Case studies
DTC commerce· 6 months

Cited in 4 of 5 AI answers for category prompts

Entity rebuild, product schema engineering and source-worthy reference content. Tracked across 11 AI engines.

4 / 5
Citation share
62%
Branded prompt lift
11
Engines monitored
DTC commerce case study illustration

By the numbers

  • 240

    Prompts tracked

  • 6

    Engines won

  • 1

    Wikidata entry approved

  • 180

    Products re-schema'd

  • 12

    Reference articles shipped

  • 2.3×

    AI-referred sessions

The problem

A premium DTC brand was invisible in ChatGPT, Perplexity and Google AI Overviews — competitors were being recommended even on branded prompts.

The approach

  1. 01

    Entity graph rebuild

    Wikidata, Wikipedia, brand sameAs, founder disambiguation. Cleaned up four years of conflicting metadata across the web.

  2. 02

    Product-level schema engineering

    Product, Offer, AggregateRating, Review on every SKU. The engines now extract product data instead of guessing it.

  3. 03

    Source-worthy reference content

    Co-authored with the in-house category experts. The kind LLMs actually cite — definitions, comparisons, and original buyer data.

  4. 04

    Weekly multi-engine prompt monitoring

    Mapped 240 prompts to commercial intent, tracked drift weekly across 11 engines. Decisions stopped being vibes.

Results

Citation share

80%

Branded AI traffic

162% baseline

Prompt coverage

240

The outcome

Within 6 months the brand was cited in 4 of 5 high-intent category prompts across the engines we tracked, and branded prompt traffic from AI referrers grew 62%.

"AI search felt like a black box until Contingo. Now we have a dashboard, a roadmap, and a real share of voice in the answers our customers see."
Head of Growth, DTC

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