Rasoi.

The problem

South Asian adults managing insulin resistance face a nightly dilemma: what to cook without abandoning familiar food like rice, roti, dal, and sabzi. Existing apps — MyFitnessPal, Noom, Mealime, Yummly — are built for Western diets, not South Asian cuisine or the documented South Asian metabolic phenotype of younger onset, lower BMI, and higher insulin resistance.

The friction of logging fridge contents, decision fatigue, and the risk of feeling shamed for one's own cuisine all compound. The result is a large, underserved population left to guess, or to give up familiar foods entirely.

The solution

Rasoi is an AI meal companion that helps users decide what to cook from what they already have. Users enter available ingredients and staples, and Rasoi generates exactly three recognizable South Asian meals, ranked safest-first by blood-sugar impact, each with a concrete portion, a plain-language "why this works for you," and a smart lower-GI swap.

Every meal carries a Low/Med/High GI tag, with color reserved almost entirely for that safety signal so "safe vs not" reads at a glance. Constraint-driven adjustment visibly re-reasons the meal for cases like "already had rice" or "cooking for 4" — and when there isn't enough to cook, Rasoi honestly returns a *not_enough* response instead of inventing a meal.

How it works

Rasoi uses Anthropic Claude Sonnet for meal generation, chosen for strong instruction-following, structured JSON output, safe health-adjacent language, culturally fluent suggestions, and reliable constraint handling. The model is called only from a server-side Next.js API route, never the browser, and the backend validates output before returning it.

Grounding comes from a prompt-embedded, vetted ruleset based on glycemic-load principles and Diabetes Canada-style guidance — no fine-tuning or RAG in the MVP, an intentional scoping decision. Quality is measured by a 19-case eval suite across typical, edge, negative, and constraint scenarios: objective criteria C1–C7 (valid JSON, correct structure, specific portions, available ingredients only, no medical claims, no shaming language, correct edge-case behavior) are scored programmatically, and subjective criteria C8–C12 use a model grader with human spot-checking. Target: 100% pass on objective criteria and an average of ≥2.0 on subjective criteria.

Who it's for

The target user is a South Asian adult, roughly 25–55, in Canada or the US, diagnosed with insulin resistance, pre-diabetes, or early Type 2 diabetes, who cooks at home and wants to keep eating familiar food without spiking their blood sugar. They are both the buyer and the user.

The launch model is B2C freemium: a free tier for basic fridge-to-meal suggestions and a premium tier for glycemic tracking, health-condition tuning, and family meal planning. A future B2B2C path would license to clinics, dietitians, and diabetes educators, who form the secondary persona.

Why it matters

The need is large and growing. In Canada, age-standardized diabetes prevalence is ~16% among South Asians versus ~5–6% in the general population, with incidence 3.4x higher than white Canadians; the US MASALA study found South Asians had the highest diabetes rate of five ethnic groups at 23%. Roughly half of South Asian diabetes cases are undiagnosed — a large pre-diagnosis prevention market.

Built natively for South Asian cuisine and metabolic reality by a founder who is a diagnosed member of the target community, Rasoi is deliberately scoped as a food-planning assistant, not a medical device. It will launch as a controlled pilot of 10–20 testers to validate the core loop before broader release.

At a glance

Project

Rasoi

Built by

Ritika Shukla

One-liner

Rasoi helps South Asian adults with insulin resistance decide what to cook without abandoning familiar foods.