What the client needed
hitdlaciebie.pl ("a hit for you" in Polish) is a personalized music gift service. A user describes who the song is for, the occasion, the mood — and receives a full original song: custom lyrics, AI-generated vocals, MP3 ready to share.
The product had to work end-to-end without manual intervention: user pays via Shopify, lyrics are generated, music is produced, file lands in the user's personal cabinet. All in Polish, all automated.
The engineering challenge was not building a form. It was building a reliable pipeline where three external AI services (LLM for lyrics, music API, Shopify for billing) hand off to each other correctly under real-world conditions: webhooks that arrive twice, CDN links that expire, test mode that silently does nothing.
Why one LLM is not enough for AI song generation
Single-model attempts failed in both directions. High temperature (creative mode): the lyrics were imaginative but structurally broken — wrong verse lengths, missing chorus markers, inconsistent rhyme scheme. Low temperature (structured mode): the lyrics were formatted correctly but flat and forgettable. The solution was a two-stage pipeline: creativity first, structure second.
Why two models instead of one
Suno V5.5 — the AI music generator used in production — requires specific formatting: [Verse], [Chorus], [Bridge] tags with consistent line lengths and rhyme patterns. Feed it inconsistent lyrics and the output breaks structurally.
Getting both creativity and structure from a single LLM call required temperature tuning that pulled in opposite directions. The fix was to stop trying.
↓
Gemini 2.5 Flash — temperature 0.8
↓ creative draft: vivid imagery, emotional language, raw ideas
Claude Sonnet 4.6 — temperature 0.35
↓ structural edit: enforces [Verse]/[Chorus] tags, rhyme scheme, line count
Final lyrics → Suno V5.5 API → MP3
Gemini 2.5 Flash at 0.8 handles the creative layer: it generates emotionally resonant raw content in Polish without worrying about format. Claude Sonnet at 0.35 handles the editorial layer: it takes the draft and enforces exactly the structure Suno expects.
Both calls go through OpenRouter, which means the same pipeline can swap models without changing the API integration. If a better Polish-language model appears, it slots in at stage one with a config change.
Tested four, shipped one
Choosing the wrong music API at launch means rebuilding the pipeline later. I tested all four candidates before committing to production. The architecture is provider-agnostic — all four remain registered. Swapping the active provider is a config change, not a rewrite.
| API | Polish vocals | Structure | Reliability | Decision |
|---|---|---|---|---|
| fal.ai / Minimax | Weak — vowel-heavy words sounded synthesized | Good | Stable | Rejected |
| ElevenLabs Music | Good timbre | No verse/chorus awareness — treats all lyrics as one block | Stable | Rejected |
| Udio | Promising quality | Good | Silent failures during testing | Rejected |
| Suno V5.5 | Natural Polish phonetics | Correct [Verse]/[Chorus] interpretation | Consistent | Shipped |
Three real production problems
/proxy-audio route that re-serves audio from the CDN on demand. The first version accepted any URL as input — a classic Server-Side Request Forgery (SSRF) hole. Fixed with strict URL.hostname validation: only *.suno.ai over HTTPS allowed. This broke legacy fal.ai URLs already stored in the database — the allowlist was extended for backward compatibility.orders/paid event can arrive twice. The result without protection: users get double the token credits, orders are processed twice, database records duplicate. Fixed with a unique constraint on shopify_order_id and ON CONFLICT DO NOTHING on insert. The pipeline became idempotent by design — re-running the same webhook has no effect after the first successful write.What shipped
- 2 weeks from first commit to live product
- 4 music generation APIs evaluated
- 2 LLM models in sequence for lyrics
- 3 real security and reliability issues caught pre-launch
- Full Shopify payments integration with HMAC verification
- Personal user cabinet with order history and audio playback
- Provider-agnostic music pipeline — swap API without rewrite
What made it work
- Two-model lyrics pipeline: creativity and structure as separate concerns
- OpenRouter as LLM gateway: model-agnostic, one API key
- Idempotent webhook processing: unique constraint on Shopify order ID
- SSRF-safe audio proxy: hostname whitelist, HTTPS-only
- Status tracking on every order: no silent failures
- Provider-agnostic music API layer: all four registered, one active
Tools used
I build full-stack AI applications from scratch: music generators, content pipelines, automation tools, SaaS products. Two weeks from brief to live. Based in Munich, working with clients across Europe.