Complete AI Training

AI app for creatives · no coding needed

Mood-matched playlist curation and music library console

Reduce manual playlist assembly while keeping mood inputs and licensing records under the buyer's control.

Made for: Music supervisors, wellbeing programme leads and playlist curators producing mood-matched listening for teams or clients

What Mood-matched playlist curation and music library console looks like
Open the demo For members · a working demo with sample data

What it does for you

The problem

Mood-matched playlists are assembled by hand across several rented tools, and mood inputs, listening history and licensing records sit in separate places.

What it gives you

Curator-approved mood-matched playlists linked to a rights record

What you give it

Permitted mood inputslistening historycatalogue metadatalicensing constraints

Build your own version of mood2music, Otto AI and more

One app with what these 9 AI tools do, yours to keep and change: mood2music, Otto AI, Jammy Chat, FaceTune.ai, PlaylistAI, Fadr, findmusic.ai, Knoiz, Jammy.

Everything these tools do, in one app

  • Mood-based music generation Creates music playlists that match the user's current emotional state.Found in mood2music, Jammy Chat, FaceTune.ai and 2 more
  • Automatic playlist creation Generates complete playlists automatically without manual song selection.Found in mood2music, Jammy Chat, FaceTune.ai and 2 more
  • Mood detection Analyzes user input or cues to determine the user's current mood.Found in mood2music, Jammy Chat, FaceTune.ai
  • Facial expression recognition Uses a selfie or camera to assess the user's mood from facial expressions.Found in Jammy Chat
  • Real-time emotion analysis Continuously analyzes emotional state to tailor music recommendations in real time.Found in FaceTune.ai
  • Multi-input curation Creates playlists from various inputs such as text prompts, images, videos, or listening history.Found in PlaylistAI
  • Prompt-based playlist generation Generates playlists from simple text prompts describing a mood, theme, or activity.Found in PlaylistAI
  • Personalized recommendations Suggests music based on the user's listening habits and preferences.Found in Fadr, findmusic.ai, Jammy
  • Swipe-based discovery Allows users to swipe right to add songs or left to skip, making music discovery interactive.Found in Fadr
  • Streaming platform integration Connects with popular music streaming services for seamless listening and library updates.Found in Fadr, findmusic.ai
  • Wide music library Provides access to a large and diverse collection of songs across many genres.Found in mood2music, FaceTune.ai, Fadr and 2 more
  • Playlist customization Allows users to fine-tune playlists by adjusting mood intensity, music style, or other parameters.Found in mood2music
  • Privacy protection Ensures user data, such as facial images, are not stored to protect privacy.Found in Jammy Chat
  • Mental wellness support Uses music to help users reflect, reset, or uplift their mood for emotional wellbeing.Found in Jammy Chat
  • Productivity enhancement Aims to boost productivity and emotional balance through tailored music choices.Found in FaceTune.ai
  • New music discovery Helps users find new songs and artists aligned with their moods or tastes.Found in FaceTune.ai, PlaylistAI, Fadr and 1 more
  • Regular content updates Keeps the music database fresh with regular updates for diverse selections.Found in findmusic.ai
  • Progress tracking Tracks user progress and provides personalized recommendations over time.Found in Jammy

How it works, step by step

  1. Capture mood from text, image, video or listening history
  2. Detect mood from typed input and optional selfie cues
  3. Track emotional state across a session to adjust suggestions
  4. Generate a complete playlist from a short mood or activity prompt
  5. Suggest tracks from listening habits and stated preferences
  6. Support swipe right to add and swipe left to skip
  7. Adjust mood intensity, style and tempo parameters
  8. Connect to streaming services for playback and library updates
  9. Search a wide multi-genre catalogue with metadata
  10. Surface new tracks and artists aligned to the mood
  11. Refresh catalogue entries on a regular update cycle
  12. Track curation progress and preference changes over time
  13. Compare the reviewed result with the recorded baseline and value assumptions
  14. Capture corrections and named-owner approval before consequential use
  15. Export a versioned curator-approved mood-matched playlists linked to a rights record with source references and unresolved questions

Build it yourself with your AI system

Build this app yourself, no coding needed

Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.

Sign in to see how to build it yourself

Build a quick version to try, or get the full app pack for Mood-matched playlist curation and music library console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.

Sign in Become a member

4 Have it built for you days to a few weeks

Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Mood-matched playlist curation and music library console with you.

Have Nexibeo build it

What's in the app pack

Included in the Complete AI Training membership.

  • The building instructions your AI follows, step by step
  • The questions your AI will ask you about your business before it starts
  • A clickable demo you can open in your browser, to see how it should work
  • A detailed blueprint of the screens, the information it keeps and the checks it runs

Become a member to get the app packAlready a member? Sign in

The files, for the technically curious
  • START-HERE.mdHow to build it with your own AI (read first)3 KB
  • README.mdOverview and links4 KB
  • questions.mdQuestions to answer before you build2 KB
  • prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare27 KB
  • prompt-vps.mdThe same build on your own server (Docker)27 KB
  • spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
  • demo/index.htmlThe working demo on sample data198 KB

Questions

Do I need to know how to code?

No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.

What does it cost?

The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.

How long does it take?

The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.

Can I change it to fit my business?

Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.

More detailsHow the AI works, safeguards and what to build first

Reduce manual playlist assembly while keeping mood inputs and licensing records under the buyer's control. For music supervisors, wellbeing programme leads and playlist curators producing mood-matched listening for teams or clients, convert permitted mood inputs, listening history, catalogue metadata and licensing constraints into curator-approved mood-matched playlists linked to a rights record. The benefit is a testable hypothesis, measured through accepted playlists per curation hour and corrections after playlist approval; do not assume that AI output alone produces business value.

Confirm the buyer's problem and scope, collect permitted mood inputs, listening history, catalogue metadata and licensing constraints, then follow this sequence: 1. Capture mood from text, image, video or listening history. 2. Detect mood from typed input and optional selfie cues. 3. Generate a complete playlist from a short mood or activity prompt. Resolve uncertain cases with qualified reviewers, approve curator-approved mood-matched playlists linked to a rights record, and measure accepted playlists per curation hour and corrections after playlist approval against a documented baseline.

How the AI works

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Facial images are processed for mood cues only and are not stored; final licensing and wellbeing checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

Safeguards

Preserve mood input consent, source attribution, licensing accuracy and usage permissions. Curators approve substantive changes and publication scope. One streaming destination and one licensed catalogue; facial images are processed for mood cues only and are not stored; final licensing and wellbeing checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.

What to build first

Pilot scope: One streaming destination and one licensed catalogue; facial images are processed for mood cues only and are not stored; final licensing and wellbeing checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: capture mood from text, image, video or listening history; detect mood from typed input and optional selfie cues. Support the third module with operator review: generate a complete playlist from a short mood or activity prompt. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

What it can connect to

Buyer-owned listening history, permitted mood inputs and licensed catalogue metadata. Cloud asset storage, streaming service import/export and playlist destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

The screens in detail

Primary screens: Mood intake and references, Editable playlist preview, Client proof and delivery. Use a thumbnail gallery for playlists, a large central editing canvas, and a right-hand panel for mood inputs, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant track. Make the task-specific outcome curator-approved mood-matched playlists linked to a rights record visible beside its evidence, review state and value baseline.