00 · Overview
Fatboy Bikes Knowledge Optimisation Brief
From fragmented product pages to a governed knowledge system.
Making Fatboy Bikes discoverable, understandable, recommendable and ready for future agent-assisted commerce.
The proposition
Fatboy does not primarily have a content-volume problem.
It has a product knowledge consistency, structure and distribution problem.
01 · Background
Product discovery is changing
Fatboy Bikes has established a distinctive Australian e-bike brand, a national stockist and service network, and an ecommerce experience that enables customers to browse, compare selected models and transact online.
However, sales growth has stagnated while product discovery is rapidly changing.
Customers increasingly use ChatGPT, Gemini, Google AI Mode and Copilot to research products, compare alternatives, validate claims and decide what to purchase before visiting a retailer’s website.
Need → synthesis → shortlist → action
To appear within these experiences, Fatboy’s product and brand knowledge must be discoverable, unambiguous, authoritative, current and machine-readable.
02 · Problem
The truth is distributed
Fatboy’s product, compliance, service, policy and retail knowledge is distributed across product pages, model selectors, collections, FAQs, certification documents, Journal articles and stockist experiences.
This information has not been modelled and governed as a single, canonical body of product knowledge.
Product pagesModel selectorsCollectionsFAQsCertificatesJournalPoliciesStockists
Output?
Which answer is authoritative?
- Important product attributes are presented inconsistently across different pages.
- Product families, generations, configurations, colours and sellable variants are not always expressed as clear machine-readable relationships.
- Legal and compliance claims are repeated without an obvious authoritative source, jurisdiction, effective date or review status.
- Availability, pricing, shipping, returns, warranties, reviews and retailer availability are not presented through one consistent data model.
- High-intent customer questions are not comprehensively addressed through authoritative comparison, use-case and ownership content.
- Stockist and service information is difficult for search engines and conversational agents to interpret as structured local-business data.
- Internal, legacy and customer-facing product taxonomies are not sufficiently separated.
Answerability gap
Search and AI platforms cannot reliably determine the best product or confidently answer detailed questions about specifications, compatibility, legality, availability and ownership.
Transactability gap
Purchasing agents require stable identifiers, variant relationships, current offers, inventory, delivery rules, policies and checkout interfaces before they can reliably recommend and transact.
03 · Challenge
Transform pages into product knowledge
Core challenge
How might we transform Fatboy Bikes’ fragmented website and commerce content into a governed product knowledge system that allows people, search engines and AI agents to accurately discover, understand, compare, recommend and purchase Fatboy products?
The objective
Create a canonical and machine-readable product knowledge foundation that improves:
- 01
Traditional search visibility and ecommerce performance.
- 02
Answer extraction across search and conversational platforms.
- 03
Citation and product recommendation within generative AI responses.
- 04
Accuracy of product, compliance and policy information.
- 05
Conversion from product research into online or stockist purchases.
- 06
Readiness for agent-assisted checkout and post-purchase services.
01Governed knowledge
02Machine-readable publishing
03Trusted answers
04Confident decisions
05Purchase + service
04 · Scope
Five connected areas
Product knowledge architecture
Define the relationships between product families, generations, configurations, sellable variants, specifications, offers, certifications, accessories, locations and policies.
Content optimisation
Restructure product and informational content around the real questions customers ask when researching, comparing, validating and buying an e-bike.
Structured publishing
Publish canonical information consistently through visible HTML, Schema.org JSON-LD, product feeds, APIs and platform integrations.
Knowledge governance
Establish ownership, approval, versioning and review processes for specifications, legal claims, certification information, pricing, policies and compatibility data.
Agentic-commerce readiness
Prepare Fatboy’s catalogue, inventory, checkout and order capabilities for future product-selection and purchasing agents while retaining Fatboy as merchant and source of truth.
Inputs
Products · specifications · evidence · compliance · offers · policies · locations
Canonical layer
One governed body of product knowledge
Outputs
Website · schema · feeds · APIs · search · AI · retailers · service
05 · Desired outcome
One accurate answer, everywhere
“Which Fatboy bike is best for carrying two adults, is legal to ride in NSW, has at least 60 kilometres of range, and is available near Wollongong?”
The goal is not to invent the answer. It is to give Fatboy’s systems the governed knowledge required to assemble and evidence it.
01Appropriate product and configuration
02Relevant specifications and limitations
03Applicable NSW compliance status
04Current price and availability
05Nearby test-ride or service locations
06Compatible accessories
07Shipping or pickup options
08Direct path to purchase
The same answer should remain consistent across:
Fatboy websiteGoogleGeminiChatGPTRetailersCustomer service
From product pagesBack to top ↑
to product knowledge.
From product knowledge
to trusted action.