Simulate how India responds — before you commit.

What Thommidhi does

Thommidhi builds populations grounded in Indian data, places them in a changing environment, and measures how they respond to a decision. Then it checks every prediction against what really happened.

India is not one consumer. State, city tier, language, household, income, family influence, digital access and culture interact — and Thommidhi models those relationships instead of mixing attributes at random.

How it works, in nine steps

Individuals, a weighted population, segments, relationships between traits, a decision (such as ₹499 or ₹699), simulated responses as probabilities, an aggregate prediction with an interval, the real-world outcome, and calibration from the error.

Research-grade by default

Predictions are labelled as predictions. Confidence is computed from sample size, separation and track record. Language models explain results but never produce the numbers. Every prediction is later scored against reality, misses included.

Where we are

The first calibration environment is content: creators test concepts before publishing. The first calibrated population covers Telugu-speaking digital audiences in Telangana and Andhra Pradesh; more Indian languages and regions follow as each is validated.