Overview
A manufacturer had deep product knowledge spread across product systems, technical documents, web pages, spreadsheets and experienced employees.
Customers did not experience those systems. They had a problem, application or requirement and needed to find the right answer.
We created a content and metadata model connecting product facts to applications, customer problems, markets and proof so the same knowledge could support websites, sales tools, search and AI retrieval.
Who This Is For
Industrial manufacturers with:
- Complex product portfolios
- Technical documentation
- Multiple applications or industries
- Distributor or direct sales channels
- International markets
- CMS, PIM, ERP or document repositories that describe the same products differently
The Situation
The product database knew dimensions, codes and specifications.
The website knew marketing descriptions.
Engineers knew why one solution worked better in a particular environment.
Sales knew the questions buyers actually asked.
Those forms of knowledge were related but not connected.
Where the Workflow Breaks
Our Approach
Model the buyer's problem space
Map relationships between:
- Product
- Application
- Industry
- Problem
- Environment
- Specification
- Benefit
- Evidence
- Certification
- Geography
- Service and support
Establish source authority
Determine which system or role owns each fact. The CMS should not become an accidental product master.
Build controlled vocabulary and metadata
Use a shared language connecting technical data to buyer intent.
Create reusable product knowledge
Separate stable facts from:
- Market messaging
- Channel presentation
- Application guidance
- Proof
- Local variation
Expose the model to retrieval
Make structured relationships useful to:
- Site search
- Filtering
- Product finding
- Internal sales tools
- Retrieval-augmented AI
- Content recommendations
Before → After
Where AI Helps
AI can interpret questions, retrieve candidate answers, create summaries and help generate channel variants.
It should retrieve from authoritative knowledge rather than improvise product truth.
What Changes
- Easier product discovery
- Less contradictory content
- Clearer source authority
- Better reuse
- Stronger sales enablement
- Improved AI retrieval
- Easier localisation
- Less dependence on individual experts for routine questions
These are the operational changes the work aims for. Any measures are agreed against your own baseline during scoping.
What This Demonstrates
AI readiness for industrial companies starts with product knowledge architecture, not prompt engineering.
Relevant experience
Named projects that show parts of this approach in practice. They evidence the underlying experience; none of them is the scenario above, and each case study says how it was delivered.
- Product-Finding Tools for a Global Paints & Coatings Manufacturer's B2B Website
A product-attribute model drawing structured data from several systems into B2B product finding.
- Redesigning Content Taxonomy for a Global Paints & Chemicals Manufacturer
A controlled vocabulary, metadata schema and governance model across product, solution and market content.
- Digital Service Platform for a Building Materials Distributor
Estimating, tendering and project documentation for professional buyers, integrated across CMS, PIM and ERP.
- Technical Search Architecture for an International Education Marketplace
A search architecture audit quantifying platform-level barriers to discovery across two global sites.
At a glance
Client type
Capabilities
- Taxonomy
- Product content
- Metadata
- CMS and PIM integration
- AI retrieval
Most relevant to
Export manufacturersRelated patterns
Creating One Knowledge Layer for Marketing, Sales and AI
Complex B2B organisation
The organisation had plenty of systems but no shared view of which information was authoritative. We created the model connecting content, product knowledge, customer questions and proof without requiring everything to move into one new platform.
Making Complex Maritime Expertise Easier to Find and Sell
Maritime technology or marine-services company
The company's portfolio made sense internally but buyers arrived with vessel, application and operational problems. We connected technical capabilities to those customer contexts so the same knowledge could support discovery, sales and AI retrieval.
Recognise this problem?
We can start by mapping the workflow, finding the bottleneck and defining a contained first improvement.
Discuss a first project