Clue Watch.

The problem

People who subscribe to multiple streaming services waste enormous time deciding what to watch — an estimated 110 hours a year — bouncing between siloed catalogs. Recommendations inside each app are trapped in that app's library, existing tools are only about 30% accurate, and 49% of subscribers say they'd cancel over poor discovery.

Competitors sit at the extremes. JustWatch overwhelms without real personalization; MovieWiser under-delivers; TasteRay demands 25+ questions just to start. Nothing reliably matches a viewer's mood and taste across all their services at once, quickly.

The solution

Clue Watch (CueWatch) is an AI recommendation layer that sits on top of fragmented streaming ecosystems. Users pick their streaming providers, rate a set of familiar movies with one click, and receive six personalized recommendations — each filtered to titles actually available on their services and explained in a short, friendly match reason.

The onboarding is designed to deliver value in 1–3 minutes before asking for a login. Recommendations improve with each rating cycle, and returning users can pick from taste/mood clusters the system has learned over time. The revenue model is hybrid freemium: a free first month, then premium at $3.99/month, unlimited and ad-free.

How it works

Clue Watch uses two LLMs with distinct jobs. An enrichment model (GPT-4.1) reads each movie's title, year, and overview from the TMDB API and extracts grounded tags — emotional tones, pacing, themes, storytelling style — stored in an enrichment table of ~400 movies, using only evidence from the metadata to avoid hallucination.

A recommendation model (gpt-5-mini via the Responses API) then selects the final six from a candidate list. Retrieval is two-stage: the user's affinity-weighted taste profile filters the catalog to 20 candidates, which are passed as structured JSON into the prompt. The recommendation prompt applies combination-pattern reasoning over loved and disliked tags and a strict REASON SPEC for conversational, two-sentence explanations. Enrichment scores 6.54/7 relevance with a 7% hallucination rate; switching from gpt-4o-mini to gpt-5-mini eliminated the recommendation hallucinations seen across all earlier test cases.

Who it's for

Clue Watch is B2C, aimed at US viewers aged 25–50 who subscribe to 2–5 streaming services and lead busy lives with work and family. These users have high decision fatigue and value time-saving tools — the segment where nearly half would cancel over poor discovery.

The experience is built for the recurring "I want to watch something tonight" moment, spanning trigger, discovery, decision, watching, and reaction. It supports both first-time users who rate movies to build a profile and returning users who select from learned mood clusters.

Why it matters

The U.S. streaming market is projected to grow at a 19.8% CAGR from 2025 to 2030, and streaming fragmentation is a durable, growing pain as households add services. A fast, grounded discovery layer that respects what people already pay for addresses a problem the platforms themselves have little incentive to solve.

The MVP is a working end-to-end pipeline on Supabase Edge Functions with deployed database tables and tested flows. Launch runs from a capstone demo with 5–10 real users to an invite-only magic-link beta. Known gaps are documented honestly: rate limiting and rollback aren't yet configured, the ~400-movie catalog must expand past 500+ titles before public launch, and TMDB commercial licensing must be resolved.

At a glance

Project

Clue Watch

Built by

Louise Lund Nielsen

One-liner

Clue Watch is an AI recommendation tool for people who subscribe to multiple streaming services and waste time bouncing between siloed catalogs.