How Clairva Solves the Problems Highlighted in India’s Draft AI Policy

One way to read India’s draft AI policy is as an attempt to retrofit order onto a system built on disorder. Modern AI systems are trained on billions of media fragments-scraped, compressed, decontextualised, and mathematically abstracted beyond human comprehension. As the policy paper notes, these are “non-deterministic” workflows where retracing the provenance of a copyrighted work is “technically infeasible”. That sentence captures the entire dilemma: AI companies can’t show where their training data came from, creators can’t prove misuse, and regulators can’t enforce anything meaningful without breaking the entire sector.

Clairva exists precisely in that gap.

If the first decade of AI was about building bigger models, the next decade will be about building accountable ones: models whose training inputs can be licensed, logged, secured, and, crucially, paid for. The Draft Policy’s ambition of a “one nation, one licence, one payment” regime is an acknowledgment that the current data economy simply does not work at scale. You cannot govern a system built on anonymous scraping. You can only replace it.

Clairva’s approach starts with the opposite assumption: that content has owners, that ownership can be encoded, and that AI pipelines should respect those boundaries. The Leinua Vault provides exactly the kind of structure policymakers are imagining: a controlled environment where copyrighted video never leaves the custody of its owners, where models access only encrypted representations, and where every interaction is metered with financial consequences. The policy wants a centralized clearing house for rights; Clairva offers a decentralized but auditable equivalent for video data.

The other anxiety in the policy paper is economic. Legislators worry that enforcing rights might raise costs and “slow down innovation,” creating a disadvantage for Indian startups. But as the paper itself acknowledges, uncontrolled scraping also depresses the market, pushes creators out of the value chain, and leads to “culturally sterile” output that undermines India’s ambition to build globally competitive AI. Here again, Clairva sits in the middle: providing AI companies with licensed, structured, high-quality datasets from Indian and Global South creators, material that is scarce, underrepresented, and commercially valuable.

In a world where provenance becomes table stakes for AI safety, the companies with the best, cleanest, and most culturally diverse datasets will win. The Draft Policy implicitly confirms what the market has already started signalling: that compliant AI is about to become a supply-chain problem. Clairva is building that supply chain.

And perhaps the most important alignment with the draft policy is philosophical. The government wants a predictable revenue model for creators; Clairva enables recurring royalties every time AI companies access or train on a partner’s content. The policy wants transparency without crippling innovation; Clairva’s vault allows verification without revealing raw data. The policy warns against inequity for small creators; Clairva’s model treats a village videographer in Uttar Pradesh and a major broadcaster with the same licensing logic.

The policy imagines a future where India becomes a trusted global provider of culturally rich, legally compliant AI training data. Clairva is already doing the work that makes that future possible.