Test your film or series before you spend on production.

You submit your material

Scripts, treatments, synopses, pitch documents. The more detail you provide, the more precise the evaluation. Even a strong logline can produce useful directional results. Upload your file or paste text directly into the submission form.

We parse and understand your concept

The system reads your material and extracts the core elements: genre, tone, setting, character dynamics, structural shape, and commercial signals. It builds a structured case file that all evaluators read from. Nothing is lost in translation between your vision and the evaluation.

We match against real industry data

Before any evaluation begins, the system searches real Indian film and OTT performance data -- directors, actors, box office patterns, and streaming outcomes. It finds the closest comparable titles, relevant track records, and regional benchmarks. This grounding in evidence is what separates a calibrated evaluation from speculation.

Five industry perspectives debate your material

A panel of five calibrated perspectives -- Producer, Audience, Critic, OTT Strategist, and Screenwriter -- each read your case file and the evidence. They form independent positions, then hear each other's arguments. Some change their minds. The result is a structured verdict, not a single opinion.

We calibrate confidence

Every evaluation is scored for reliability. The system measures evidence strength, panel alignment, data coverage, and score stability. A high-confidence evaluation means strong comparables and broad agreement. A low-confidence one means limited data or significant disagreement -- and the report says so. Low confidence results are explicitly flagged and should be treated as directional. You always know how much to trust the result.

You receive a producer-grade report

The final output is a structured evaluation with a greenlight score, panel breakdown, comparable analysis, risk assessment, and a clear recommendation. It reads like a studio development memo, not an AI chatbot response. Every claim is grounded in evidence, every risk is quantified, and the recommendation comes with conditions.

Asking ChatGPT

A single model producing a single opinion with no evidence, no calibration, and no structured methodology. It will say whatever sounds plausible.

Structured panel with evidence and calibrated confidence

Five independent perspectives, grounded in real film and OTT data, with explicit confidence scoring and disagreement tracking.

Running a survey

Generic respondents answering generic questions about a concept they read in two sentences. No depth, no deliberation, no industry context.

Simulated audience that reacts, debates, and changes minds

Each perspective reads the full material, forms a position, engages with counterarguments, and can revise its view. The process mirrors real development meetings.

Hiring a focus group

Expensive, slow, geographically limited, and heavily influenced by group dynamics and moderator bias. Results take weeks.

50 simulated perspectives for 1/10th the cost in 1/10th the time

Instant evaluation across multiple audience segments and industry lenses. No scheduling, no venue, no post-analysis delays.

Trusting your gut

Pattern matching based on personal experience, which is inherently limited by what you have seen and what you remember. Recency bias dominates.

Data-backed evaluation across real comparable titles

Every recommendation is grounded in actual box office results, streaming performance, and directorial track records. The data remembers what intuition forgets.

Professional