Catalogues with thousands of variants, accessories and compatibility rules make configuring complex products a demanding process for both customers and the sales network. An AI-powered product configurator interprets requirements expressed in natural language, guides users towards valid combinations and checks technical and production constraints in real time. Combining AI with deterministic rules also enables automatic generation of quotations and documentation, simplifying the process from the initial request to the final configuration.<\/p>\n\n
When a catalogue contains numerous variants, choosing a product involves more than simply selecting an item. Component compatibility, technical specifications, production constraints and possible alternatives all need to be checked.<\/p>\n\n
The complexity affects both the customer, who may struggle to navigate the available options, and the salesperson, who must turn the stated requirements into a technically correct configuration that can be manufactured.<\/p>\n\n
An incorrect configuration may only be discovered at later stages, making it necessary to revise the offer or quotation.<\/p>\n\n
Artificial Intelligence can make this complexity easier to navigate, while a rules-based engine ensures that the configuration is valid.<\/p>\n\n
Step 1 \u2014 Interpreting Requirements in Natural Language<\/p>\n\n
Users can describe what they are looking for in natural language, without necessarily knowing the technical structure of the catalogue.<\/p>\n\n
The AI interprets the request, identifies the relevant requirements and guides the user towards the most suitable features and options.<\/p>\n\n
The complex catalogue is thus transformed into a simpler, guided interaction.<\/p>\n\n
Step 2 \u2014 Validating Compatibility and Constraints<\/p>\n\n
The identified proposals are checked against the technical and production rules defined in the configurator.<\/p>\n\n
AI and the configuration engine therefore perform complementary roles: the AI understands the request, interacts with the user and suggests possible solutions; the configurator carries out deterministic, repeatable checks to ensure that the combination is valid and can be manufactured.<\/p>\n\n
Step 3 \u2014 Quotation and Documentation<\/p>\n\n
Once the configuration has been completed and validated, the collected information can be used to automatically generate the quotation and the associated documentation.<\/p>\n\n
The sales process can then continue without manually transferring and reconstructing the information gathered during configuration.<\/p>\n\n
Processing can also be implemented on customer-owned servers, according to the organisation\u2019s infrastructure requirements.<\/p>\n\n
Fewer Incorrect Configurations<\/p>\n\n
Checking technical and production constraints makes it possible to catch invalid combinations before they reach subsequent sales or production stages.<\/p>\n\n
Faster Sales Cycles<\/p>\n\n
Guided configuration and automatic quotation generation reduce the manual steps needed to turn a requirement into an offer.<\/p>\n\n
A Simpler Experience for Customers and Salespeople<\/p>\n\n
Natural language interaction makes a complex catalogue more accessible, without requiring users to know all the available technical specifications in advance.<\/p>\n\n
New Upselling Opportunities<\/p>\n\n
The AI can suggest options, accessories and alternatives that match the user\u2019s stated requirements, helping identify additional sales opportunities.<\/p>\n","protected":false},"excerpt":{"rendered":"
Catalogues with thousands of variants, accessories and compatibility rules make configuring complex products a demanding process for both customers and the sales network. An AI-powered product configurator interprets requirements expressed in natural language, guides users towards valid combinations and checks technical and production constraints in real time. Combining AI with deterministic rules also enables automatic […]<\/p>\n","protected":false},"author":3,"featured_media":3340,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[42,35],"tags":[],"class_list":["post-3339","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-for-manufacturing","category-case-studies"],"yoast_head":"\n