MyLens.

The problem

For couples who love cooking together but resent the planning, the weekly loop breaks down at every stage. Sunday planning gets avoided — and skipped more often than done — which cascades into unoptimized shopping, improvising all week, and food waste. Plans then break mid-week when schedules shift or cravings change.

The deeper problem is that existing tools store recipes but never learn a household. 61% of households say meal planning matters, but only 37% actually plan. Ingredient sharing across a week is combinatorially hard for a human, the best version of a dish lives in no one's memory, and point solutions each win only one job — none combine constraint-safe trust, cross-recipe optimization, and adaptive replanning.

The solution

Madeleine is a meal planner built for constraint-heavy couples that learns a household over time rather than just filtering meals. One tap on "What do we eat this week?" produces a full week from the recipes people already trust, respecting dietary constraints, optimizing shared ingredients, and planning around cooking sessions rather than individual meals.

Its core differentiator is compounding household taste memory — constraints, signature dishes, validated adaptations, and dislikes that surface over time and become the switching cost. Free-text mood steering ("Asian week," "comfort food") reshapes the plan, and photo-log replanning lets a couple snap what they actually ate so the rest of the week readjusts.

How it works

The guiding principle of the current design is AI for judgment, code for truth. Madeleine runs on Claude Sonnet 4.5 through a Supabase Edge Function, using its vision capability for photo interpretation and recipe import, with a household profile injected as JSON on every call.

The master prompt (V1.9) applies library-first rules — scan the saved recipe library before inventing anything — plus hard constraint rules, soft priorities, and a strict JSON schema with structured day/meal "covers" and a recipe-ID contract. Deterministic code then handles everything that must be correct: resolving recipe identity by ID with a fuzzy-match backstop, copying exact ingredients, building the grocery list as the provable sum of the week, and rejecting any plan that would save with missing ingredients. RAG is deliberately deferred until the adaptation memory grows in V2.

Who it's for

Madeleine is a B2C product priced per household, not per individual — the paying unit is the couple. The target segment is dual-working Gen Z / Millennial / Gen X couples who cook and eat together but resent the coordination overhead, where at least one partner has a meaningful dietary constraint like gluten-free, dairy-free, or vegetarian.

They batch-cook, shop weekly across stores, get inspired by restaurants and social media but lose the ideas, and have abandoned planning systems before. They aren't looking for a recipe app — they can already cook — but for an extension of their own memory and taste. The reference household is Olivia and Eric in Grenoble, the primary discovery subjects and first testers.

Why it matters

Home cooking is dominant and constraint-aware planning is mainstream: 91% of consumers use online recipes and 43% specifically seek diet-specific options, yet no product owns the full weekly workflow. Charging per household roughly doubles willingness-to-pay per acquisition, and the value compounds — week 12 plans feel more "theirs" than week 1, making the taste profile the moat competitors can't replicate.

The business is pre-revenue 0-to-1, validating a single hypothesis: that a constraint-heavy couple given one-tap weekly plans will still be using it after four cycles. Evaluation revealed the central finding — a 91.7% automated pass rate but only 64% human delight — which drove the pivot to splitting creative judgment (the model) from provable correctness (code).

At a glance

Project

MyLens

Built by

Olivia Lottmann

One-liner

MyLens is a meal-planning product for households with real dietary constraints, changing cravings, and a growing personal recipe library.