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How to Build an AI Product

A practical guide to building AI-wrapper products, with a real case study on building TranquAI — an AI-powered meditation app.

The AI industry is booming, but you don’t need to manufacture chips or train foundation models to be part of it. There’s a growing role that sits between AI model creators and end users: the AI-Wrapper Product Builder.

The AI Industry Landscape

The AI ecosystem can be broken down into three layers:

  • Hardware Manufacturers — NVIDIA, AMD, Intel, Google — building the GPUs and TPUs that power AI
  • AI Model Creators — OpenAI, Anthropic, Google, Meta — training the large language models and other AI systems
  • Users — everyone else consuming AI through ChatGPT, Gemini, and similar tools

Creating new hardware is near impossible for most of us. Training a new foundation model is also extremely hard. But there’s a sweet spot in between: building products that wrap AI models into focused, valuable experiences.

What is an AI-Wrapper Product?

A product that wraps AI models inside an application layer with UI/UX tailored to a specific use case.

Think of AI as a multi-purpose 3rd-party service: it can do almost anything, but it’s not specialized in anything. Our job isn’t to build the AI. It’s to make it perform one thing extremely well by giving it structure, constraints, and UX around it. This is what “wrapping AI” actually means.

What’s Already on the Market?

Here are some successful AI-wrapper products that demonstrate this concept:

PhotoAI.com

  • Wrapper: Web Application
  • AI Models: Image generation
  • UI/UX: Simple flow to upload selfies and generate themed photos
  • Use Case: Create new, high-quality photos of yourself in any style or scenario

Cal AI

  • Wrapper: Mobile App
  • AI Models: Image recognition
  • UI/UX: Simple interface to capture or upload food photos, review detected items, and log nutrition
  • Use Case: Automatically estimate calories and nutrients from a photo, making food tracking fast and effortless

Plant Identifier Apps

  • Wrapper: Mobile App
  • AI Models: Image recognition
  • UI/UX: Simple interface to capture plant photos and get information about the plant
  • Use Case: Identify plant species, diagnose plant health issues, and get care recommendations directly from a photo

Daily Friend AI

  • Wrapper: Mobile App
  • AI Models: Language Model, Voice Recognition, Voice generation
  • UI/UX: Conversational interface designed for daily emotional check-ins, guidance, and supportive reflections
  • Use Case: Provide a friendly, supportive AI companion for daily conversations, emotional wellness, and personal growth

Each of these products takes a general-purpose AI capability and wraps it in a focused experience that delivers real value.

How I Built One: TranquAI

To demonstrate the process, I built TranquAI — an AI-powered meditation app that generates personalized guided meditation sessions with voice narration.

  • Wrapper: Mobile App
  • AI Models: Language Model, Voice generation
  • UI/UX: Calm, guided flow to select mood or purpose, generate meditation sessions, and listen to personalized audio
  • Use Case: Create personalized guided meditation sessions tailored to each user’s mood, intention, and emotional state

Building TranquAI required wrapping two AI models: an LLM to generate meditation scripts, and a voice generation model to narrate them.

Step 1: Choosing an LLM Provider and Model

The first decision is which LLM provider and model to use. The main providers I evaluated were:

  • OpenAI — GPT-5, GPT-4.1, and their variants
  • Google Gemini
  • Meta — open-source models

Each provider offers multiple models with different trade-offs between quality, speed, and cost. For meditation script generation, quality and tone matter more than raw speed.

Step 2: Crafting the System Prompt

The system prompt is the foundational instruction that shapes the AI’s behavior. It sets the role (meditation coach), the tone (calm, soft), the structure (intro, breathing, visualization, closing), and the guardrails (no medical claims, no harsh language). This keeps the output consistent across all users.

Getting the system prompt right took multiple iterations:

Iteration 1: Set a role

You are a compassionate and calming meditation coach.

Write a guided meditation that fits a total playback time of
exactly ${duration} minutes at a slow, mindful pace.

Use simple, soothing, sensory-rich language suitable for audio
narration.

Iteration 2: Give structure

The meditation script should:

- Be appropriately paced and worded to fill the exact duration
  in minutes provided by the user, based on a slow, mindful
  narration speed.
- Start with a warm welcome and intention setting based on the
  user's purpose.
- Guide the listener through slow, deep breaths.
- Include a full-body relaxation scan (head to toe).
- Offer gentle visualizations or metaphors relevant to the
  user's purpose (e.g., calming ocean for stress, floating
  clouds for sleep).
- Include affirmations or reflections aligned with the purpose.
- Include a story or lesson aligned with the purpose if possible.
- End with a soft return to awareness, or transition into sleep
  or stillness if appropriate.

Iteration 3: Set duration constraints

Hard length rules (must follow)

- Target ~${TARGET_WORDS} total words (+-5%).
- If content is too long, remove some part of it.

The target word count is precalculated: duration_in_minutes * 85. This ensures the generated script matches the requested meditation length when narrated at a slow, mindful pace.

Iteration 4: Output format

Output only the full text of the meditation script, consistent
in length with the requested duration. Do not include any
explanations or formatting outside the script itself.

Iteration 5: Provide examples

Including a concrete example in the system prompt dramatically improved output quality. Here’s a snippet from the example for a 2-minute stress relief script:

Welcome. Settle in comfortably. <break time="2s"/>
Let your eyes close gently. <break time="2s"/>
Take a deep, slow breath in <break time="3s"/> and softly
release. <break time="3s"/>
Let your body start to relax. <break time="2s"/>
...

The final input prompt to the user is simple:

Create a ${duration}-minutes guided meditation script for the
following purpose: ${purpose}

Step 3: Choosing a Voice Generation Provider

With the meditation script generated, the next step is converting it to audio. I evaluated three providers:

  • ElevenLabs
  • OpenAI
  • Google

For each provider, you need to select a voice actor and a model. ElevenLabs offers actors categorized by style (Narrative & Story, Conversational, Entertainment) and models like Eleven Multilingual v2 for high-quality, emotionally rich output.

Iteration 6: Guidelines for the voice model

The LLM output needs to be formatted specifically for the voice model. I added strict formatting rules to the system prompt:

Strict formatting and style rules:
- Do NOT use ellipsis (...) anywhere in the text.
- Do NOT place ellipsis (...) and <break> tags in the same
  sentence or in adjacent sentences.
- Use only single <break time="(n)s"/> tags to indicate pauses,
  with n up to 3 seconds.
- Do NOT stack or repeat multiple <break> tags to create longer
  pauses.

These rules prevent the voice model from producing awkward pauses or unnatural speech patterns.

Step 4: Building the UI/UX

The final layer is the user interface — this is where the “wrapper” comes together. TranquAI’s UI guides users through a simple flow:

  1. Choose a category — Stress & Emotions, Energy & Daily Use, Personal Growth, or Custom Purpose
  2. Choose a purpose — Predefined options like “Relieve Stress” or “Reduce Anxiety”, or write a custom purpose
  3. Choose a duration — Quick Reset or Standard Session
  4. Choose a voice — Select from available voice actors with preview
  5. Generate and listen — The app generates the script, converts it to audio, and plays it back

Users can also browse their library of previously generated custom meditations and replay them anytime.

Conclusion

Yes, it’s just an AI wrapper. But there is still meaningful work to be done — from iterating on system prompts, to selecting the right models, to designing a focused user experience. And most importantly, there is real value to deliver to users.

The opportunity for AI-wrapper products is wide open. Pick a use case, wrap the right AI models around it, and build something people actually want to use.