For businesses selling complex products, product information is one of the most important parts of your AI Agent.
Customers may care about much more than a product name. They may ask about technical specifications, dimensions, capacity, performance, applications, compatibility, age, condition, serial numbers or differences between models.
The better the product information, the better your AI Agent can support the conversation.
1. Start With Product Identity
1.1 Basic Product Information Each product should have enough information to identify it clearly.
Depending on your business, this may include:
- Product name
- Brand
- Model
- Category
- SKU
- Internal reference
- Asset number
- Serial number
Not every product requires every field.
1.2 Model-Level and Individual Product Information Different businesses manage products differently.
A manufacturer or distributor may mainly work at model level.
A rental, used-product or asset-based business may manage individual units.
For individual units, information may include:
- Serial number
- Manufacturing year
- Age
- Condition
- Usage hours
- Location
- Availability
- Sales status
- Rental status
- Price
- Actual product photos
This distinction is important.
Model information explains what the product is designed to do.
Individual product information explains the condition, status or availability of a specific unit.
2. Write a Useful Product Description
A good product description should explain:
- What the product is
- What it is designed for
- Typical applications
- Main benefits
- Important limitations
Avoid descriptions that are purely promotional but provide little useful information.
The Agent needs enough context to understand when the product may or may not be suitable.
3. Add Important Technical Specifications
3.1 Product Specifications For complex products, specifications are often critical to the customer's decision.
Depending on the product, this may include:
- Capacity
- Dimensions
- Weight
- Power
- Performance
- Operating range
- Material
- Voltage
- Pressure
- Accuracy
- Standards
- Compatibility
Only include specifications that are relevant to the product.
3.2 Technical Documents Where available, include approved supporting information such as:
- Product catalogues
- Datasheets
- Specification sheets
- Manuals
- Technical tables
- Performance charts
- Load charts
- Diagrams
- Compatibility tables
These documents help the Agent answer more detailed technical questions.
4. Add Application and Compatibility Information
Specifications alone may not be enough.
Where relevant, explain:
- What the product is suitable for
- What it is not suitable for
- Which applications it supports
- Which products or accessories it works with
- Which conditions need to be confirmed before recommendation
For parts, accessories or replacement products, you may also include:
- Part number
- Compatible model
- Serial range
- Required accessories
- Replacement product
- Alternative product
This helps the AI Agent understand product relationships rather than simply repeat technical data.
5. Product Information in AI Webstore Suite
For AI Webstore Suite users, your structured product information can also power customer-facing product pages.
This allows customers to browse products visually while the AI Agent helps them:
- Understand specifications
- Compare options
- Explain differences
- Answer technical questions
- Identify suitable products
- Continue the sales or rental conversation
Your Webstore and AI Agent can therefore work together around the same product knowledge.
6. Start With Your Priority Products
You do not need to complete your entire catalogue before starting.
Begin with:
- Your most important product categories
- Your most frequently requested products
- Products that receive the most technical questions
- Products that generate the most enquiries
Expand your product knowledge over time.
7. Key Takeaway
For complex products, good product data matters.
Start with clear product identity, useful descriptions, important specifications and relevant technical documents.
Then improve the product knowledge as real customer questions reveal what information is missing.