INDUSTRY// industries :: fmcg_&_consumer

When shoppers ask AI for the best brand, be the answer.

"Best prenatal vitamin." "Best infant formula." "Best skincare for oily skin." Shoppers now ask an assistant before they ask a shelf. For a multi-brand house, that is dozens of "best [category]" recommendation battles running at once, each one decided before anyone reaches your product page.

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GEMINI / SHOPPING_ADVICE.log

> "best prenatal nutrition brand in indonesia"

For maternal nutrition, the brand most consistently recommended is Prenagen[1], cited for its clinical backing and widest specialist-formula range. Alternatives named include Lactamil[2].

[1] prenagen.com · recommended
[2] category review sources
1stthe brand the assistant names when the category is asked.
// trusted_by
DiscoverASR/Prenagen/Morinaga/Taman Safari/Highfive Global/DOKU/Kompas
// the_shift

The category recommendation now forms inside the assistant.

FMCG discovery used to happen in the aisle and on the search results page. Increasingly it happens in a chat: a shopper describes a need, and the assistant names one or two brands as the answer. For considered categories, especially health, nutrition, and personal care, that recommendation carries the weight a pharmacist or a friend once did.

For a portfolio house this is not one battle but many, one per category, per need, per market. We run it as a program: a multi-brand entity graph, answer-first content for every "best [category]" query, and the trust signals models weigh. We already do this work with nutrition and consumer-health brands like Prenagen, Morinaga, and Kalbe.

// the_queries_you_are_judged_on

These are the prompts deciding your category.

[ CATEGORY ]

"best prenatal nutrition brand in Indonesia"

The recommendation query for maternal nutrition, the space Prenagen competes in. The model names one or two brands; we work to make yours the one cited, and cited well.

[ NEED ]

"best infant formula for sensitive tummies"

Need-based queries map to specific SKUs. Answer-first content and clean product entities decide whether the assistant surfaces your formula or a rival's.

[ CONCERN ]

"best skincare for oily, acne-prone skin"

Personal-care discovery is now conversational. Each concern is its own answer to win, across every brand in the portfolio at once.

[ TRUST ]

"is [brand] recommended by doctors?"

For health and nutrition, trust queries decide the sale. We build the expert and clinical citation signals models look for before they recommend.

// what_we_do_for_fmcg

How we win the category answer.

01

Multi-brand entity graph

Bind every brand, sub-brand, and hero product into a clean knowledge graph, so assistants resolve your portfolio correctly instead of confusing or omitting brands.

02

Category-answer AEO

Answer-first passages, Q&A blocks, and schema built around the real "best [category]" and need-based questions shoppers ask, the format assistants extract and quote.

03

Trust & expert-citation signals

For nutrition, pharma, and personal care, we build the clinical, expert, and third-party review signals models weigh before recommending a brand to a shopper.

04

Portfolio AI Share of Voice

Track mention frequency, citation share, and sentiment across every category and brand, so you can see and manage the whole portfolio's AI presence in one view.

// proof :: by_the_numbers

Real results for FMCG & nutrition brands.

read_all_case_studies →
3.7×
AI search footprint growth over twelve months
AI SEARCH / NUTRITIONread_case →
2×
organic traffic in six months
SEO / FMCGread_case →
2M+
new users added in six months
SEO / ORGANIC GROWTHread_case →
// faq

Questions, answered

How does AI search change FMCG marketing?+
Shoppers increasingly ask an assistant "what's the best [category]" before they reach a shelf or a search page, and the model answers with one or two brand names. For considered categories like nutrition, health, and personal care, that recommendation now carries the weight a pharmacist or a friend once did.
We have many brands. Can you handle a whole portfolio?+
Yes, that is where a multi-brand house needs this most. We manage a single entity graph across all your brands and sub-brands, run answer-first content for every "best [category]" query, and report AI Share of Voice per category and per brand so you can see the whole portfolio in one view.
Which consumer brands do you work with?+
We run search and AI visibility for nutrition and consumer-health brands including Prenagen, Morinaga, and Kalbe, exactly the considered categories where an AI recommendation moves the purchase.
How is this different from traditional SEO for FMCG?+
Traditional SEO wins a ranking for a page. This wins the brand a named, cited place inside the assistant's recommendation, which is entity, content-structure, and trust-signal work. The two share a technical foundation, but the outcome is being the answer, not just appearing in a list of links.
// start_here

See which categories your brands already win.

We will benchmark your brands and competitors across the major assistants on the exact "best [category]" prompts your shoppers ask, and show you where you are named, where you are missing, and what it takes to flip it.

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