Pebble.

The problem

Parents deciding where to take a toddler face a scattered, low-trust search. Gathering options across Facebook groups, WhatsApp threads, event calendars, blogs, and Google Maps takes 10–30 minutes and returns a long, redundant list of generic places.

Worse, the signal that matters is missing. Smaller local venues have little or no rating, and where ratings exist, "4.5 stars from 200 strangers" says nothing about whether a place works for one specific 19-month-old. Good recommendations from trusted parents live in WhatsApp and drop-off conversations, and evaporate between the Monday they're mentioned and the Friday they're needed.

The solution

Pebble is an AI recommendation app that turns a parent's visit into a structured, shareable record and answers questions with trusted, friend-based results. A parent uploads a photo and a few keywords, and Pebble drafts a recommendation the parent confirms in their own voice.

On retrieval, natural-language queries return 1–3 recommendations tagged by who they came from — a friend, a same-crèche parent, a parent of a same-age child — rather than 50 generic results. Every logged place is an endorsement by definition, so there are no star ratings to interpret; the parent decides who to trust based on the relationship. Pebble starts with toddler parents in Luxembourg and the Greater Region.

How it works

V1 runs two models server-side. Claude Sonnet 4.6 handles contribution: a forced `submit_recommendation` tool call reads the photo and any contributor text and returns validated JSON, with place identity trusted only from Google Places — vision is treated as a signal, never the source of truth, to prevent fabricated details. Two small compose calls turn the parent's answer into a first-person note and an optional heads-up tip.

Semantic search uses Voyage's voyage-3.5 embeddings at 1024 dimensions, matched through a Supabase `match_places` function whose `SECURITY INVOKER` setting keeps the friends-only access policy active. The trust-critical retrieval path is deterministic, so a model outage degrades polish and relevance but never trust.

Who it's for

Pebble is B2C, built for the "Tired Friday Parent" — a parent of a child aged 12–48 months in Luxembourg or the Greater Region. This persona skews foreign-born (matching Luxembourg's ~47% average), arrives with few local connections, captures 20+ photos per outing, and is comfortable with AI-chat interfaces.

Planned expansion reaches parents of preschool, school-age, and tween children. A secondary B2B2C path envisions crèche partnerships and, later, verified venue listings — provided verification never becomes promotion and undermines the trust mechanic.

Why it matters

Friend-recommendation apps are historically a graveyard — Wist, Path, and Foursquare Tips all failed on the cold-start problem and the free, zero-friction substitute of the camera roll and group chats. What has changed is that AI collapses contribution cost: a photo plus two keywords now produces a useful structured entry, reopening the category.

Luxembourg's dense, high-income, weakly-networked expat parent population is uniquely suited to seed it, and the personal-log-plus-friend-graph pattern is already validated by Letterboxd, Beli, and Goodreads. Pebble is at idea-validation stage, launching as a closed, invite-only pilot to 5–8 friend users, with expansion gated on trust-loop signal rather than a date.

At a glance

Project

Pebble

Built by

James Cai

One-liner

Pebble is an AI-powered recommendation app for parents trying to find child-friendly places through trusted social connections instead of scattered listings and ads.