AI Disclosure

Last updated: May 24, 2026

Safi uses artificial intelligence to analyze the music you upload. This page explains what AI does, what it doesn't do, and how your data is handled.

What Safi's AI does

Safi's analysis pipeline uses one large language model plus multiple commercial music data sources to produce a similarity report:

Language model (the analysis brain):

• Anthropic Claude — the AI that reads your lyrics, weighs results from the databases below, applies copyright doctrine (substantial similarity, idea/expression, de minimis, scènes à faire), and produces the scored report.

Sources that examine your submission:

• AudD — commercial audio fingerprinting against released recordings. The strongest signal Safi has, and it requires you to upload audio.
• AcoustID — open-source audio fingerprint matching backed by MusicBrainz. A second, independent read on the same question.
• ACRCloud — scans for your melody inside released works, including covers and re-recordings that no fingerprint would catch.
• Genius — distinctive phrases from your lyrics are searched against Genius. Genius returns song titles and links; it does not return lyric text, so a result is a candidate worth reading, not proof that two sets of words overlap.

Used after something has been identified:

• MusicBrainz — once a recording has been identified, MusicBrainz is asked what underlying composition it belongs to, and who is credited.

Looked up by title only, and never treated as similarity evidence:

• Spotify, Discogs, Deezer, Last.fm, WikiData — searched on your song's name. These tell you what is already released under the same title, which affects whether people can find your song. They see neither your audio nor your lyrics, they are not shown to the AI that writes your report, and they can never raise a risk score.

Not similarity checks:

• Hooktheory — a chord-progression reference table, used for context about how common a progression is.
• OpenAI Moderation — safety classifier checking submitted content against abuse policies, entirely separate from copyright analysis.

Not every source runs on every analysis, and some need credentials that may not be configured at a given moment. Every report lists what was checked and what was not, because “nothing was found” and “nothing was looked at” are different answers.

The output is a similarity report with risk scores, cited matching recordings (if any), recommended fixes, and a side-by-side comparison panel when a match is found.

What Safi's AI does NOT do

• Safi does not make legal determinations. A clean report is not a guarantee that your work is free from infringement claims.
• Safi does not register, file, or transmit your work to copyright registries.
• Safi does not store your audio long-term by default (see Data Handling below).
• Safi does not use your songs to train AI models.

Data handling

Lyrics — text you submit is sent to Anthropic's API for analysis. Anthropic processes this data per their privacy policy and does not use it to train their models. Your lyrics are stored in your Safi account so you can revisit reports.

Audio files — when you upload audio, the file is sent to three fingerprinting services that examine the recording itself: AudD, AcoustID and ACRCloud. After the analysis finishes, the uploaded file is deleted — unless you ticked “Keep my file for 30 days” when you uploaded it, in which case it is stored for 30 days so you can re-run the check, then deleted. The match data those services returned stays in your report either way, so you can revisit the results after the audio is gone.

Reports — your generated similarity reports are stored in your account so you can access them later. You can delete individual reports at any time from your dashboard.

Known limitations — what Safi sometimes gets wrong

Safi is a fast first-pass safety filter, not a copyright lawyer. Like every AI system, it can be wrong. These are the specific failure modes we have observed and are actively working to reduce. We surface them here so you can read every report with the right context.

Song titles are not copyrightable

US Copyright Office (37 CFR § 202.1) is explicit: “Names, titles, and short phrases or expressions are not subject to copyright protection.” Hundreds of songs legally share titles — there are 30+ songs called “Tonight,” many “Hold On”s, multiple “Free Fallin’”s. If Safi flags your song because the title matches a known release, but the melody, vocal, and lyrical substance are different, that is not infringement. We have explicit prompt rules to score these as low-risk; if you see a high score driven purely by a title match, please report it.

Audio fingerprint matches can fire on production texture

AudD’s commercial fingerprinter is excellent but it matches on acoustic features — tempo, key, instrumentation, production texture — not just on copied melody. A fingerprint hit on a song you’ve never heard can mean the engines share a chord progression, a Brazilian percussion groove, or a similar mix — none of which is protectable. If Safi reports an AudD match but you listen at the matched timecode and can’t hear your song in theirs, the match is most likely production-texture overlap, not melodic borrowing. An AudD match on its own raises the melody grade, so a production-texture hit can lift your report by itself. Until 16 September 2026 a catalogue search returning the same title also counted towards confirming it; it no longer does, because a different song sharing a name is not evidence about a fingerprint.

Database matches mean “recognizable,” not “protected”

When multiple databases (Spotify, Last.fm, Deezer, MusicBrainz, Discogs) all return the same matched title, it just means that song exists and is indexed in many places. It does not mean the matched song’s phrase or hook is legally protected against your reuse. Safi does not treat several databases returning the same title as evidence about your audio, because none of them heard it.

Genre conventions and cultural language

Brazilian gospel, sertanejo, MPB, K-pop, reggaeton, country, and many other genres have shared vocabularies — scripture references, common metaphors, standard chord progressions, characteristic instrumentation. Safi has rules to ignore these as scènes à faire (genre conventions), but the model occasionally over-flags. If your song uses common cultural or genre language and Safi scores it high, please listen to the cited match before changing anything.

The model has a knowledge cutoff

The underlying language model has a training cutoff, meaning very recent releases or extremely obscure works may not be cited accurately. Database lookups partially compensate for this, but Safi cannot guarantee coverage of every song ever released.

What to do when Safi seems wrong

• Listen to the cited song — your report includes direct Spotify, Apple Music and Deezer links, wherever the provider returned one. On the report page the Spotify and Apple Music links open at the matched timecode; the Deezer link and the links in your PDF open at the start of the track. If you don’t hear your song in theirs, trust your ears.
• Re-run the analysis — if you checked “Keep my file for 30 days,” you can re-run the same audio against an updated pipeline as we refine the model.
• Consult an attorney for high-stakes releases — a sync deal, major-label release, or commercial campaign should always involve qualified entertainment counsel, regardless of what Safi says.
• Email us — support@akilitech.io if a score looks wrong to you. We use real cases to calibrate the model.

We will keep updating this list as we learn more from real reports. Safi’s scoring reflects what the AI detected against the databases it has access to — AI systems can produce false positives (flagging similarity where none exists in fact) and false negatives (missing real similarity). Always review the cited works manually before making release decisions on tracks that score in the higher-risk ranges.

Your rights

You can:

• Delete your reports at any time from your account dashboard
• Request full account deletion by emailing support@akilitech.io
• Receive a copy of the data Safi has about you (data export) on request

Changes to this disclosure

We may update this AI Disclosure when we add new AI features, change models or providers, or alter how we handle your data. The "Last updated" date at the top reflects the most recent change.

Contact

Questions about how Safi uses AI? Email support@akilitech.io.