Packaged food
Start with the product name, packaging details, or nutrition and ingredient panel. Matching accuracy depends on the available product information.
Product-firstThe journey
From “What is this?” to “Now I understand.”
Nutri Eat-IQ is built around a simple idea: make food information easier to explore at the moment people need it. Different foods need different paths — packaged products, homemade dishes, and customized meals do not all fit the same lookup workflow.
A flexible flow
Three paths, one goal
The starting point can change; the aim stays the same: useful context for a more informed choice.
Homemade meals
A meal-first workflow can consider dish type, ingredients, preparation, and portion size. Any nutrition output should be treated as an estimate unless measured recipe data is available.
Meal-firstCustomized food
For a dish such as cheese Maggi, a base food match can be refined with toppings or additions. Similar-food suggestions can help when the exact named item is unavailable.
Flexible matchingInside the experience
What happens at each stage
The following describes the intended product experience; exact results depend on the implementation and the quality of the input data.
01
Choose how to begin
Search a known food, inspect a packaged product, or start with a meal description or photo where that workflow is supported.
02
Identify the best available match
The system can use an exact match when available, or guide the user toward a similar food and ask for missing details such as portion, ingredients, or preparation.
03
Read the nutrition picture
Explore calories and nutrients where data supports them, plus plain-language explanations of ingredients, additives, allergens, and label claims.
04
Compare and learn
Use alternatives, food comparisons, educational content, and habit tools to turn information into a practical next step.
Example in real life
When the exact dish is missing
Imagine you want to log homemade coconut chutney or cheese Maggi, but the exact entry is not in a food database.
The old friction
Search. Scroll. Give up.
A database built around packaged products can struggle with family recipes, local dishes, toppings, and foods that vary from kitchen to kitchen.
The Nutri Eat-IQ idea
Start with what is on the plate.
Use meal-first identification or a close food match, then refine the result with portion and recipe details. Similar foods are a fallback — not a promise that every dish will be identified perfectly.
A note on accuracy
Helpful estimates should be honest estimates
A photo cannot reliably reveal exact ingredient quantities, oil absorbed during cooking, recipe variations, or portion weight. For that reason, meal analysis should show uncertainty, allow corrections, and never replace professional medical advice.
Designed for clarity, not false precision
Use the estimate as a starting point. Add ingredient and portion details when possible, and consult a qualified professional for medical nutrition needs.
Interactive walkthrough
What affects a meal estimate?
Choose the details you know to see how context changes an estimate. This is a demonstration, not a live AI result.
Select the details you know, then run the walkthrough.