Launch AI Platform Checklist

Identify pain points

Look for personal frustrations, industry inefficiencies, or repetitive tasks that AI can solve.

Search forums & communities

Check Reddit, Quora, and niche Discord/Facebook groups for recurring complaints and questions.

Analyze existing solutions

Read negative reviews on G2, Capterra, or App Store to find what current tools are missing.

Create a waitlist/landing page

Before building, set up a simple page explaining the value prop to gauge interest and collect emails.

Define the target audience

Niche down as much as possible (e.g., instead of "marketers", target "freelance SEO copywriters").

Define the core use case

Identify the single "Aha!" moment where the user realizes the value of your AI tool.

Deep competitive analysis

Analyze top 3 competitors: their features, pricing, UX, and identify your unique differentiator.

Define the MVP

Strictly limit the initial version to 1-2 core features that solve the primary pain point.

Choose your development path

Decide between No-Code (Bubble/Make), Low-Code (FlutterFlow), or Custom Code (React/Node/Python).

Select AI Provider(s)

Choose APIs like OpenAI, Anthropic, Gemini, or open-source models via Replicate/Together AI.

Determine hosting & infrastructure

Options: Vercel/Netlify for simple apps, Render/Heroku for full-stack, or AWS/GCP for heavy scaling.

Database selection

Choose a standard DB (PostgreSQL/Supabase) for users, and optionally a Vector DB (Pinecone) if using RAG.

Design prompt architecture

Draft, test, and version-control your system prompts, user inputs, and few-shot examples.

Implement RAG (Optional)

If your AI needs custom knowledge, set up document parsing, embeddings, and vector search.

Set up data pipelines (Optional)

If fine-tuning is needed, build pipelines to clean and format training data.

Implement fallback mechanisms

Code fallbacks for when the AI API fails, times out, or returns malformed data (e.g., bad JSON).

Design intuitive onboarding

Show, don't just tell. Use templates or interactive guides to help users get their first successful result.

Implement loading states

AI takes time. Use streaming text, skeletons, or engaging spinners to keep users patient.

Build clear error handling

Provide graceful degradation and helpful messages when users hit rate limits or errors occur.

Add user feedback mechanisms

Include simple thumbs up/down or text feedback on AI responses to improve your prompts later.

Secure user authentication

Implement robust login (e.g., Clerk, Supabase Auth, Auth0) to protect user accounts and data.

Protect API keys

Never expose your OpenAI/Gemini keys in frontend code. Always route requests through your backend.

Draft legal documentation

Create Terms of Service and Privacy Policy, explicitly stating how user data interacts with AI models.

Implement content moderation

Use moderation APIs to prevent users from generating NSFW content, hate speech, or prompt injections.

Prepare marketing assets

Create a compelling demo video, high-quality screenshots, and use-case specific guides.

Set up analytics

Integrate tools like PostHog or Google Analytics to track user journeys and conversion rates.

Plan Product Hunt launch

Prepare your tagline, maker comment, and reach out to your community for support on launch day.

Leverage social media

Share your "build in public" journey on Twitter/X, LinkedIn, or relevant Reddit communities.

Monitor API costs closely

Track token usage per user to ensure your pricing model remains profitable.

Track system uptime

Set up alerts (e.g., Sentry, Datadog) for critical failures or API rate limit hits.

Analyze user drop-offs

Review analytics to see where users get stuck and refine the UX or onboarding flow.

Plan model upgrades

Stay updated with new AI models and plan migrations (e.g., moving to a faster/cheaper model).