Your customers are already asking AI.
When customers ask AI, are you in the answer?
Winin reveals how AI discovers, understands and recommends your brands and products—then turns each gap into evidence-led work your team can approve, publish and retest.
The monitored model roster includes ChatGPT, Claude, Gemini, Perplexity, DeepSeek, Grok, Copilot, Kimi, Qwen, GLM, Doubao, MiniMax and ERNIE Bot.
Before customers make a purchase decision,
AI may already be recommending a competitor.
These conversations happen every day — you simply aren’t there.
Each absence can reduce the chance that customers include your brand in their consideration set.
Stale or incorrect information may reach an answer before your correction and appear under your brand name.
Being mentioned is not being chosen. What customers remember are someone else’s reasons.
Without visibility there is no response; budget spent, with no way to verify the effect.
An AI recommendation
is not a switch.
Across three stages, a brand can be discovered, enter consideration and receive an accurate recommendation. Winin identifies where the gap occurs and prioritizes work supported by evidence.
Shortlist solves “are you considered”;
Accurate & recommended solves “do they trust you, do they pick you”.
REACH
Can AI read your information?SHORTLIST
Will it put you on the candidate list?ACCURATE & RECOMMENDED
Is your brand described accurately — and recommended?From spotting the gap to continuous improvement.
The right order: see clearly first, then act; act on evidence; verify after acting.
Find the gap
Pin down where the brand is absent, less competitive or misrepresented in AI answers.
Build the case
Establish an approved, verifiable factual basis.
Close the gap
Close information gaps one by one.
Retest
What changed? Retest under the same conditions.
Operate
Turn the full loop into a repeatable capability.
Every confirmed gap
gets a matching optimization playbook.
Winin compares the question, model answers and visible sources to identify where the gap occurs, then turns that finding into executable work.
Fact governance
Conflicts in identity, product facts, price and policy: first pin down source, owner and validity to form a usable version — before touching public content.
Site & product pages
For confirmed gaps, prescribe fact pages, product pages, FAQs, comparison content and structured data.
Enterprise channels
Locate the official stores, distributors, catalogs, partner profiles and product data that need correcting — as channel governance tasks.
Independent sources
Where independent evidence is missing, plan first-party research, test evidence, media materials, expert views or partner collateral.
Find the gap. Govern the facts. Advance the work.
Homer is the enterprise knowledge foundation: approved facts with their sources, versions and public permissions. Edith finds the gaps, Homer governs the facts, and Friday advances authorized optimization work.
From diagnosis to execution,
every step has a matching service.
AI recommendation diagnostics
See where you’re considered, recommended, or absent — every answer keeps its raw content, reasons and cited sources.
Managed monitoring & retesting
Continuously track changes across models, competitors, facts and sources; same-conditions retesting keeps before-and-after changes on record.
Enterprise knowledge foundation building
Unify brand info scattered across site, e-commerce, press and internal docs — single source of truth, owner, version control.
Encode the foundation for AI
Turn approved facts into structured public content that machines can parse and teams can trace; diagnosis and optimization share the same factual source.
Brand GEO
Using facts approved in Homer, improve how the brand is understood, considered and recommended across site content, channels and external sources.
Product GEO
For category, comparison and buying questions, close the gaps in product facts and recommendation reasons.
After the work, your brand gains four operating capabilities.
These interfaces illustrate product capabilities. Formal results depend on retained answers, verified publication and comparable retesting.
Retain the answer; trace omissions, errors and source changes.
Track the questions where the brand enters consideration.
Source, owner, version and validity stay linked.
Retest after verified publication on the same basis.
Winin completed 14 tasks.
You made the final decision.
Three anonymized scenarios show how Winin moves from monitoring to approved action and comparable retesting.
The following are anonymized client cases. Client names are not disclosed; the work, measurement scope and project outcomes are shown within the delivery record.
Formal conclusions require retained answers, verified releases and comparable retesting.
Agreed data and knowledge assets remain transferable at handoff.
From “AI doesn’t know you” to category first-candidate
Starting point: 7 of 9 models never mentioned the brand in “espresso recommendations”; the flagship quoted at a stale price by 2.
Measuring from “mentioned” all the way to “bought”
AI platforms already show product cards in answers. We build the full chain: how much drops at each cell, why, and how to fix it.
Winning the reasons back in “vendor comparisons”
Buyers ask AI first “which vendor is reliable”. Before: AI knew the name, but every recommendation reason went to competitors.
Pure monitoring tools stop at the first cell.
See the problem clearly, then decide how much to invest.
Free Check
5 fixed questions · one sample per question · up to 3 active model APIs; an initial signal, not a formal baseline.
Start the free check →Formal Baseline
Custom questions, models and cadence; a complete retestable baseline.
Contact us →Brand · Product GEO
Optimization strategy, site/content/channel execution, and same-conditions retesting.
Get a plan →Continuous Monitoring
Ongoing tracking of AI recommendation shifts, competitor moves and new growth openings.
Contact us →First the concepts, then the work.
The most-asked questions about GEO and AEO — including the uncomfortable ones.
What is GEO?+
GEO (Generative Engine Optimization) improves the conditions for accurate interpretation, consideration and citation through approved facts and publicly verifiable expressions; measured change still requires retesting. SEO focuses on discovery and clicks in search results, while GEO focuses on how generative answers understand and represent you.
What is AEO, and how does it differ from GEO?+
AEO (Answer Engine Optimization) focuses on making direct answers easy to retrieve and support. GEO overlaps with AEO but also examines how generative systems understand, compare, consider and recommend a brand or product. Both depend on clear facts, accessible content and comparable measurement.
How is GEO different from SEO — do we still need SEO?+
Yes. SEO improves how content is crawled, indexed and discovered in search; GEO examines how generative systems understand and use that information. Clear fact pages, product information and structured data can support both, but search performance and changes in AI answers should be measured separately.
Does AI actually read our website?+
AI may draw on training data, third-party media, marketplace pages, user content and your site. Homer organizes scattered information into approved facts with clear, traceable expressions; whether a model uses them, and how it responds, still has to be observed.
Why do AI answers keep changing?+
Training data, retrieval paths and generation strategies are all updated continuously, so the same question can get different answers on different days — that is normal. What matters is having a stable baseline to measure the direction of change, not drawing conclusions from a single answer.
What is structured data, and what does it do for AI?+
Structured data is a set of machine-readable annotations (such as Schema or JSON-LD) that states what something is, its price and whether it is available. It makes explicit facts easier to identify; whether AI or search systems use them, and how they express them, still has to be observed.