learn — not based on the plan we wrote today.

Before we invite anyone

Are we ready to onboard them?

A pilot only works if the platform handles the first 50 users without falling over. These are the questions we answer with platform / product / Malek before the TAI demo gets scheduled.

Onboarding experience

First 15 minutes, day 1

  • · Sign-up → time-to-first-value: target ≤ 5 min.
  • · Calendar OAuth: works on Google Workspace + iCloud + Outlook?
  • · Slack OAuth: works on personal + workspace?
  • · Voice setup: mic permission, fallback if denied?
  • · First AI action: prep brief for next real meeting — does it land or feel generic?
  • · What happens if integrations fail silently?
Their current pain

What TAI members complain about

  • · "I can't keep up with my Slack DMs across multiple workspaces."
  • · "I miss action items from meetings because note-taking sucks."
  • · "Calendar prep takes 20 min I don't have."
  • · "Event RSVPs scattered across Connpass / Lu.ma / Peatix."
  • · "Follow-ups slip through the cracks; my CRM is a notebook."
  • · If Zunou solves 2–3 of these well, they'll try it. If it solves 1 plus has rough edges, they won't.
What makes them stick

Day 7, 30, 90 retention

  • · Day 7: morning brief becomes the first app opened.
  • · Day 30: 5+ colleagues active in same workspace (the magic number) — cross-context insights are visible.
  • · Day 90: internal team chat shifts from Slack to Zunou for at least one workflow.
  • · Habit loop: morning brief → meeting prep → in-meeting capture → post-meeting follow-up → next morning's brief.
Open platform questions

Need answers before the demo

  • · Can we handle 50 concurrent active users without degradation?
  • · Inference cost per active user — what's the actual runrate?
  • · Multi-workspace isolation — solid or leaky?
  • · What's the support model when a TAI member DMs Malek with a bug at midnight?
  • · APPI compliance posture — even if not enterprise, JP users will ask.
  • · Disaster mode: what's the rollback plan if the launch demo glitches live?

The honest answer to "ready?": we don't know yet. Phase 0 (foundations) answers it. The TAI pilot demo doesn't get scheduled until the open-question column above is green. If platform readiness takes longer than expected, we slip the demo — not the platform's quality bar.

07 How it actually unfolds

Five phases. Each gated on readiness, not on the calendar.

We don't promise dates we can't keep. We promise readiness gates the team agrees on in advance. Earliest plausible launch is summer 2026; later is fine if Phase 0 isn't clean.

  1. Phase 0 · Foundations

    Ship the cost guardrails and platform readiness checks.

    Token budget meter + multi-model routing tier — cheap model for high-volume work, premium for heavy reasoning, model-agnostic (Claude / GPT / Gemini routable based on cost-per-task). JP landing page on zunou.anysigma.com. Magic-number counters in the PWA. Events-feed v0 (Connpass + Doorkeeper public APIs). Validate community overlap via roster sampling. Gate: all six items shipped, or no pilot.

  2. Phase 1 · The synchronized launch

    Four Tokyo communities, same week.

    Week 1: AI Tinkerers Ginza demo + Tokyo Founders private launch. Week 2: TAI presentation + Venture Café Thursday Gathering. Same-week PR Times release. Member-of-N badge live in product. Gate: 100 signups across the four, ≥30% calendar-connected.

  3. Phase 2 · IVS Kyoto + density push

    Convert the conference into ARR-credible logos.

    IVS LAUNCHPAD pitch (or side-event regardless). Booth with live "summarize this booth's pitches" demo. Pre-conference auto-prep emailed to RSVP'd attendees who connect calendar. Gate: NSM ≥ 60 WAU past magic number by end of phase.

  4. Phase 3 · Density compound + first paying logos

    Prove paid conversion before the free runway expires.

    First paid logo + PR Times release. Akai Wagon / Indelible portfolio rollouts. "This Week in Tokyo" digest hits 5,000 weekly uniques. Ringi automation alpha to 3 enterprise design partners. Apply for METI IT subsidy. Gate: ≥5 paid logos, MoM growth ≥ 20%.

  5. Phase 4 · Stage-gate review

    The single yes/no/extend decision against six PMF criteria.

    ~6 months after Phase 1 launch. NSM ≥ 200 · One launch community at 25%+ density · 35%+ activation in 14 days · ≥3 paid logos · Inference ≤¥600/AU/mo · Member-of-N ≥ 15%. Hit ≥4 → fuel. ≤2 → pivot. 3 → extend 60 days.

  6. Phase 5a · If PMF — fuel

    Raise sponsored-seat caps · open communities #5–10 · begin SI partner conversations.

    NTT Data / Fujitsu / Itochu for the year-2 enterprise lane. Akai Wagon + Indelible portfolios fully active. Reach ¥30M ARR by month 12 of active rollout. Goal: KGI hit.

  7. Phase 5b · If pivot

    Rotate the wedge — most likely Ringi-first vertical or events-only product.

    Re-run validation (10 calls in 4 weeks) on the new wedge. Keep the four community partnerships warm; don't burn them. Aim to re-launch a focused product within 90 days.

08 Prior art

The pattern repeats. We're not improvising.

Four billion-dollar outcomes. Each manufactured density inside overlapping groups before going broad.

Notion
2018–19

Simultaneously seeded YC + 500 Startups + Techstars portfolios + designer Twitter.

Over half of YC's recent batch became customers. 95% organic traffic.
First Round Review
Discord
2015–16

Won gaming guild leaders ('supernodes') first; shipped Twitch integration as cross-community accelerant.

133% MoM growth at 3M users.
Growthcurve case study
Figma Community
2019–20

Public gallery of design files / templates / plugins → SEO + activation + pull-mechanism.

300+ creators, 600+ public files. Became how non-users discovered Figma.
First Round on Figma's 5 phases
Lenny's Newsletter
2020+

Free weekly newsletter for 9 months before charging anything; paid Slack as the dense layer.

1M subscribers by 2024.
Growth In Reverse
09 What we're committing

One number is a commitment. The rest are working hypotheses.

We're explicit about which is which. Pretending the budget envelope or per-user cost is locked-in would be dishonest at this stage.

¥30M ARR
Aspirational KGI
Within 12 months of synchronized launch · ≥20% MoM in second half
¥600 / user / mo
Working free-tier inference cap
Hypothesis to test against real consumption. Will refit at month 1.
¥18–24M
Working budget envelope (TBD)
Refine after Phase 0 produces real usage data.

Real envelope = (observed per-user inference × user count) + (community partnerships we choose to fund) + (line items we ratify). Don't make hasty assumptions.

10 What could break this

88% of AI agent pilots fail to graduate to production.

That's the Gartner finding for 2026. Most enterprise buyers have been burned, or seen peers burned. Our discipline is the response.

Source citations on every AI output. Addresses Gartner's #1 blocker — evaluation gaps (64% of failed pilots). Every action Zunou takes is anchored to the meeting / message / document it came from.

Human-in-the-loop on every external action. Addresses governance friction (57% of failed pilots). AI drafts; humans send. We never auto-act on someone's behalf.

Refuse to ship anything below 65% acceptance in beta. Addresses model reliability (51% of failed pilots). The metric is gated — if a feature can't beat the bar, it doesn't reach launch.

11 The moment we'll know

A pre-committed stop-or-go decision in ~6 months.

No 18-month death march. Six PMF criteria; three possible outcomes; one explicit rule we agree to in advance.

≥ 4 of 6
Fuel

Open communities #5–10. Raise sponsored-seat caps. Push toward the KGI.

3 of 6
Extend 60 days

Then re-decide. Don't force it; don't kill it prematurely either.

≤ 2 of 6
Pivot

Most likely candidates: Ringi-first vertical, or events-only product.

The six criteria: NSM ≥ 200 weekly active users past the magic number · One launch community at 25%+ density · 35%+ activation in 14 days · ≥ 3 paid logos · Inference cost ≤ ¥600 / AU / mo · Member-of-N ≥ 15%.

12 The ask

Seventeen decisions. Default = ship.

We don't need consensus — we need explicit objections so we can address them or proceed with the disagreement noted. React on each.

Strategy & launch · 1–13
01
Geographic focus — Japan-led for 12 months

Home market; second geography sequenced post-PMF, not preemptively.

default = ship
02
KGI — ¥30M ARR within 12 months of synchronized launch

≥20% MoM in the second half. Open: when the 12-month clock starts, given platform readiness gates Phase 1.

default = ship
03
NSM — WAU past magic number, in a partner community

Knowing it grows slowly at first.

default = ship
04
Magic number (v0) — 5 / 1 / 3 in 14 days

5 colleagues from same community + 1 calendar + 3 AI actions accepted, in 14 days. Refit monthly.

default = ship
05
Synchronized 4-community launch

Yes, or phase 2+2?

default = ship
06
Sponsored-seat cap + free-trial duration — open for discussion

Working estimate: 50 members × ¥600 × N months × 4 communities. Two candidate Ns: 3 months (tighter runway, faster conversion test) vs 6 months (more generous, gives the magic-number cycle time to fire twice). Question to test with community owners themselves: what duration feels generous to their members vs feels like a teaser? Bounded liability either way, not 'free forever'.

default = ship
07
Public events feed — before / with / after waitlist?

Connpass + Doorkeeper APIs + Lu.ma at launch.

default = ship
08
Calendar as gate

Accept that users without calendar see a deliberately broken-feeling product.

default = ship
09
Budget approach — methodology over total

Per-user inference math + sponsored-seat caps + line-item placeholders. Total ~¥18–24M is a working estimate, not a commitment.

default = ship
10
Stage-gate rule — ~6 months after Phase 1 launch

≥4 of 6 PMF criteria → fuel; ≤2 → pivot; 3 → extend 60 days.

default = ship
11
Adopt Masaru's positioning angle as the lead message

“Strategy in Notion. Tasks in Asana. Decisions in Slack. Good luck.”

default = ship
12
Retire HN / PH / IH and cold-LinkedIn

Replace with warm-intro + community + earned media.

default = ship
13
Community-discovery direction (A / B / C)

A = passive emergence (default); B = lightweight in-product recs (Phase 3+ test); C = community discovery as core surface.

default = ship
Operational gaps · 14–17
14
Pricing v0 — adopt tier shape + self-serve upgrade flow

From §16.5 gap audit (S1). Free + ≥¥3,500/mo paid. Refined via validation calls.

default = ship
15
Support model v0 — Marco + Malek covering JP/EN business hours

From §16.5 (S2). In-app widget + Slack channel. First CSM hire post-day-180 if Phase 5a fuel.

default = ship
16
Attribution v0 — last-touch for cohort analysis

From §16.5 (M2). Revisit at day 90 once data flows.

default = ship
17
Loop 6 commitment — invest product eng in meeting-prep viral loop

From §4.6 Loop 6. Loom rode this exact loop to 25M users — every shared video is an ad.

default = ship
13 Honest about what's missing

What we still need to build before launch.

A real plan names its gaps. Four operational gaps surfaced in the audit became Decisions 14–17 above. Here's the rest of the list.

Strategic · 5 gaps
  • · Pricing v0 (tier shape + self-serve upgrade)
  • · Customer support model (who answers in JP / EN, on what channel)
  • · Crisis / outage playbook for launch week
  • · Hiring plan tied to day-180 stage-gate
  • · Advisor / investor update cadence
Product · 7 gaps
  • · Mobile / PWA experience details
  • · Account deletion + data export (APPI)
  • · Team admin / permissions UX
  • · Notification preferences
  • · Performance budget + JP CDN
  • · Accessibility (WCAG AA)
  • · Error recovery during meetings
GTM artifacts · 8 gaps
  • · Onboarding email copy (cadence ready)
  • · Founder demo script for AI Tinkerers
  • · Community-owner pitch deck (separate from this)
  • · Investor pitch deck
  • · Partnership MoU template
  • · PR Times release calendar
  • · JP press relationships
  • · Customer reference library (Phase 2)
Measurement + risk · 8 gaps
  • · Cohort analysis cadence (Mon / first Friday)
  • · Attribution model (last-touch v0)
  • · Qualitative feedback loop post-launch
  • · Churn / exit interview process
  • · Single-founder dependency contingency
  • · Competitive-event response (Notion AI launch, etc.)
  • · Launch-community-cancellation backup
  • · AI provider outage graceful degradation (multi-provider failover: Anthropic / OpenAI / Gemini)

Tagged: MUST before Phase 1 · SHOULD before Phase 2 · DEFER to Phase 3+. The 17 decisions above resolve the highest-priority gaps. The remaining 24 need owners, dates, and tracking — Phase 0 work items.

14 How to engage

React on the 17. Push back specifically. Default = ship.

We don't need consensus — we need explicit objections so we can address them or proceed with the disagreement noted.

If you're on board

A single thumbs-up reply on the 17 is enough. We move forward and lock the Phase 0 owners + dates.

If you have a concern

Name the specific decision number + the alternative you'd ratify instead. We'll add it to the agenda for the live discussion.

Three ways to respond

Reply to Marco

Email or Slack DM. Decision number + ✅ / ⚠️ / ❌. One line each is fine.

Team thread

Slack thread on the launch channel. Everyone sees the reactions at once — fastest path to alignment.

Live review slot

30-min decision meeting once we have written reactions. Calendar invite from Marco when scheduled.

"If you finish reading this and have no objections, we haven't written it well enough."

15 Define the terms

Glossary.

The international team doesn't all share the same vocabulary. Every acronym and strategic term used in this proposal — defined in one place.

Metrics & strategy

KGI 重要目標達成指標
Key Goal Indicator (重要目標達成指標) is the top-level outcome the company commits to. In Japanese strategy practice it sits above NSM and KPIs. Ours is ¥30M ARR within 12 months of synchronized launch.
NSM
North Star Metric — the single product metric that most closely tracks the value users get. Connects KGI (lagging) to KPIs (leading). Ours: weekly active users past the magic number, in a partner community.
KPI
Key Performance Indicator — the weekly-tracked leading metrics (activation rate, time-to-calendar-connect, W4 retention, etc.) that roll up into NSM. See §11 (Stage-gate) for our specific six.
PMF
Product-Market Fit — the point where customers pull the product through the funnel instead of being pushed. Often invisible from inside but unmistakable from outside.
ARR
Annual Recurring Revenue — the yearly value of subscription contracts. Standard B2B SaaS health metric. ¥30M ARR ≈ US$200k.
MoM
Month-over-Month growth rate — (this month − last month) / last month. We commit to ≥20% MoM in the second half of the launch window.
ICP
Ideal Customer Profile — the company / user we're built for and who we deliberately target. Ours: founder-led English-tolerant Tokyo scale-ups.
WAU
Weekly Active Users — users who engaged within the rolling 7-day window. We use this for NSM rather than DAU (daily) because executive use patterns are weekly, not daily.
DAU
Daily Active Users — meaningful action within 24h. DAU/WAU ratio is a 'stickiness' indicator.
AU
Active User — a single engaged user. Our inference budget is expressed per active user per month (¥600/AU/mo on free tier).

Growth & product

Magic number
The threshold (defined per product) above which retention jumps. Slack: 2,000 messages. Facebook: 7 friends in 10 days. Ours (v0 hypothesis): 5 colleagues from same community + 1 calendar + 3 AI actions accepted, in 14 days.
Density
Zunou-specific term — the condition where enough colleagues in the same community / team are using the product that its AI has the cross-context it needs to be useful. The product IS the density.
PLG
Product-Led Growth — go-to-market where the product (free tier + self-serve onboarding) does the selling. Notion, Linear, Figma, Slack all archetypal PLG.
CLG
Community-Led Growth — distribution model where the user community generates referrals, content, social proof. Often paired with PLG.
Member-of-N
A Zunou-specific KPI: the % of weekly active users who are members of 2+ partner communities. Tracks the cross-community percolation effect from the sympathetic-detonation launch.

Physics & network theory

Sympathetic detonation
Physics term used for our launch mechanic. When multiple communities with overlapping members are launched in the same week, attendance at one triggers attendance at another. Adjacent fuses light each other.
Percolation threshold
Statistical physics concept describing when adding edges to a graph causes the structure to flip from sparse disconnected clusters into one giant connected component. Our 4-community launch is engineered to cross this threshold inside Tokyo's English-speaking founder graph.

AI & technical

MCP
Model Context Protocol — open standard introduced by Anthropic in Nov 2024, adopted by OpenAI / Microsoft / Google in 2025–26, donated to the Linux Foundation in Dec 2025. 10,000+ public servers exist. Zunou is MCP-native.
HITL
Human-in-the-Loop — AI proposes / drafts, humans confirm before any externally-visible action (sending email, posting to Slack, creating events). Our discipline against the 88% agent-pilot failure rate.
LLM
Large Language Model — the family of AI systems (Claude / GPT / Gemini / Llama / Mistral / etc.) that Zunou routes through MCP-mediated context for each task.

Japan-specific

Ringi 稟議
稟議 (ringi) — the standard Japanese corporate decision process: a written proposal (ringisho) circulates from lower-level employees upward, each stamping approval, before a final senior sign-off. Slow but builds organisational buy-in. Zunou's Ringi-automation alpha is a defensible JP-specific feature.
Keigo 敬語
敬語 (keigo) — Japanese honorific speech system, with three registers: teineigo (polite), sonkeigo (respectful, for the listener), kenjogo (humble, for the speaker). External-facing communication that gets keigo wrong reads as offensive. The Zunou wedge against Notion AI / Slack AI / Copilot.
APPI
Act on the Protection of Personal Information — Japan's primary data privacy law. The 2025–26 amendments add administrative penalties and stricter cross-border transfer rules. Required compliance for any JP enterprise customer.
METI
経済産業省 — Japan's Ministry of Economy, Trade and Industry. Runs national AI strategy and the SME AI subsidy programs (50–66% project cost reimbursement, ¥300k–¥4.5M per grant). JP-companies are eligible.
IVS
Infinity Ventures Summit — Japan's largest startup conference, held annually in Kyoto (July). 60+ alumni exits, freee and COVER among them. Organised by Headline Asia. Phase 2 of our rollout is anchored here.
TAI
Tokyo AI (TAI) — the largest technical AI community in Japan. Engineers, researchers, investors, PMs. Founded by Ilya Kulyatin. Monthly meetups + Connpass + Slack. 4,000+ members as of May 2026.
AiSalon
AiSalon Tokyo — global community for AI-focused founders, builders, investors. Tokyo chapter co-hosted with Tokyo AI, JETRO-supported. Monthly in-person events with lightning talks.
JETRO
Japan External Trade Organization — government-affiliated agency that supports foreign business with entry into Japan and Japanese business expansion globally. Free advisory; useful for non-JP companies setting up. Less relevant for Zunou (we're JP-native) except for inbound advisor relationships.

Business & process

PWA
Progressive Web App — a web app that installs to the user's home screen, runs offline, sends push notifications. Zunou's current shipping surface is a PWA.
MoU
Memorandum of Understanding — a written but typically non-binding agreement that signals commitment. Used in our portfolio-as-community play for the GP-Zunou agreement.
MoSCoW
Prioritization framework that buckets work into Must (do now), Should (after must), Could (if time), Won't (this cycle). Used in product-requirements.md to tier features for Phase 0 / 1 / 2 / deferred.
OAuth
Open Authorization — the protocol that lets you grant a third-party (like Zunou) access to your Google Calendar or Slack workspace without sharing your password. Foundation of all modern integrations.
SaaS
Software-as-a-Service — the dominant B2B software model: web-delivered, subscription-priced, continuously updated. Zunou is SaaS.
CSM
Customer Success Manager — a role responsible for post-sale customer adoption, retention, and expansion. Decision 15 commits to delaying our first CSM hire to post-day-180 stage-gate.
GTM
Go-to-Market — the strategy for launching, positioning, distributing, selling a product. This whole proposal is Zunou's Japan-led GTM plan.
CDN
Content Delivery Network — distributed servers around the world that cache static content close to users for fast delivery. Cloudflare runs one of the largest. zunou.anysigma.com is served from it.
16 The receipts

References.

Every numerical claim above traces back to a public source. Listed here for anyone who wants to verify or go deeper.

Last verified: 2026-05-11. If a source url is broken or you want a section traced to a specific footnote, ask Marco. The detailed source-by-claim mapping lives in the internal strategy markdown.