The whole loop,
from chat to proof.

Talgeo is organized like your store, not like an analytics tool: shelves, products, questions, fixes — and the receipts for all of it.

Overview

Product health at a glance

One table answers the daily question — “is every product OK everywhere AI looks?” Visibility with deltas, win rate, Q&A coverage colored by urgency, citations with a “you” badge when your own site is among them, and whether ChatGPT knows the product cold.

PRODUCT VIS · WIN · Q&A · CITED · COLD
Petite Bougie 12% · 71% · 6/9 · 59 you ✓ ·
Grande Bougie 9% · 53% · 9/9 · 41 you ✓ ·
Coffret Découverte 7% · 43% · 4/9 · 27 ·
Prompts

The questions, out in the open

Your visibility denominator is a list you can read: category-blind shopping questions generated from your catalog, plus any prompt you add. Run a check anytime — every answer is stored whole.

“best scented candle for a small apartment” ran 11×
“non-toxic candles safe for a bedroom” ran 11×
“luxury candle gift under €70” ran 11×
+ Track this prompt
Product Q&A

Answers your shoppers already asked for

Every product carries the questions AI shoppers ask about it. The gaps come with prefilled answers drawn from your own product facts — where only you know the truth, the draft asks you. Approve & publish, or stage to the queue.

“Is the massage candle wax safe on skin?”
Karité-based, melts at body temperature — made for skin.
Approve & publish Send to Queue
needs your facts “What is the return window?”
Citations & Outreach

Where the engines read — and how to get there

Every chat lists its sources. Talgeo classifies them — your site, UGC, editorial, competitors — and turns the gaps into outreach: real reviewers, subreddits and editors for your categories, each with an honest pitch drafted and ready.

reddit.com ugc 31 citations
byrdie.com editorial 14 citations
maisonlumiere.com you 12 citations
Earn a mention → drafted pitch to r/Candles
Technical GEO

The crawl-level door, kept open

AI engines can only recommend what they can read. Talgeo audits the machine-facing store — feeds, structured data, llms.txt, the agent door (UCP) — and walks you through each fix, then re-checks it.

critical llms.txt missing Show me how →
high product feed lacks GTIN on 3 items
pass structured data · robots · sitemap
Proof

Before / after, on record

Every published fix triggers a re-test of the exact question that failed. The before/after pair — with full transcripts — is the report your Monday morning wants, and the one your accountant believes.

before “burn time unknown; consider Diptyque”
after “45 hours, hand-poured — a clear pick” ✓ proven
re-tested automatically after publish

See it on your own catalog.

The demo runs on your products, your categories, your rivals — not a slide deck.

Book a demo