MasterPulse

Master Suno tracks for release-ready streaming in minutes.

MasterPulse is an AI-assisted mastering workspace for tracks created in Suno, designed for independent artists and creators who want a fast path from AI-generated demo to distribution-ready release. It analyzes the mix, applies mastering presets and intelligent adjustments, and exports polished versions for music platforms with consistent loudness, clarity, and codec-safe headroom.

Business Goals

  • Reach 2,000 monthly active users within 6 months of launch.
  • Convert at least 8% of free users to paid plans within 90 days of first upload.
  • Maintain a monthly churn rate below 6% for paid subscribers by month 6.
  • Achieve 35% of new signups uploading a first track within 10 minutes.
  • Generate 25% of new user acquisition through organic sharing and referral links by month 9.

User Goals

  • Upload a Suno-generated track and receive a release-ready master quickly.
  • Hear multiple mastering styles before committing to one version.
  • Get platform-safe loudness and headroom without learning audio engineering.
  • Export WAV, FLAC, and streaming-optimized MP3 versions with clear naming.
  • Store favorite mastering presets for future songs and albums.

Non-Goals

  • Not a full DAW or multitrack editor.
  • Not a stem-separation or remixing tool in v1.
  • Not a music distribution service for submitting to Spotify, Apple Music, or other stores.
  • Not a human-in-the-loop mastering marketplace in the initial release.

Indie Creator Alex, 29 - Alex makes songs in Suno and posts them to TikTok and streaming platforms. They need a simple way to make the track sound more polished without hiring a mastering engineer.

Indie Creator Alex, 29

  • As a creator, I want to upload my Suno export and get mastering suggestions, so that I can release tracks faster.
  • As a creator, I want to compare mastered previews side by side, so that I can choose the best sound for my song.
  • As a creator, I want one-click exports in streaming-ready formats, so that I can upload to distribution services without extra steps.

Content Studio Maya, 38 - Maya manages a small content team producing branded music for social campaigns. She needs consistency across many tracks and a repeatable workflow.

Content Studio Maya, 38

  • As a studio manager, I want saved preset chains, so that every brand track has the same sonic character.
  • As a studio manager, I want batch mastering for multiple songs, so that I can process a campaign efficiently.
  • As a studio manager, I want project notes and version history, so that my team can track revisions.

Hobbyist Ben, 22 - Ben uses Suno casually and does not understand compression, EQ, or loudness standards. He wants the product to make good decisions for him.

Hobbyist Ben, 22

  • As a beginner, I want the app to explain why a master sounds better, so that I can learn over time.
  • As a beginner, I want a recommended default master, so that I do not have to tune technical settings.
  • As a beginner, I want warnings when my track is too loud or clipped, so that I avoid bad uploads.

Track Upload and Analysis · High priority

  • Users can upload Suno-generated audio or import from supported sources, then receive an automatic analysis of the file before mastering begins.
  • Support WAV, FLAC, AIFF, and MP3 up to 200 MB in v1.
  • Detect sample rate, peak level, integrated loudness, dynamic range, clipping, and stereo balance.
  • Flag obvious issues such as clipping, DC offset, and very low input gain before processing.
  • Persist an analysis summary so the user can compare before/after results.
  • Reject unsupported or corrupted files with a clear error and retry path.

AI Mastering Chain · High priority

  • The system generates an intelligent mastering chain tailored to the track, using a combination of deterministic audio processing and AI-driven recommendations.
  • Use loudness normalization targets for streaming and a separate loud export target.
  • Apply EQ, multiband compression, saturation, stereo widening, limiting, and dithering where appropriate.
  • Choose mastering presets based on genre inference, spectral balance, and dynamics profile.
  • Let the user select from automatic, warm, bright, club, and vocal-forward styles.
  • Produce a dry-run preview and a final rendered file for comparison.

Preview and Approval · High priority

  • Users can audition multiple masters before exporting and can approve a final version with clear quality indicators.
  • Generate A/B previews with matched loudness to avoid misleading comparisons.
  • Show a simple quality score for loudness, clarity, and clipping risk.
  • Allow reprocessing with one-click style changes or intensity sliders.
  • Save a version history of every render with timestamp and settings.
  • Surface a warning when export targets may reduce quality below acceptable thresholds.

Export and Delivery · Medium priority

  • Users can export mastered audio in the formats needed for streaming and archival delivery.
  • Export WAV for archival and FLAC/MP3 for convenience.
  • Generate platform-safe versions with metadata intact when possible.
  • Provide file naming templates with artist, track name, version, and date.
  • Allow download links to expire after a configurable period.
  • Support background rendering for long files and notify the user when ready.

Accounts, Projects, and Presets · Medium priority

  • Users can organize tracks, save reusable mastering preferences, and return to prior work quickly.
  • Create projects with track name, genre, reference track, and notes.
  • Save presets as personal, team, or project-scoped.
  • Store reference tracks and preferred loudness targets per project.
  • Provide search, sort, and status filters for completed and pending tracks.
  • Support team collaboration on shared workspaces in a later phase.

Quick Start Onboarding

  • Sign up with email or Google in under 30 seconds.
  • Create a first project and name the track.
  • Upload a Suno export or drag and drop a file.
  • Choose an auto master or one of four styles.
  • Hear a preview in under 3 minutes for a typical 3 to 4 minute track.
  • Download the mastered file and see an export checklist for streaming readiness.

1. Upload and Inspect

  • The user uploads a generated song and the system validates the file before processing begins.
  • Check file type, duration, size, and corruption.
  • Show a clear error if the file is unsupported or too short for mastering.
  • Display input analysis with peak, loudness, and clipping indicators.

2. Choose Mastering Intent

  • The user selects an automatic path or a genre/style preset that guides the mastering chain.
  • Offer defaults like streaming, warm, bright, club, and vocal-forward.
  • Explain the expected effect of each style in plain language.
  • If no genre is detected confidently, fall back to a general-purpose preset.

3. Generate Preview

  • The engine creates a short preview and a full render candidate so the user can compare output safely.
  • Match preview loudness to the original for honest A/B testing.
  • Retry automatically if rendering artifacts or clipping are detected.
  • Show progress and estimated wait time for files over 5 minutes.

4. Review and Refine

  • The user listens, compares, and optionally tweaks mastering intensity or switches styles.
  • Provide before/after toggles and quick reprocess options.
  • If the user raises intensity too high, surface a warning about pumping or harshness.
  • Keep prior versions so the user can revert instantly.

5. Export Release Master

  • The user downloads the approved master in the chosen format with correct naming and metadata.
  • Offer WAV, FLAC, and MP3 exports with configurable loudness targets.
  • Include file integrity checks and a completed status indicator.
  • If export fails, preserve the render and provide a resumable retry.

Power User and Edge Handling

  • Custom mastering chains with ordered modules and per-module intensity.
  • Reference track matching to approximate tonal balance and loudness.
  • Batch processing for albums, EPs, and social campaign packs.
  • Version comparison with waveform, loudness, and spectral views.
  • Team workspaces with shared presets and approval notes.
  • Automatic detection of Suno-specific artifacts such as overly bright highs or synthetic transients and compensating correction suggestions.

Interface Principles

  • Simple upload-first layout with one dominant call to action.
  • Waveform, loudness, and clipping indicators that are easy to scan.
  • Accessible color contrast, keyboard controls, and screen-reader labels for all media controls.
  • Responsive playback controls optimized for desktop and mobile review.
  • Fast perceived performance with progressive rendering and non-blocking background jobs.
  • Clear status states for queued, analyzing, rendering, ready, failed, and expired.

Alex finishes a Suno-generated track at midnight and wants to release it the next day. Instead of guessing at EQ and limiting, Alex uploads the file into MasterPulse, selects a streaming-ready master, and hears an immediate preview with balanced loudness and cleaner highs.

The system detects that the original track is slightly hot and overly bright, then recommends a gentler chain with controlled compression and a safer limiter ceiling. Alex approves the result, downloads a WAV and MP3, and uploads the master to a distributor the same night.

For Alex, the value is speed and confidence. For the business, the value is repeat usage: a creator who can turn Suno ideas into polished releases without hiring external help is much more likely to come back for every new song.

User-Centric Metrics

  • 85% of uploaded tracks receive a usable preview within 3 minutes for average-length songs.
  • 70% of users rate the first master as acceptable or better without needing more than one revision.
  • Average time from upload to final export under 8 minutes for tracks under 5 minutes long.
  • 60% of users save at least one preset within their first 30 days.
  • Average export success rate above 98% with no audio corruption complaints.

Business Metrics

  • 8% free-to-paid conversion within 90 days.
  • 45% of paid subscribers remain active after 3 months.
  • 20% month-over-month growth in processed tracks during the first 6 months.
  • At least 30% of new signups return for a second track within 14 days.

Technical Metrics

  • 99.9% monthly uptime for upload, render, and download services.
  • Preview generation p95 latency under 180 seconds for standard tracks.
  • Final render p95 latency under 10 minutes for 5-minute tracks.
  • Zero tolerance for unauthorized access to stored audio assets; all storage encrypted at rest and in transit.

Tracking Plan

  • track_uploaded
  • analysis_completed
  • preset_selected
  • preview_generated
  • preview_played
  • export_requested
  • export_completed

Technical Needs

  • Frontend in Next.js with React and Tailwind CSS for fast product iteration.
  • Backend API in Node.js or Python FastAPI for job orchestration and user/project management.
  • Audio processing workers using FFmpeg, SoX, and Python libraries such as librosa and pyloudnorm.
  • Async job queue with Redis Queue, BullMQ, or Celery for rendering and preview generation.
  • Object storage on AWS S3 or compatible storage for original and mastered audio files.
  • PostgreSQL for users, projects, presets, jobs, and audit logs.
  • Observability via OpenTelemetry, Sentry, and centralized logs for render failures and latency monitoring.

Integration Points

  • Google OAuth and email authentication.
  • Suno upload import via file upload and optional future API-based import if available.
  • Stripe for subscription billing and usage-based overages.
  • AWS S3 or Cloudflare R2 for file storage and delivery.
  • Resend or SendGrid for render-complete and export notification emails.

Data Storage & Privacy

  • Encrypt audio files and metadata at rest and in transit.
  • Store only the minimum personal data required for account and billing operations.
  • Provide deletion flows for projects and audio assets to support GDPR and CCPA requests.
  • Separate user account data from media assets using access-controlled buckets and signed URLs.
  • Keep a clear retention policy for failed uploads, temporary previews, and deleted files.

Scalability & Performance

  • Use background jobs so long renders never block the API.
  • Cache analysis results and preset recommendations to reduce repeated computation.
  • Use chunked uploads and resumable transfers for large files.
  • Autoscale audio workers separately from web traffic during release spikes.

Potential Challenges

  • Suno-generated tracks may have synthetic artifacts that generic mastering does not handle well; mitigate by adding Suno-specific analysis heuristics and genre-aware presets.
  • Users may expect mastering to fix poor composition or arrangement; mitigate with clear scoping language and pre-flight warnings about source limitations.
  • Audio rendering can become expensive at scale; mitigate with preset-based defaults, job batching, and usage limits on free plans.
  • Preview comparisons can be misleading if loudness is not matched; mitigate by loudness-matched A/B playback and clear UI labeling.
  • File privacy is sensitive for unreleased music; mitigate with signed URLs, short-lived access tokens, and automatic cleanup of temporary files.

Team & resourcing - Small team - 2 engineers, 1 designer, part-time PM, and part-time audio specialist.

Phase 1: MVP Upload and Master · Weeks 1-4

  • User authentication and project creation
  • Audio upload and file validation
  • Basic analysis dashboard with loudness and clipping detection
  • Single automatic mastering preset
  • Rendered preview and downloadable master

Phase 2: Style Presets and Comparison · Weeks 5-8

  • Five mastering styles
  • A/B preview comparison
  • Version history
  • Saved presets
  • Improved error handling and job status UI

Phase 3: Export and Billing · Weeks 9-12

  • WAV, FLAC, and MP3 export options
  • Stripe subscriptions and usage limits
  • Email notifications for completed renders
  • Retention and deletion controls
  • Usage analytics and admin dashboard

Phase 4: Collaboration and Scale · Weeks 13-16

  • Team workspaces
  • Batch mastering
  • Reference track matching
  • Worker autoscaling and queue optimization
  • Accessibility pass and performance tuning

Paste this into Cursor, Bolt, Lovable, or v0 to start building.

Build a web app called MasterPulse for AI-assisted mastering of Suno-generated music tracks.

Product summary:
Users upload a single audio file created in Suno or another AI music tool, the system analyzes the track, recommends a mastering style, renders a polished master, and lets the user preview, compare, and export the final audio for streaming release.

Core features:
1. Authentication with email and Google OAuth.
2. Project dashboard with track list, status, and presets.
3. Upload flow for WAV, FLAC, AIFF, and MP3 up to 200 MB with resumable upload support.
4. Audio analysis pipeline that detects duration, sample rate, peak level, integrated loudness, dynamic range, clipping, DC offset, and stereo balance.
5. Mastering presets: automatic, streaming, warm, bright, club, vocal-forward.
6. Background rendering job that produces a preview and a final export using FFmpeg plus a Python audio analysis service.
7. A/B comparison player with loudness-matched playback and clear before/after labeling.
8. Export screen with WAV, FLAC, and MP3 download options, file naming templates, and version history.
9. Save and reuse personal presets.
10. Billing with Stripe for free and paid tiers.

Primary screens and flows:
1. Landing page with clear CTA to upload a track.
2. Sign in and onboarding with first project creation.
3. Upload and analysis screen with file validation and progress state.
4. Mastering configuration screen with preset selection and intensity controls.
5. Render status screen with queue, progress, and ETA.
6. Preview comparison screen with waveform and playback controls.
7. Export screen with download buttons and metadata summary.
8. Project dashboard showing history, saved presets, and completed masters.

Data model:
Users, Workspaces, Projects, Tracks, Uploads, AnalysisResults, MasteringPresets, MasteringJobs, RenderedFiles, PlaybackSessions, BillingPlans, UsageEvents.
Each track should belong to one project and have many analysis results and rendered versions. Each mastering job should track status, preset used, input file, output files, and processing logs.

Recommended stack:
Next.js 14, React, TypeScript, Tailwind CSS, shadcn/ui, PostgreSQL with Prisma, Redis queue with BullMQ, Node.js API routes or separate FastAPI audio service, S3-compatible storage, FFmpeg, librosa, pyloudnorm, Stripe, Google OAuth, Sentry, and OpenTelemetry.

Implementation requirements:
Use a clean, modern UI optimized for creators. Make upload and preview very fast. Use signed URLs for all audio access. Include empty states, loading states, failed render recovery, and accessible controls. Build the app so the audio pipeline runs asynchronously and the UI updates via job polling or websockets. Seed the app with realistic demo data and sample preset chains. Create all main pages, database schema, API routes, job worker, and UI components needed for an MVP.

Business Idea

Смотри, мой случай создания мастеринга с помощью искусственного интеллекта для выпуска на музыкальной площадке. И именно музыка сделана с помощью Suno, чтобы он анализировал и понимал. Ты сам составь полное, что там, через какие-то плагины, искусственные интеллекты обрабатывал, чтобы красиво все было.

Make My PRD

Design by The Resonance | Powered by GPC – The AI Transformation Company

    PRD: Смотри, мой случай создания мастеринга с помощью...