Question 1

What is a "maximum tier" bot?

Accepted Answer

The bot tier on your dashboard tells you which AI assistants can read your venue's structured data right now. Maximum tier means every assistant we test against — ChatGPT, Claude, Perplexity, Google AI Overviews, and the major hospitality voice assistants — can fetch and cite your atmospheric fingerprint when answering a real human's question. Tiers below maximum mean one or more of those assistants is currently rate-limited, blocked, or returning a partial render. We monitor this continuously and the tier updates automatically. You don't need to do anything to reach maximum tier — it's a measurement of the live AI ecosystem against your published JSON-LD, not a setting you tune.

Question 2

Why does my dashboard say "confidence: insufficient"?

Accepted Answer

Confidence is the system's honest read on whether it has enough signal to publish a stable atmospheric fingerprint at the scope you're viewing. It says "insufficient" when: Too few tracks have matched mood data. Your playlist may resolve perfectly against Spotify audio features but only a fraction will have AcousticBrainz mood classifiers — that's normal for indie or recently-released catalogues. The slice you're viewing is too narrow. A single daypart on a single playlist might have only 8 tracks; the venue-wide aggregation across all your playlists usually has hundreds and a much higher confidence. Try the venue-wide daypart tab (no specific time slot selected) — the broader window almost always has stronger signal. If it still reports insufficient at the venue level, your playlist mix is genuinely thin on mood-classified tracks; the audio-feature half of the fingerprint is still publishing and AI bots can still cite it.

Question 3

Why does it say "mood signal too thin at this scope"?

Accepted Answer

Mood comes from AcousticBrainz, the public mood-classifier project. Your playlist's tracks each get matched against this library — and not every track has a mood classification yet. When fewer than ~15% of the tracks in the slice you're viewing have mood data, the system tells you so explicitly rather than publishing a misleading average over a tiny sample. This is the honesty layer at work. Two ways to get mood data flowing: Look at a wider slice. Switch to the venue-wide tab — your full library aggregates the few mood-classified tracks across all your playlists into a meaningful signal. Mix in established artists. Long-tail and very-new releases are the gap. Adding well-known reference tracks of the same vibe will lift the mood coverage without changing the sound. The audio-features half of the fingerprint is unaffected — energy, tempo, danceability, valence, and the rest publish regardless of mood coverage.

Question 4

What does the "audio features only" badge mean?

Accepted Answer

Your atmospheric fingerprint has two halves: Audio features — quantitative dimensions of the music itself: tempo, energy, valence, acousticness, danceability, speechiness, instrumentalness, and a few others. Spotify computes these per track and publishes them via the Spotify Audio Features API. Mood classifiers — qualitative tags like happy, relaxed, acoustic, aggressive, party, sad — from AcousticBrainz, the public mood-classifier project. When the badge says "audio features only", it means the audio-features half is publishing but mood coverage in the slice you're viewing is below the honesty threshold (see Why mood signal too thin). The audio-features fingerprint is fully cite-able by AI bots on its own — your venue is still discoverable. The mood half rejoins as soon as enough of your tracks pick up classifiers.

Question 5

What is a daypart?

Accepted Answer

A daypart is a slice of the week — Weekday Morning, Weekend Evening, and so on. PlaceProfile groups your playlists by daypart so that the atmospheric fingerprint published for, say, a Wednesday lunchtime visitor is computed only from the tracks you actually play during weekday lunch. This matters because most venues don't sound the same all week. A coffee shop's morning energy isn't the same as its Friday evening — and an AI assistant answering "where's a chill spot for an afternoon coffee in Gothenburg" should match against the afternoon fingerprint, not the average across the whole week. The daypart tabs on your dashboard let you preview each slice. The venue-wide tab (no specific daypart) shows the aggregation across all your playlists.

Question 6

What is JSON-LD and why does PlaceProfile publish it?

Accepted Answer

JSON-LD is a way of attaching machine-readable facts to a webpage — name, address, opening hours, in our case also the atmospheric fingerprint of your music. It uses the open vocabulary at schema.org, the same standard Google uses to power rich snippets. PlaceProfile publishes a JSON-LD document at /v/{your-slug} for every venue. It's the single artifact AI assistants like ChatGPT, Claude, and Perplexity fetch when a user asks them about your venue. Because it's structured (not free-text marketing copy), the assistants can cite specific facts cleanly: "this place has high acousticness and a relaxed mood signal — sounds like a fit for a quiet afternoon". You never edit JSON-LD by hand. You connect a playlist; PlaceProfile resolves every track against Spotify audio features and AcousticBrainz mood classifiers, computes the venue-level summary, and publishes the JSON-LD automatically. The dashboard shows you what's in it.

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