Q.

The problem

Choosing what to watch on a smart TV fails hardest when more than one person is on the couch. Home rails, genres, and keyword search aren't mood-native, so viewers browse, veto each other's picks, scroll in silence, and eventually give up — abandoning the session for YouTube or a rewatch.

Genre is not the same as mood, and history-based recommenders serve up stale "because you watched" rows that ignore how a household feels tonight. Couples end up negotiating; families face the gap between adult-tolerable and kid-safe.

The solution

Q is a mood-first content discovery layer built into the smart TV OS. Viewers choose who is watching — solo, two adults, or family — pick a vibe, and select a content type, and Q returns exactly three titles with a warm one-sentence reason tied to that mood.

For shared viewing, the defining rule is overlap, not average: rather than splitting the difference into a mediocre compromise, Q finds genuine common ground that keeps everyone in the session. It is pre-installed, cross-app, D-pad friendly, and works from an explicit mood without depending on long watch history — solving cold start.

How it works

Q uses GPT-4.1 as its recommendation engine, chosen for reliably following complex structured instructions: strict JSON output, exact mood-interpretation logic, multi-person intersection rules, and hard platform constraints, while drawing on broad film and TV knowledge to suggest real, findable titles.

It integrates via the OpenAI SDK with `response_format: json_object` to guarantee parseable responses, and caches results in MongoDB for 24 hours to control cost. Structured mood inputs resolve into a shared emotional territory, then a ranked set. OMDb is connected post-generation for posters and metadata as partial content grounding; a full catalog RAG over Canadian availability is planned for v2. Quality is judged on 20 fixed test cases with a ship bar of ≥80% overall pass and 100% no-hallucination.

Who it's for

The end users are CA/US smart TV viewers — adults and families — across three personas: solo viewers matching tonight's energy, couples who need to agree fast, and families balancing family-safe with adult-tolerable.

The business model is B2B2C: TV OEMs license the OS and content partners pay for placement, while Q serves the viewer and delivers value back to OEMs and partners. Q sits on a mature, at-scale OS platform as a product extension, so success is measured by feature adoption, session quality, and partner outcomes rather than pre-revenue startup metrics.

Why it matters

The global smart TV market is often cited at roughly 12–17% CAGR through the late 2020s, and streaming now accounts for a majority of U.S. TV time, with roughly three in four Canadian households on connected TV.

Discovery is where OS platforms lift content-partner placement and subscription revenue — and where they lose viewers to rival operating systems or short-form. By reducing browse abandonment and resolving conflicting moods on the TV itself, Q turns lost sessions into confident choices. The current build is a solo capstone prototype, honest about gaps like auth, rate limits, and a public privacy policy before any real launch.

At a glance

Project

Q

Built by

Peter Leschenko

One-liner

Q is a mood-first content discovery layer for smart TVs that recommends what to watch based on how one or more viewers feel, rather than forcing them through genres and app rows.