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We start with products tested in real clinical workflows, then expand the shared infrastructure and add specialty AI in stages.
Traction
Progress measured in real clinical settings, not only in demos.
The selection criteria were refined through real cases of freckles, lentigines, and melasma. Because the product is used daily at the validation site, investors can see validation speed and field fit together.
We are building a network of clinical partners for early deployment, validation, and feedback. These institutions are not merely distribution channels; they are the foundation for product learning and expansion.
We are co-developing computer-vision support for quantitative wound assessment in plastic surgery. Clinical and research expertise have been connected from the design stage to create a stronger foundation for future validation.
Five interlocking advantages that one copycat specialty AI cannot easily reproduce.
Why this structure is hard to copy
Shared interpretation and reasoning infrastructure used across every clinic is an asset a single specialty AI cannot build. It compounds as more specialties come online.
We clinically validate DermatoScan AI as the first case and set the bar for third-party AI. The validation data widens the gap with later entrants as it accumulates.
Early partner clinics, a specialty network, and the University of Ulsan academic channel form the base for adoption and validation. A trust-based healthcare network cannot be built through advertising alone.
The platform is designed to support rather than replace clinicians. Keeping licensed professionals in control reduces a key barrier to clinical adoption.
The founding team combines hands-on experience in elective outpatient care with technical execution. Clinical problem definition and product implementation happen within one integrated team.
Market
A single specialty module addresses a narrow market. Shared infrastructure used across outpatient care expands the opportunity. We size the market as infrastructure subscriptions plus specialty AI services.
The broad global opportunity across medical AI services, care connectivity, workforce training, and remote consultation.
The markets directly addressed by shared outpatient infrastructure and specialty AI, beginning with dermatology and plastic surgery.
The initial three-year opportunity across Korean clinics serving international patients and clinical software for domestic institutions.
Global
Korea pairs high clinical standards with fast digital-care adoption, so medical AI validated here earns trust abroad. The value of physician-supporting AI grows most in emerging markets where specialists are scarce.
A validation record built under Korea's high clinical bar is the starting line for adoption abroad. The credibility of Korean medicine travels with the product.
Built on Dockie-talkie real-time medical interpretation, it is made for multilingual and cross-border care from day one.
The harder specialist access is, the wider the gap physician-supporting AI closes, and that gap is largest in emerging markets.
With little legacy EMR to displace, a cloud clinical OS can go live quickly.
Global expansion is a Phase 3 goal. There is no overseas revenue or live operation today.
Business Model
The model is a monthly per-physician-seat subscription. Start with the AI-based chart, add interpretation, reasoning, and specialty AI per seat, or take an all-access plan. It is decoupled from procedure price and referral, so revenue never pulls on a physician's judgment.
| revenue source | structure |
|---|---|
| AI-based chart (Clinical OS · Layer 0) | KRW 150,000 / seat·mo |
| Shared infrastructure (Dockie-talkie · Clinical Copilot) | KRW 100,000-150,000 / seat·mo |
| Specialty vertical AI (DermatoScan · WoundScan) | KRW 150,000 / seat·mo each |
| Full platform, unlimited | KRW 400,000 / seat·mo |
| Third-party AI onboarding | Model TBD · phased |
All prices are per physician seat per month and are not tied to procedure price or type. Bundling more items adds another 5% off per item, and pricing may change before launch.
Per seat / mo
Billed by physician seat, not by procedure price or volume
KRW 0.15-0.40M
From the base chart to all-access, per seat (target)
3-Year Target
We aim for roughly KRW 5B in annual revenue within three years by expanding the number of adopting clinics and the specialty AI products.
100
Clinics onboarded
5,000
Cumulative patients
3+
AI products
~KRW 5B
Annual revenue (target)
Roadmap
Non-dilutive support funds early validation and development; seed investment then accelerates commercial execution. The two tracks are sequential and complementary.
We are running DermatoScan validation and Dockie-talkie clinic testing while developing WoundScan in parallel, pursuing government support programs, with Apgujeong Tune Clinic as the first validation site.
We plan to launch Clinical Copilot alongside a seed round, bring shared infrastructure services to hospitals, and expand into additional specialties.
We plan to launch third-party AI onboarding, pursue expansion into Southeast Asia and the Middle East, and prepare for a Series A round.
Funding
In parallel, we plan to pursue Korean venture-company certification through the R&D track to support policy access, tax benefits, and future fundraising.
In 2026, we plan to fund the initial validation and development base through government and institutional programs suited to medical AI, limiting early dilution.
With initial validation cases and internal product data in hand, we are looking for investors to help build the critical pre-launch stage. We value partners who understand both the responsibility and the pace required in medical AI.
Team
A practicing physician-founder, an integrated clinical-engineering core, and a specialty advisory network work within one product-validation loop.
Leads strategy, clinical validation, and first-in-clinic rollout as a physician-founder with direct clinical authority.
Leads AetherHeal's end-to-end agent system and backend architecture. He holds a B.S. in Computer Science from Sungkyunkwan University and M.S. and PhD degrees in engineering from KAIST’s Graduate School of Culture Technology. He designs systems that turn conversations, images, and clinical notes into one reviewable data flow.
Network
Specialist physicians take part in product validation and clinical-standard setting. These are advisory relationships grounded in active practice and research, not headcount.
FAQ
We've compiled the most frequently asked questions during the investment review process.
We can share key metrics, the business plan, and current fundraising materials.