LansonAI
Deep Tech Voice Infrastructure with Defensible IP

Powered by Trinity Engine™
Up to 120× Speed

1 hour of audio processed
in under 30 seconds
80% Cost Reduction

Infrastructure cost at
roughly 1/10th of industry standard
Patent Family Moat

Zero-Reflow Rendering method already deployed in production



Founder
Zhen Zhang
Ex-Tencent (5 years, 100M+ DAU systems)
Deal Terms
Raising: Pre-seed SAFE (post-money)
Initial allocation: $100K @ $10M cap
Remaining allocation: $200K @ $12M cap
Target: $300K (hard cap $500K)

1

Problem: A Structural Gap That Has Always Existed
Voice is humanity's most natural output — but it was never designed to be consumed.

When you type, you process as you go: you choose words, restructure sentences, delete and rewrite. The output arrives already organized.
When you speak, none of that happens. You express first. The work of organizing, capturing, and making sense of it is deferred — to whoever is listening, or whatever machine comes next.
This isn't a recognition problem. Speech-to-text has existed for decades.

It's a context problem: the gap between heard and understood — between raw audio and something a human can skim, a system can query, or an AI can reason over — has never been closed.
Voice data today is linear, ephemeral, and unstructured by default. It can't be searched. It can't be cited. It can't be passed to a model as reliable context. Every generation of voice products hit the same ceiling: you could transcribe speech, but you couldn't use it.

The result: the most natural human interface produces the least reusable information.

2

Why Now: The Inflection Point Is Here
Two forces converged simultaneously — for the first time in history.

LLMs Changed the Foundation

  • For 30 years, voice failed not because machines couldn't hear — but because they couldn't understand context.
  • LLMs changed that. Contextual understanding is now reliable, fast, and deployable at scale — the missing layer finally exists.
Spatial Computing Is Arriving

  • Apple Vision Pro and Meta Quest have shipped. Stable, cognition-safe captions are no longer a feature — they are a baseline requirement.
  • For the first time, a hardware platform makes voice rendering quality mandatory infrastructure.

3

Trinity Engine: Technical Strength Overview


Performance Metrics
120×*
Speed
1 hour audio → 30 seconds
90%*
Zero-Edit Rate
Publication-ready output
80%*
Cost Reduction
Significant cost savings

† 120× speed | 90% zero-edit rate | 80% cost reduction are based on internal benchmarks. Full methodology and test conditions in Appendix.

4

Trinity Engine: User Experience Delivery
Technology sets our ceiling — product philosophy determines how far we go.
How Voice Rendering Works Today
Flow-Layout Rendering
Text blocks reflow constantly, causing users to lose their place and experience motion sickness.
Cognitive Reconstruction
Constant text shifting forces the brain to re-anchor, creating a fundamental barrier to usability.
Zero-Reflow Rendering Delivers
Fixed Coordinate Anchoring
Caption containers use stable 3D coordinates so text appears in place without reflowing.
Cognition-Safe Delivery
follow live conversations naturally without re-anchoring or motion sickness. Try it yourself — link below.

5

Patent Strategy: Early-Mover Advantage
What This Patent Covers
  • Establishes prior art and first-to-file position on Zero-Reflow Rendering
  • Creates licensing negotiation leverage across 2D, AR, and spatial computing surfaces
  • Forces competitors to design around our method — or license it
  • Already deployed in production: this is not a theoretical claim
Why Platforms Need This
  • Apple Vision Pro: Accessibility APIs becoming mandatory infrastructure
  • Meta Quest: Real-time captions required for key enterprise and social use cases
  • AR Glasses Wave: Stable, cognition-safe captions are table stakes for mass adoption
  • Every major platform entering spatial computing will need a solution — we already have one
Licensing Potential
  • Comparable IP deals in voice/display suggest meaningful per-device royalty potential
  • Platform integration licenses represent a separate revenue layer from SaaS
  • Strategic acquisition optionality: 3–5 year horizon
  • Patent grant expected within 12–18 months; licensing is upside, not base case

We welcome technical due diligence at any level of scrutiny — and we'd love to show you what's already running in production. If you're building in spatial computing, accessibility, or voice infrastructure, let's talk.

6

Market: Bottom-Up TAM with Clear Path



Revenue Scenarios

7

Early Traction & Product Validation
127
Active Users
100% organic beta
523
Hours Processed
Total platform usage
90%
Zero-Edit Rate
vs. 34–41% industry avg
76%
Willing to Pay
From user survey
Retention & Usage
  • Wave 1 (Oct 2025): 34% retention
  • Wave 2 (Dec 2025): 58% retention
  • Heavy Users (10+ hrs): 8 users
  • Average: 4.1 hrs/user | Median: 2.3 hrs/user
Quality Benchmark (Zero-Edit Rate)
12%
Whisper raw
34%
Otter.ai
41%
Descript
90%
LansonAI
01
Month 1 Launch Plan
Scale to 30-40 paying users at $49.99/mo
02
Month 3 Target
$5K MRR (~100 paying users at $49.99/mo)

8

Competitive Landscape: Performance Gap
Lanson Podcast vs. Content Tools
† 120× speed and 90% zero-edit rate are based on internal benchmarks.
Full methodology and test conditions in Appendix.
Lanson Live vs. API Infrastructure
  • Competitors' figures show API price. LansonAI figure reflects infrastructure cost — not current pricing.
  • LansonAI also offers a complete end-to-end solution including patented display rendering technology.
Our Unfair Advantages
  • Trinity Engine™ architecture: 3-layer optimization no competitor has
  • Patent family: Cognition-safe Voice Context Layer rendering
  • IP License: first-mover position across 2D, spatial computing, and future HCI surfaces
  • Execution speed: Shipped production system in 6 months — iOS / Android / Web
  • Infrastructure cost:< $0.0005/audio min — ~10× unit‑economics headroom vs. public market list pricing

9

Team:Technical Founder with Billion-Scale Experience
What Makes This Founder Different
1
Systems Architecture at Scale

Five years at Tencent building and maintaining infrastructure serving 100M+ DAU
2
End-to-End Product Execution

Built Web, iOS, and Android — solo — in 6 months Everything included, end to end.
3
Taste and Product Judgment

Built and refined the product without a design team, PM, or marketing budget.
What I've built
Full-stack product (Web / iOS / Android)
Trinity Engine processing pipeline
Serverless infrastructure with control center
127 active users, 523 hours processed
Patent design, write, filed and prosecuting
Brand identity, positioning & launch video

10

Business Model: Product → Platform → IP
Primary: Lanson Podcast subscription
Note: This pricing model reflects the current creator subscription tiers only. Lanson Live is not included here and will be monetized separately after broader product validation.
1
Creator Starter
Free
Target: New users
2
Creator Plus
$29.99/mo ($279/yr)
Target: Creators
3
Creator Pro
$49.99/mo ($470/yr)
Target: Production studios
Unit economics (projected):
$50
CAC
$15
COGS
5%
Churn
$700
LTV
* LTV calculated based on Creator Pro tier ($49.99/mo), $15 COGS, and 5% monthly churn (industry benchmark). Blended ARPU across tiers TBD.
Secondary: Infrastructure — API / SDK
Offer
Trinity Engine™ as an embeddable SDK and hosted API for platforms, apps, and enterprises that need production-grade Voice Context delivery.
Revenue model
Usage-based API pricing (incl. Lanson Live real-time transcription API, planned) + platform integration contracts.
Target
Conferencing tools, media platforms, accessibility layers, AR/MR OS vendors.
Tertiary: Patent licensing
Target partners
Apple, Meta, Google, spatial computing OEMs
Revenue model
Per-device royalty or platform integration license
Timeline
Contingent on patent grant (12-18 months)
12-18 month milestones
01
Month 3
Hit $5K MRR and validate at least one repeatable acquisition channel
02
Month 6
Scale to $15-20K MRR with positive unit economics on core channel(s)
03
Month 12-18
Prepare seed/Series A with $30-50K+ MRR and institutional readiness (team, IP, metrics)

11

The Ask: Pre-Seed SAFE (Rolling Close)
The product, technical foundation, and market narrative are already in place. We are raising because growth is the next constraint.
Use of funds (6-9 months)
Growth & Distribution (50%)
Creator partnerships, content marketing, SEO/ASO, paid experiments to find a repeatable channel
Product & Engineering (30%)
Improving Trinity Engine™, UX, onboarding, analytics and self-serve flows
Infra, Legal & Ops
(20%)
Server costs, tools, patent prosecution, basic operations
Key milestones for this round:
Launch paid tiers (Month 1)
Reach $5K MRR in 90 days (~100 paying users at $49.99/mo)
Establish at least one repeatable acquisition channel with positive unit economics
Maintain 6+ months runway at the end of this period to set up the next round

12

Vision & Contact

Lanson building the next-generation Voice Context Layer — capturing fleeting speech as it happens and settling it into readable, searchable, and computable context with near-zero cognitive load.

Roadmap
1
Short-term
Become the go-to production tool for professional content creators
2
Mid-term
Power enterprise transcription with human-level quality across languages
3
Long-term
Own the language rendering layer for every spatial computing platform


Talk is cheap. Try it yourself.



Email

13

Appendix A: Methodology & Data Sources

All performance claims are based on internal measurements and publicly available data. Full methodology available on request.


Batch Processing Speed
120×*
Measured end-to-end: 1 hour of audio processed in under 30 seconds using Trinity Engine's serverless parallel architecture. Benchmark conducted on internal test suite. Actual performance may vary by audio length.
Zero-Edit Rate
90%*
Defined as: output requiring no human correction before publication. Measured across internal test sessions during beta. Sample size and methodology available on request.
Cost Reduction
80%*
Based on internal production cost benchmarks (measured) compared to publicly listed per-minute pricing from major providers including AssemblyAI, Deepgram, Comparison reflects cost-to-deliver, not retail pricing.

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