5,000 years of Indian music.
Now AI-ready.
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
of Indian classical music's 5,000-year tradition has been formally documented (UNESCO estimate). Centuries of oral knowledge are vanishing.
Creator
Zero commercially licensable, consent-logged Indian classical corpora existed before RaagaPay. The musicians who hold the tradition were shut out of the AI value chain.
AI Bias
Models built on 12 equal-tempered tones cannot hear a 22-shruti system. Internal benchmarking shows consistent failure on Indian classical material.
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.
Internal benchmarking shows consistent misreads of shruti, raag and tala in Indian classical contexts. The training data simply does not exist.
Maestro-verified, consent-logged, research-grade data built for the microtonal reality of the music.
See how80 Parameters. Per Track.
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. 80 metadata parameters per track.
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 Metadata Fields
Raag, taal, gharana, laya, shruti. 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.
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01
Record
A maestro performs in the studio, on their terms.
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02
Preserve
Every nuance archived at 96kHz against the 80-point schema.
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03
Train AI
Partners train on authentic, fully cleared recordings.
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04
Share Value
Royalties flow to the maestro on every use.
Building Toward a 500+ Hour Corpus.
From a verified pilot corpus to production-scale supply: 50 maestros under exclusive agreement, 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, 80-point annotation schema applied end to end, ethical protocols live.
Corpus scaled toward 500+ hours, 50 maestros under exclusive agreement, Carnatic recording begins.
Who's Behind RaagaPay
Debjit Mitra
Sound engineer, composer and sonic branding architect.
25+ years across Spotify, the BBC and Zee TV.
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.
Delhi startup RaagaPay builds the first ethical AI dataset for Hindustani classical music with lifetime artist royalties
On the 80-parameter annotation schema 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.
Build with the tradition,
not around it.
License the first commercially licensable, consent-logged Indian classical corpus, or apply for research access.