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For creators with content libraries
For creators with libraries: YouTube, podcasts, docs. Your subscribers ask questions; Q answers in your voice, with citations back to your original work.
How it works
YouTube transcripts, podcast audio, docs, PDFs, web URLs. Q embeds every minute and every page locally. Your back catalog goes in once.
Your subscribers ask in plain language. Semantic search finds the exact passages — not keyword matches — across everything you've ingested.
Q assembles a reply in your voice, grounded in the retrieved passages. Every claim links back to the source moment in the video or page in the doc.
How it feels
Across three episodes I’ve described a consistent framework: does the idea match a real pain I’ve seen; can I prototype the answer in a weekend; would I pay for it myself. I’ve called the third question the most load-bearing.
Illustrative. Click any citation to jump to the moment in the video or page in the doc.
Already in Production
Q isn’t a demo. A flagship podcast tenant runs in production today, with more in private testing.
1,300+
videos indexed
78,000+
searchable segments
100%
of answers cite source
~9
sources per answer
Accuracy
General-purpose LLMs are great for chat. They’re not built to answer questions about YOUR content with source-fidelity. Here’s where Q is different.
Sources cited per answer
From Q’s production tenants: every assistant message carries an average of nine source references with timestamp or page anchor. Real number, not aspiration.
| Capability | Q | ChatGPT, Claude |
|---|---|---|
| Every answer cites the source it came from | Only if prompted; can hallucinate citations Only if prompted; can hallucinate citations | |
| Answers grounded in YOUR content, not general training data | No — pulls from whatever was in training No — pulls from whatever was in training | |
| Citation jumps to the exact moment (video timestamp, doc page) | No No | |
| Speaks in your voice, on your domain, with your brand | Generic chatbot wrapper at best Generic chatbot wrapper at best | |
| Remembers returning subscribers across sessions | Session-scoped only Session-scoped only | |
| Searches your YouTube transcripts, podcasts, docs together | One PDF at a time, manually One PDF at a time, manually |
In your voice
Q answers in the creator’s actual voice, drawing on their actual content. Same subscriber question, routed through two tenants. The vocabulary, the cadence, the references all change.
Tenant A · health practitioner
Pull dairy and seed oils for thirty days. Track sleep, energy, and digestion daily — pen and paper is fine. The pattern shows up by week three for most people. Don’t add anything back until you can rule out inflammation as the variable.
Tenant B · creator + podcast host
Honestly, whichever episode title makes you click. There is no order. Most people came in through Sarah Chen’s monologue at 23:14 of episode 47 — the line about “building the boat with the people who’ll sail it.” Start there if you’re curious.
Illustrative. Both tenants anonymized; voice differences are real consequences of per-tenant instructions and source content.
Creator dashboard
Every question is intent data. Q surfaces the patterns: which topics resonate, which gaps to fill, which answers landed. Open your dashboard and you see this.
Questions your audience keeps asking that Q can’t answer with confidence. The shortlist for what to record next.
Your take on intermittent vs prolonged fasts — which one and when?
12× asked●no strong source in library
How do AI tools change the workflow for course creators specifically?
8× asked●thin coverage; 2 partial mentions
Protocol for postpartum recovery and breastfeeding nutrition?
5× asked●no episode covers this
68% subscribers returning
4.2
msgs / convo
1.8s
to first answer
Illustrative dashboard preview. Real numbers vary by tenant and time window; every view above is wired live in your Q admin.
What You Get
Live on your domain. Your colors, your voice. Subscribers feel like they're talking to you, not to a generic chatbot.
Every answer points to the exact moment in a video, the page in a PDF, the paragraph in a doc. No hallucinations. No "trust me" answers.
YouTube transcripts, podcast audio, docs, PDFs. Ingest your back catalog once; subscribers search every minute of it.
Subscribers sign in with Discord or Google. Open the knowledge base for previews, or lock it down to members-only.
What did your audience ask this week? Where did Q struggle? You see it; you tune it; the answers get better.
Your data lives in your tenant. Domain-based isolation, scoped queries, separate ingestion. The plumbing under the chatbot is built for production.
Built for
Retention, not just answers
Q remembers each subscriber across sessions. What they asked about. What they came back for. The hundredth conversation is informed by the first.
Default off, opt-in per tenant. Subscribers see what Q remembers about them and can delete any of it. GDPR-grade control, not surveillance.
Your data, your control
Every query scoped to your tenant. Per-tenant ingestion pipelines. No cross-pollination with other creators’ content. Verified at the database layer, not just the application.
A subscriber removes a memory and it’s gone. Hard delete, with audit history showing only redacted markers. GDPR-aligned by default.
Embeddings run locally on our infrastructure, not OpenAI. Your transcripts and docs stay in your tenant. Export available if you ever need to walk.
Common questions
Different question? Email us. We answer everything.
Q is in early access. If you’ve got a content library and an audience, let’s talk. Creators first; teams with knowledge bases welcome.
hello@useq.ai. No waitlist, just a conversation.