Live subtitles for any desktop app.

Translation lives in silos.

A serious language learner watches native foreign content on desktop for hours a day. The live stream has no fan-sub. The lecture player is a native app, not a browser tab. So they pause every sentence, look up words, lose the flow — and give up.

• Zoom captions stay in Zoom. YouTube subs stay in YouTube. Teams AI stays in Teams. Browser extensions cover only web tabs.
• None of them reach a native installed desktop client — a desktop LMS player, Webex, VLC, a regional conferencing tool, a desktop game.
• And for a learner, killing the original audio kills the job: they need to hear the source while reading the translation.

One OS-level layer. Any app.

A desktop app for Mac and Windows that captures audio from any running process and overlays a real-time translation — in under 700ms, the threshold where it reads as interpretation, not a delayed echo.

• Dual-track (default): live captions + a quiet whisper-dub layered under the preserved original voice. The learner keeps the source for ear-training and gets the missing 40% filled in.
• Auto-mute + dub (optional): for users who want hands-off comprehension over learning — original muted, AI voice on top.
• One-click Anki / export: capture a line + its translation and mine it to flashcards — the learner's own study artifact.

Pick the app, pick the language once, toggle on. It feels like one button — that's the whole promise.

Two curves crossed in 2025.

One enables the experience. One opens a lane nobody else can take. We're honest about which is which.

• Latency floor dropped under 700ms (shared). Streaming STT <300ms (Deepgram Nova-3), LLM-MT <200ms, streaming TTS <200ms TTFB (Cartesia Sonic). Real — but it crossed for everyone on the same APIs. This alone is not a moat.
• OS-level audio capture unlocked (narrow, defensible). macOS 14 CoreAudio process taps and Windows 11 audio-session APIs let third-party apps capture per-process audio from native clients without kernel extensions — first stable across both OSes in 2024–25. A browser extension structurally cannot do this.

A $13M wedge inside a $72M TAM.

Bottom-up, not market-report inflation. We lead with the wedge we can actually win, not the ceiling.

The S2S translation segment is ~$481.6M (2025); AI-in-translation hits $3.68B in 2026 at 25% CAGR. A broad-consumer ceiling — 5M heavy English-first users × $144 — would be ~$720M, but that zone is already capped by Zoom's free feature and free browser extensions. We treat $720M as headroom, never as the base case.

Sources: Expert Market Research (S2S $481.6M), The Business Research Company (AI translation $3.68B / 25.2% CAGR).

Pick the app. Toggle on.

The whole flow is one screen and one switch. Everything else runs in the background.

• 1 · Trigger. User opens a native app with foreign-language audio — a lecture player, Webex, a stream.
• 2 · Pick. Babelio tray → choose the app from the capture picker → confirm target language (remembered) → toggle ON.
• 3 · Translate. VAD detects speech → streaming STT→MT→TTS plays a quiet whisper under the original + a confidence-shaded caption overlay, all <700ms behind source.
• 4 · Review. Session transcript saved — aligned bilingual, jump-to-timestamp, export to Anki. Every correction feeds the eval set.

North Star: weekly native-desktop sessions translated end-to-end ≥ 4 per active user. Did they trust it enough to leave it running over a real lecture.

$12/mo, metered for margin.

Hybrid: flat $12/mo Pro base + metered translation-minutes via a reverse trial. Cheap subtitle minutes are the monetized core; dub minutes are metered to protect margin.

• COGS (source of truth, Cartesia + Deepgram, 55% duty cycle): ~$0.50/active dub-hr, ~$0.31/active subtitle-hr. STT is the dominant leg, not TTS — so falling model prices help us.
• Unit economics: ARPU $12, blended COGS ~$3.26/Pro user → 73% GM. CAC $35, payback 4.0 months, LTV ~$120 at 7%/mo churn → LTV:CAC ~3:1 (capped — not the retracted 5.7:1).

Prototype. Zero users — and we'll say so.

A working prototype exists. No users, no revenue, no LOIs yet. Everything past that is a validation plan, not a claim. We'd rather be honest than inflate.

• Week 1 — Wedge & WTP. 15–20 Mom-Test interviews with immersion learners from r/LearnJapanese, Refold / MoeWay / Migaku. Kill if <30% recall a specific painful live-content incident in 30 days.
• Week 2 — Concierge WTP. Hand-deliver subtitles to 5–10 learners, charge metered $5/hr. Output: 5 paying / LOI users, 3 testimonials. Kill if nobody pays a rate that clears COGS.
• Week 3 — Thin product + loop. Ship narrowest native-capture flow on one OS, instrument the sentence-mining-card activation event + loop factor K. Kill if <40% activate even hand-held.
• Week 4 — Channel test. Time the launch to a recurring immersion-challenge kickoff. Measure CAC, install-completion, K, GM/user. Go / no-go with real numbers.

PMF status: PENDING. We claim PMF only at ≥40% "very disappointed" (Sean Ellis) across ≥30 hands. Not before.

Everyone owns a box. Nobody owns ours.

Axes: app coverage (single-platform ↔ OS-wide) × output (captions ↔ voice dub). Our only defensible cell is native-installed-app voice dub with original audio preserved.

Big Tech will ship in-product translation — but an OS-wide cross-vendor layer cannibalizes nobody's core, so no single platform owner is incentivized to build it. The open lane survives precisely because it sits between the walled gardens.

The capture is a head start. The telemetry is the moat.

We're blunt: the audio tap uses public CoreAudio / WASAPI APIs — a funded competitor replicates it in a quarter. It buys time to collect the one asset that compounds.

• Per-app capture + eval telemetry flywheel — instrumented from session one. Which app, codec, packet jitter, VAD false-trigger rate, mute/unmute timing, every user correction. GPT-N never sees it. Over thousands of sessions you learn the optimal capture profile per application — a new entrant can't re-derive it without the same install base.
• Switching costs, then network effects — per-app profiles + persistent voice/glossary settings lock the workflow; multi-party team dubbing makes the product more valuable as colleagues join. Buildable, not present at MVP.

Seed the communities that already mine sentences.

PLG self-serve + founder-led community seeding for the first 100. No outbound sales at $144/yr. The buyer is a single learner who already funds their own immersion stack.

• Primary (80%) — community-led. r/LearnJapanese (~700k), r/Korean, Refold / TheMoeWay / Migaku Discords. Dense, named, calendar-timed (immersion-challenge kickoffs are recurring trigger events). CAC $15–35 [hypothesis].
• Secondary (20%) — SEO / content. High-intent long-tail extensions can't serve: "watch Japanese stream with subtitles desktop", "sentence mining live VTuber stream."
• Growth loop — legally-clean. The shareable artifact is the user's own bilingual sentence-mining card (their annotation, never the source media), posted where mining screenshots are already native behavior. Loop factor K ≥ 0.3 to prove; K < 0.15 → fall back to paid CAC.

Install friction (Gatekeeper / antivirus) is the #3 risk — mitigated by notarized signed builds, a first-run trust playbook, and seeding via a trusted community member, not a cold drop.

The honest open gap. One hire decides everything.

We won't pretend otherwise: the entire moat and the 10-week MVP hinge on one rare profile — a Rust + CoreAudio / WASAPI engineer who can ship per-process native audio capture across both OSes. That person is not yet confirmed.

• What we're solving with this round. Confirm the OS-internals co-founder / first hire and record verifiable founder background in audio / OS-internals + distribution audience.
• Why we surface it. A pitch that hides its weakest link wastes everyone's time. If the capture engineer doesn't commit, the timeline and the moat slip — so we treat the hire as the first milestone of the round, not an afterthought.

Raise to compress time-to-moat.

Milestones: capture engineer committed (week 0) → 4-week validation passed with real loop-K and CAC (month 1) → monetizable MVP shipped at ≤$0.50/active-hr (month 4) → ~$10K MRR (month 9–10) → the telemetry asset that underwrites the B2B/SDK Series A.