ShipExperimentsFast

From hypothesis to real users in 14 days. We build, measure, and make honest ship-or-kill decisions — no committees, no sunk costs.

gv-os
$
4
Experiments Shipped
14-day
Build Cycles
100%
Replay Rate
$0
Wasted on Bad Ideas
Our Process

From Hypothesis to Honest Results

01
Hypothesis
Start with a falsifiable bet
02
Brief
One page, clear kill conditions
03
Test
Build minimum, ship fast
04
Measure
Collect real data, find surprises
05
Ship or Kill
No sunk cost fallacy
How We Build

Opinionated Stack, Fast Results

FrontendServer-first frameworks for fast loads and SEO
Next.js / Nuxt / React / Vue / TypeScript / Tailwind CSS
AI / MLAI-native from the start — generation, embeddings, voice
Claude API / Vercel AI SDK / ElevenLabs / LangChain / Pinecone
DataServerless-friendly datastores, no ops overhead
Prisma / Neon Postgres / DuckDB / Redis
InfrastructureDeploy in minutes, IaC when it matters
Vercel / GCP / Terraform / Docker / Doppler
Real-timeLive features without third-party lock-in
Socket.IO / WebSocket
QualityCatch problems before users do
Vitest / Playwright / Sentry / OpenTelemetry
IntegrationsPre-built connectors — build product, not plumbing
Plaid / Auth0 / Slack Bolt / Google APIs
How We Think

Not Philosophy. Practice.

Ship in Days

Time-box everything. If it takes longer than two weeks, scope is wrong.

4 experiments shipped, avg 11-day cycle

Write It Down

If you can't explain the hypothesis in one sentence, you don't understand it yet.

Every experiment starts with a one-page brief

AI as Cofounder

AI writes the first draft, reviews the PR, and challenges assumptions. Humans make the final call.

Claude-assisted across code, copy, architecture, and decisions

Data Kills Ego

Every experiment has kill conditions defined upfront. When the numbers say stop, we stop.

Pre-registered kill criteria on 100% of experiments

“The goal is not to build things. The goal is to learn whether things should be built.”
NG
About the Founder

Nick Gerner

VP of Engineering leading teams that build AI platforms, ML models, and provider-facing apps serving thousands of healthcare practices. Before that, I rose from engineer to VP at a venture studio where we built and scaled companies across energy, healthcare, and enterprise sales. Co-founded a healthcare software company. MS and BS from Johns Hopkins — distributed systems and machine learning.

Gerner Ventures is where I apply everything I've learned about building AI products at scale to my own experiments. Every idea gets a 14-day trial: build it, measure it, ship it or kill it. No committees, no sunk-cost fallacy. I've watched too many good ideas die in planning and bad ideas survive on momentum — this is my answer.

VP Engineering · Venture Studio Alum · Johns Hopkins MS/BS · AI Platforms · Healthcare Tech

Have an Idea? Let's Talk.

Whether you're exploring a partnership, have an experiment idea, or just want to compare notes on building things — I'd love to hear from you.

Especially interested in: AI applications, developer tools, and anyone running their own experiments.

Gerner Ventures