The cultural intelligence layer for AI.
Exclusively licensed. Expert-annotated. Consent-logged at the source.
Hear the Dataset.
Sample recordings from the corpus. Real maestros, real instruments, captured at 96kHz studio fidelity with musicological precision.
The Triple Crisis.
Indian classical music is one of the most complex tonal systems on Earth. And the most invisible to AI.
Cultural
Hindustani classical knowledge survives almost entirely through oral transmission from master to student, with no systematic archive built for computational study.
Creator
No commercially licensable, consent-logged corpus of Hindustani classical and Bollywood audio existed before RaagaPay. The musicians who hold the tradition had no seat in the AI value chain.
AI Bias
Models built on 12 equal-tempered tones cannot represent a 22-shruti continuous pitch system by design. The tonal information falls between the grid lines they use.
12 tones. Or 22 shrutis.
Today's AI hears a grid. A raag lives between the notes.
Rigid. Quantized. Culturally flat.
Fluid. Expressive. Alive between the notes.
Synthesized tones for illustration only. Real corpus recordings are in the Listening Room above.
Existing models are trained almost entirely on 12-tone Western material. Shruti, raag and tala structure falls outside what they were built to represent.
Maestro-verified, consent-logged, research-grade data built for the microtonal reality of the music.
See howThe 80-Point Annotation Framework.
We don't tag genre. We map what only trained musicians can hear.
Pitch Anchor
Human-verified tonic and shruti labels on every track.
Stylistic DNA
Gharana lineage documented per performance: Gwalior, Kirana, Agra and more.
Remix-Ready Stems
Vocals, sitar, tabla, harmonium, bansuri and sarangi, isolated and aligned.
See What's Inside.
Archival-grade recordings, annotated with the 80-point annotation framework.
Built on Integrity. Not Scraping.
Musicians paid upfront and on every use. You get clean, rights-cleared data.
Methodological Rigour
Every data point verified by trained musicians. Every process documented for reproducibility.
96kHz / 24-bit
Studio-grade fidelity. Every microtonal nuance preserved.
80-Point Annotation Framework
Raag, taal, gharana, laya, shruti, all annotated per track.
FAIR Compliant
Findable, Accessible, Interoperable, Reusable.
100% Artist Consent
Documented consent, attribution and royalty agreements.
Fair Trade AI
Upfront Pay
Paid immediately on recording.
Lifetime Royalties
Revenue share on every use.
Rights-Cleared
Full licensing, full consent, clean chain of title.
Cultural Credit
Lineage attribution, always.
Choose Your Licence.
Three structured tiers built for the realities of academic study, commercial AI development, and enterprise procurement.
Research Pilot Licence
A starter corpus for non-commercial study, citation and publication.
Commercial Development Licence
The full production corpus, cleared for commercial model training.
Enterprise Licence
Multi-year strategic access, co-branding and direct artist liaison.
Four Steps. One Ecosystem.
From the studio to the model to the maestro's pocket. A loop that pays back.
-
01
Record
A maestro performs in the studio, on their terms.
-
02
Preserve
Every nuance archived at 96kHz against the 80-point annotation framework.
-
03
Train AI
Partners train on authentic, fully cleared recordings.
-
04
Share Value
Royalties flow to the maestro on every use.
Scaling the Corpus.
From a verified pilot corpus to production-scale supply. Hindustani first, Carnatic in Phase 2.
Recorded, annotated end to end, and 100% consent-logged. Tabla 42, Bansuri 23, Harmonium 21, Sitar 18, Vocals 17, Sarangi 14.
135 studio tracks, the 80-point annotation framework applied end to end, ethical protocols live.
Corpus expansion underway, the artist roster growing under exclusive agreements, Carnatic recording begins.
Who's Behind RaagaPay
Debjit Mitra
Musician and entrepreneur at the intersection of Indian classical music and AI.
Background spanning Spotify, the BBC, Zee TV and Ableton.
Sets product, strategy and the artist-first model.
The Industry Is Listening.
Independent coverage of RaagaPay across music-industry press, legal analysis and AI commentary.
New Indian startup RaagaPay wants to fix AI's Hindustani classical music problem
Feature interview with founder Debjit Mitra on closing the Hindustani training-data gap.
Keeping Ownership Authentic: AI and Control in the Modern Matrix of Music
Cites RaagaPay's royalty-based dataset model among consent-forward approaches to AI training.
Global Cultures Are Expanding AI Music
A survey of builders teaching AI non-Western musical systems, with RaagaPay named for its rights-cleared Hindustani corpus and per-performance metadata.
Delhi startup RaagaPay builds the first ethical AI dataset for Hindustani classical music with lifetime artist royalties
On the 80-point annotation framework and the lifetime artist royalty model.
Frame: AI Music Isn't Taking Work. It's Moving It.
AI music culture and analysis on where the work, and the value, in music is actually moving.
RaagaPay in conversation: AI and Indian classical music
Video discussion featuring RaagaPay. The link opens at the segment, 48:34.
Writing about RaagaPay? For interviews, comment or corpus documentation, write to debjit@raagapay.in.
Questions, Answered.
What AI teams, researchers and procurement ask us most, before the rate card.
RaagaPay is the cultural intelligence layer for music AI. We record, annotate and license the first commercially licensable, consent-logged corpus of Hindustani classical and Bollywood audio, built for enterprise AI training and evaluation, with artist consent and royalty terms attached to every track.
The Phase 1 corpus contains 135 studio tracks recorded at 96kHz/24-bit as multi-stem masters, across six instrument and vocal categories: tabla (42), bansuri (23), harmonium (21), sitar (18), vocals (17) and sarangi (14). The repertoire spans Hindustani classical and Bollywood.
Every track is annotated by trained musicians across five categories: performance parameters, performer information, cultural context, technical analysis, and preservation and rights. The field-level schema is shared with partners under agreement.
The corpus is 100% consent-logged at source. Every artist signs documented consent that explicitly authorises AI training use, is paid upfront for the recording session, and earns royalties on every subsequent use, with lineage attribution preserved in the metadata.
Three tiers: a research pilot licence for non-commercial academic study from $2,500 for twelve months, a commercial development licence for AI model training on annual terms, and enterprise agreements for multi-year strategic access. Commercial terms are provided through the rate card.
Yes. Under a signed NDA and dataset evaluation agreement, we provide an evaluation pack of six full-length tracks with their complete annotation records, on a two-week evaluation licence, typically delivered within 48 hours.
Not yet. The current corpus covers Hindustani classical and Bollywood repertoire. Carnatic recording is planned for Phase 2 of the roadmap, and enterprise partners receive priority access as it comes online.
RaagaPay, operated by The Sonic Story Private Limited in New Delhi, India, is the contracting party of record on every licence. Artists retain their documented rights and royalty entitlements under their signed agreements, and consent records travel with each track.
Build with the tradition,
not around it.
License the first commercially licensable, consent-logged corpus of Hindustani classical and Bollywood audio, or apply for research access.