AI health app launch
We built a consumer AI health app and its landing page in about four months. The founder's launch post drove 1,500 waitlist sign-ups in one day.
What we worked on
Launch & Customer Acquisition AI Product (LLM) Build Landing Page & Waitlist
Year completed
2026
Background
A health-tech founder wanted to give consumers a trustworthy, personal read on their own bloodwork, then launch publicly with a waitlist. He needed a product credible enough to send launch traffic to, and a page for that traffic to land on.
Challenges
Lab results arrive as PDFs in inconsistent formats. The app had to extract and normalize them, score them, and explain them, and the landing page lived in a separate prototype, outside the app.
Outcome
A launch-ready AI MVP: lab-PDF extraction and normalization, a health score, AI chat, nutrition, daily actions and a health roadmap, with the landing page and waitlist capture built into the app. When the founder announced publicly, he reported 1,500 waitlist sign-ups in a single day. The traffic came from his own launch post. We built the product and the page it landed on.
A consumer health-tech startup (name withheld)
Name withheld
Launch-ready in about four months: the AI health app and landing page behind 1,500 waitlist sign-ups in one day
1,500
Waitlist sign-ups in one day after the founder's launch post (client-reported)
~4 months
From first sprint to the end of the support sprint
6+
AI features grounded in each user's own lab results
Pull quote
This has been working great. Really happy.
Founder, consumer health-tech startup (March 2026)
Snapshot
Industry
Consumer health tech: AI lab-results interpretation
Services
MVP build, AI pipeline for document extraction and normalization, scoring logic, landing page and waitlist capture, transactional email
Duration
December 2025 to March 2026 (MVP sprints plus a support sprint)
Before
No launch-ready product to send traffic to, a landing page in a separate prototype, and lab PDFs in formats that didn't match.
After
A launch-ready AI MVP in about four months, with the landing page and waitlist capture inside the app. The founder's launch post brought 1,500 waitlist sign-ups in one day (client-reported).
Timeline
Dec 2025
First sprint
The core frame working end to end.
Jan 2026
Lab upload and landing page
Lab upload and profile flow in founder testing; landing page with waitlist capture integrated into the app.
Feb 2026
MVP hardening
Biomarker matching, reference ranges, AI response speed and authentication.
Mar 2026
Public launch
The founder's announcement brings 1,500 waitlist sign-ups in a day (client-reported); support sprint QA.
What we designed
MVP flow: onboarding, complete profile, lab upload, nutrition, chat, today’s action and score algorithms.
Lab upload had to work across lab formats and normalize results to a reference sheet with optimal, average and caution ranges.
AI features: chat, nutrition, action items, biomarker analysis, biomarker education and a health roadmap.
Why this work existed
Give consumers a trustworthy, personal read on their own bloodwork, and give the founder a product and landing page ready for a public launch.
What we built
Lab upload with AI extraction and normalization, biomarker synonym matching and admin matching tools.
Reference ranges and charts, biological and cardiovascular age, and a proprietary health score.
AI chat, nutrition, daily actions and a health roadmap, all grounded in the user’s own results.
The landing page with waitlist capture, integrated into the app, plus social sign-in and automated welcome and password emails.
What powers it: an LLM pipeline for document extraction and the AI features. No AI was used in the acquisition itself.
The problem this solved
We turned unstructured lab PDFs into a structured, scored, explainable health profile, and gave the founder a launch-ready product and waitlist page before his announcement.
FAQ
Did Creme Digital drive the 1,500 sign-ups?
No. The traffic came from the founder's own launch announcement. We built the product and the landing page with waitlist capture that the traffic landed on.
Where is AI used?
In the product: an LLM pipeline extracts and normalizes lab PDFs and powers chat, nutrition, daily actions and the health roadmap. No AI was used in the acquisition itself.
How long did it take?
About four months, from the first sprint in December 2025 to the end of the support sprint in March 2026.
Are paying users or revenue figures available?
No. The waitlist number is client-reported, and we don't claim paying users, revenue or clinical accuracy.
Who is this kind of engagement for?
Founders about to launch who need the product and the capture page in place before the announcement.
