SaaS Development Company in Austin

Music Technology / Entertainment

An Austin-based music technology company (Seed, $4.2 million raised) was building a platform to help independent artists and small labels track and collect music royalties across streaming platforms, radio, and sync licensing. The problem: independent artists lose an estimated 25% of their earned royalties because the music rights ecosystem is fragmented and opaque. An artist's song generates royalties from 12+ sources (Spotify, Apple Music, YouTube, Amazon Music, radio airplay, film/TV sync, cover versions, samples, live performance, and more), and each source has different reporting formats, payment timelines, and accounting methods. The prototype tracked Spotify and Apple Music but nothing else. Problems: data fragmentation (each royalty source reports in a different format — Spotify provides daily streaming data via API, radio airplay comes from SoundExchange quarterly, sync licensing is tracked by publishers manually), matching failures (the same song might be identified differently across platforms — different ISRCs, different metadata, and different spellings — causing royalties to be allocated to the wrong rights holder), payment timing (some royalties arrive in 30 days, others in 6+ months — artists had no visibility into expected future payments), and split management (songs have multiple rights holders — songwriter, performer, producer, label — and royalty splits vary by song and by income source). The company needed 500 artist accounts within 12 months.

We built a comprehensive music rights and royalty SaaS with multi-source data ingestion, AI-powered matching, payment forecasting, and split management. (1) Multi-source royalty ingestion: connecting to all major royalty sources. Streaming platforms: direct API connections to Spotify for Artists, Apple Music for Artists, Amazon Music for Artists, YouTube Music, Tidal, and Deezer — ingesting daily or weekly streaming data. Digital distributors: integration with DistroKid, TuneCore, CD Baby, and other distributors — importing the royalty data they collect from platforms the artist doesn't have direct access to. Performance rights: SoundExchange (digital radio royalties), ASCAP, BMI, and SESAC (public performance royalties) — importing quarterly statements and matching them to the artist's catalogue. Sync licensing: manual and semi-automated import of sync licence agreements and payments — film, TV, advertising, and video game placements. Publishing: import of publishing royalty statements from publishers and publishing administrators. Social media: TikTok, Instagram Reels, and YouTube Shorts — tracking the use of the artist's music in user-generated content and the associated royalties. Data normalisation: all royalty data normalised into a consistent format — platform, territory, play count, revenue, and time period — regardless of source format. (2) AI-powered matching: resolving identity issues across platforms. Song matching: using ISRC, ISWC, and fuzzy matching on song title, artist name, and album to match royalty entries to the correct song in the artist's catalogue. The AI model handles: title variations ("Don't Stop" vs. "Dont Stop" vs. "Don't Stop [feat. Artist B]"), metadata errors (incorrect ISRCs — surprisingly common), and cover versions (distinguishing the artist's original from covers, which generate separate royalty streams). Rights holder matching: matching royalty payments to the correct rights holder — particularly complex for songs with multiple rights holders where different sources pay different parties. Unmatched royalty detection: identifying royalty payments across the ecosystem that should be flowing to the artist but aren't — "We found $2,400 in unclaimed royalties from YouTube that weren't being collected because of a metadata mismatch." (3) Payment forecasting: predicting future income. Historical trend analysis: based on streaming trajectory, seasonal patterns, and release cycle — predicting royalty income for the next 3, 6, and 12 months. Release impact modelling: when an artist releases new music, the model projects the impact on streaming and royalty income based on the artist's historical release patterns and comparable artists. Sync pipeline: tracking sync licensing pitches and their probability of closing — weighted pipeline showing expected sync income. Cash flow calendar: showing when payments are expected from each source — "SoundExchange Q2 payment expected June 15: $3,200" — giving artists visibility into their cash flow. (4) Split management: handling complex multi-party royalty splits. Song-level splits: each song can have different splits for different income streams — "Recording royalties: 50% artist, 30% producer, 20% label. Publishing royalties: 60% songwriter A, 40% songwriter B." Automatic split calculation: when royalties arrive, the platform automatically calculates each rights holder's share and generates payment records. Split agreements: digital split agreements that all parties sign — creating a clear, auditable record of who gets what. Dispute resolution: when split disagreements arise (common in collaborative music), the platform provides clear documentation of the agreed splits and the supporting royalty data.

Artist adoption: 840 artist accounts in 12 months (68% above target). Artists range from independent solo artists (1,000 monthly listeners) to small labels managing 50+ artists. Total catalogue managed: 45,000 songs across all accounts. Recovered royalties: the platform identified $1.8 million in uncollected or misallocated royalties across all artists in year one — an average of $2,143 per artist. The largest single recovery: $34,000 for an artist whose publishing royalties were being paid to a former publisher who no longer represented them. AI matching accuracy: 96.4% of royalty entries matched to the correct song automatically — the remaining 3.6% flagged for manual review. False positive rate: 0.3% (mismatched royalties caught by artist review). The matching reduced manual reconciliation time by 89% compared to spreadsheet-based tracking. Payment forecasting: 3-month forecasts accurate within ±8% (compared to ±30-40% when artists estimated manually). Artists report that income predictability changed their financial planning — "For the first time, I can budget like a normal person because I know what's coming." Split management: 12,000 split agreements created on the platform, managing payments to 3,400 unique rights holders. Split disputes decreased 78% among artists using the platform (the digital agreements and transparent data eliminate the ambiguity that causes disputes). Artist satisfaction: NPS +68. The most valued features: unmatched royalty detection ("finding money I didn't know I was owed"), payment forecasting ("finally understanding my cash flow"), and the unified dashboard ("seeing everything in one place instead of logging into 12 different platforms"). Series A: the 840 artists and $1.8M in recovered royalties contributed to a $15 million Series A. The lead investor's thesis: "The infrastructure layer for independent music economics." Development investment: $290,000 over 14 weeks.