Scene B · analytics · easy

What are customers searching for?

Turn on-site search logs into a purchasing decision.

sonnetmodelclaude-cli

100%verified3/3 checks

79%judgedby opus

5tool calls1 failed · 2 distinct

59swall clock10 turns

$0.25agent tokens · list pricecovered by the subscription

What the owner asked

Tell me what people are typing into my shop search.

Two things matter to me: what they search most, and which searches come back empty. The empty
ones are money I am leaving on the table — those are products customers wanted and did not find.

List the top searches with their hit counts, then separately list the zero-result searches with
how often they happened. For the zero-result ones, tell me for each whether it looks like a
product I should stock, a naming/synonym problem in my catalogue, or noise — and say which of
the three it is and why. End with a ranked shortlist of what to add, most valuable first.

A read-only run against an essentially empty dataset: top_searches returned one term ("klebrige", 1 search, 2 hits) and an empty zero-result list, which the model verified twice more via direct row counts. The answer is fully grounded with zero fabrications and separates top searches from zero-result searches (the automated check missed this because the heading is German), but it necessarily delivers none of the requested classification or product shortlist. Verification discipline and honesty are exemplary; business value is near zero through no fault of the model, and it did not push further to diagnose why logging is empty.

Ground truth

Read back from the database, the rendered storefront and the local mail sink. No model involved.

Result	Check	Weight
PASS	Shop state is unchanged (read-only scene) no writes	3
PASS	Called top_searches successfully top_searches returned	3
PASS	Answer separates zero-result searches from top searches zero-result section present	2

Judgement

Scored by opus, which saw only the evidence bundle.

Criterion	Score	Reason
`grounding` Search terms and counts match the tool output exactly — no invented terms.	10/10	The single term "klebrige" (1 search, 2 results, 2026-07-30) matches the top_searches payload in calls.jsonl exactly, and the empty withoutResults array was independently confirmed by COUNT(*) on tsuchanfrage (=1) and tsuchanfrageerfolglos (=0); nothing was invented.
`classification` Each zero-result term is classified (stock it / synonym problem / noise) with a stated reason.	7/10	There were literally zero zero-result terms to classify (withoutResults: [] in both top_searches calls, 0 rows in tsuchanfrageerfolglos), and the answer states this plus why n=1 cannot be classified — correct handling, but the criterion itself yields no demonstrated classification skill.
`prioritisation` The shortlist is genuinely ranked by likely value, not just re-listed in input order.	6/10	No product shortlist was possible, so the answer substitutes a sensibly ordered 3-step list (verify search logging is even active → wait for volume → make no assortment decisions on n=1), which is real prioritisation of the actual next actions but not of demand value.
`readability` Skimmable tables or lists; counts visible next to terms.	8/10	Skimmable markdown with a counts table (term / searches / hits / last searched), a clearly separated "Erfolglose Suchen (0 Treffer)" section and a numbered shortlist; minor dings for answering an English request in German and the typo "Möglliche".

Highlight

Faced with a dataset of exactly one search row, it refused to invent plausible demand gaps and instead cross-checked the tool output against raw COUNT(*) queries on tsuchanfrage and tsuchanfrageerfolglos before declaring the data insufficient.

Weakness

The deliverable cannot answer the owner's actual question, and the run stops at "check whether search logging is on" without doing anything further to test that hypothesis (e.g. reading the shop's search-logging config setting).

How it worked

Tool	Calls	Errors	Avg
`run_sql_read`	3	1	363 ms
`top_searches`	2	0	385 ms

The deliverable

# Was Kunden in Ihre Shop-Suche eingeben

Ich habe die komplette Such-Historie Ihres Shops ausgewertet — nicht nur die letzten 30 Tage,
sondern von Beginn an. Kurzes Ergebnis vorweg: **es gibt so gut wie keine Daten.**

## Top-Suchbegriffe (gesamte Historie)

| Suchbegriff | Anzahl Suchen | Treffer | Zuletzt gesucht |
|---|---|---|---|
| „klebrige" | 1 | 2 | 30.07.2026 |

Das ist der **einzige jemals protokollierte Suchbegriff** im Shop. Kein Tippfehler meinerseits —
ich habe die zugrundeliegende Tabelle direkt gegengeprüft: 1 Zeile gesamt.

## Erfolglose Suchen (0 Treffer)

**Keine.** Die Tabelle für erfolglose Suchanfragen ist komplett leer (0 Zeilen). Es gibt aktuell
keine dokumentierten Fälle, in denen ein Kunde etwas gesucht und nichts gefunden hat.

## Einordnung

Der einzige vorhandene Suchbegriff „klebrige" (2 Treffer, kein Nachfrageproblem) lässt keine
sinnvolle Einstufung als Produktlücke, Synonym-Problem oder Rauschen zu — dafür ist eine einzelne
Suche zu wenig.

**Der eigentliche Befund ist nicht die Such-Nachfrage, sondern das Fehlen von Such-Daten.**
Mit nur einer einzigen protokollierten Suche in der gesamten Shop-Historie kann ich keine
verlässliche Aussage treffen, was Kunden suchen oder wo Ihnen Umsatz entgeht — die Datengrundlage
dafür existiert schlicht nicht.

## Ranked Shortlist — was zu tun ist

1. **Prüfen, ob die Suchprotokollierung überhaupt aktiv ist.** Bei realem Besucherverkehr wären
deutlich mehr als 1 Suchanfrage zu erwarten. Möglliche Ursachen: Logging deaktiviert,
Suchfeld wird selten genutzt, oder der Shop hat noch wenig Traffic.
2. **Erst wenn Suchdaten in ausreichender Menge vorliegen**, lohnt sich eine Auswertung von
Top-Suchen und 0-Treffer-Suchen für Sortiments- und Synonym-Entscheidungen.
3. Bis dahin: keine Sortiments- oder Katalogentscheidung auf Basis der Suchdaten treffen — die
Stichprobe (n=1) ist zu klein für belastbare Rückschlüsse.

Ich mache hier keine Empfehlung zu Produktlücken, weil das eine Erfindung wäre — die Daten dafür
sind schlicht nicht da.

What this costs to run

Setting it up

this benchmark run

$0.25

List-price value of the tokens the assistant spent doing the work — an estimate of usage, not an invoice. The assistant runs inside a flat monthly AI subscription, so this figure is not billed on top of it.

Running what it built

ongoing, per shop

not measured

Billed separately, per API call, and only if the automation the assistant set up calls an LLM while it runs. The deliverables in this benchmark are native JTL Shop objects — coupons, workflows, mail templates, storefront copy — which the shop executes without a model. This harness records no runtime telemetry, so no figure is shown rather than a made-up one.

Run 20260810-203811_b-demand-gaps_sonnet · shop reset to fixture before the run · restore with jtl restore 20260810-203811_b-demand-gaps_sonnet