Demo v2 in production

AI pre-visualization
for Indian cinema.

Screenplay to full-length rough cut. Multi-character consistency. Telugu TTS. Multi-provider video routing. Built as research lab + production engagement model.

Join waitlist Research papers
The thesis

“Will this screenplay actually work as a film?”

Answered for ₹3-15 Lakhs instead of ₹5-50 Crore in production risk. Tollywood ships 250-300 films a year. Most are low-to-mid budget (₹50L-5Cr) where production risk is existential. Story validation before committing is the wedge.

Studios is positioned as India's AI cinema research lab — operating productions while publishing breakthrough techniques. Output targets: 5-7 published papers over 12 months (arXiv → ACM MM / SIGGRAPH Asia / CVPR), 1-2 tier-1 Tollywood production deployments, OTT commissioning conversations.

Capabilities

What the engine does.

Multi-Character Pre-Viz

Consistent character rendering across a full-length rough cut. Up to 3 characters per shot with @tag prompt binding.

Multi-Provider Routing

Runway Gen-4.5, Veo 3.1, Wan 2.2 self-hosted. Cheapest-per-quality routing across shot intent (dialogue, volume, establishing).

Indian-Language TTS

Sarvam Bulbul v3 for hero dialogue — the Indian-language TTS that beats ElevenLabs on naturalness. AI4Bharat IndicF5 self-hosted for bulk.

Screenplay → Rough Cut

5-agent LangGraph pipeline. Screenplay parse → shot planning → generation → audio → QA. End-to-end from pages to MP4.

Research

Papers published + forthcoming.

We publish negative findings alongside positive ones. Ablation rigor applied to AI cinema.

Benchmarking AI Video Models for Feature-Length Pre-Viz

In draft · arXiv target Q2 2026

Target: ACM MM 2026 / SIGGRAPH Asia 2026

Multi-provider benchmark of Runway, Veo, Wan, LTX, Seedance on character consistency, motion quality, prompt adherence, and cost-per-second. Published negative findings included.

Character Consistency in Long-Form AI Video (Paper 2)

Draft later 2026

Target: arXiv → CVPR 2027 workshop

Asset-first strategy, cascading generation, reference-image best practices for multi-character feature-length output.

Telugu AI Cinema: TTS and Cultural Adaptation (Paper 5)

Planned

Target: arXiv → IEEE TMM

Multi-provider Indian-language TTS comparison + cultural adaptation patterns for Telugu cinema pre-viz.

See all Koovis AI research at /papers.

Join the waitlist

For Telugu producers, OTT commissioners, research collaborators.

Early-engagement pricing: ₹3-15 Lakhs per pre-viz. Productized tiers coming later in 2026. We're selecting 1-2 tier-1 Tollywood engagements for 2026.

No spam. One email when it ships.

FAQ

Common questions.

Can I commission a pre-viz for my film?+
Not yet as a full commission. We're in early engagements and shipping Demo v2 (Baahubali Coronation, 240 seconds). If you're a Telugu producer or production house interested in early collaboration, join the waitlist and we'll reach out when productized offerings open.
What language is the pre-viz in?+
Telugu first (hero dialogue via Sarvam Bulbul). Multi-language roadmap: Tamil, Kannada, Malayalam, Hindi, Bengali, Marathi planned. We optimize for Indian-language fidelity; Western tools are English-first.
What does this cost?+
Early-engagement pricing: ₹3-15 Lakhs per pre-viz project. Productized tiers (₹5-25L) will follow once we open general availability. For reference: traditional pre-production is ₹5-50 Crore of production commitment to validate story.
What do you publish?+
Multi-agent architecture papers, character consistency findings, Indian-language TTS benchmarks, economics of AI pre-visualization, legal frameworks for AI content in Indian cinema. Target venues: arXiv, ACM MM, SIGGRAPH Asia, CVPR workshops, IEEE TMM, Computer Law & Security Review.
Is this the same engine as Koovis Pulse?+
Yes — Koovis Pulse and Koovis Studios share the same koovis-ai-media engine (5-agent LangGraph + Sarvam TTS + Runway/Veo gateway). Pulse applies it to D2C UGC ads (short-form, high-volume). Studios applies it to cinema pre-viz (long-form, character-heavy). Same pipeline, orthogonal buyers.