Published on
August 14, 2026
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5
min
Managing product information at enterprise scale is rarely just about storing data.
As catalogues grow, so does the network of information surrounding every product: categories, variants, brands, attributes, options, data schemas, tags, publication states and validation rules. Product teams need to understand not only where information lives, but also how these different structures relate to one another.
Lidia PIM LEO introduces a different way to interact with that complexity.
Built as an intelligent assistant within the Lidia PIM environment, LEO allows users to work with product information through natural language. Instead of requiring every question to begin with the right menu, filter or technical field, users can start with what they actually want to know.
The result is a PIM experience designed to feel less like navigating a database and more like having a conversation with the product catalogue.
Modern commerce organisations often manage thousands - and sometimes far more - product records across multiple channels, markets and business models.
But catalogue scale is only one part of the challenge.
A product can sit within a category hierarchy, inherit a data schema, contain multiple variants, depend on specific options and properties, carry different tags and move through several stages before it is ready to be published.
For experienced PIM users, these relationships become part of everyday work. For newer users, they can create a considerable learning curve. And even experienced teams can spend time switching between screens, adjusting filters or tracing the relationship between different records.
LEO is designed to reduce that friction by adding an intelligent conversational layer to Lidia PIM.
One of LEO's core capabilities is its ability to understand natural-language requests and identify what type of PIM information the user is looking for.
A user might ask about a product, variant, category, brand, data pool, option, property or tag. LEO can recognise the underlying intent and route the request towards the relevant information.
That changes the starting point of product-data discovery.
Rather than translating a business question into a series of menus and filters, users can begin with questions such as:
Some questions are about the organisation's actual catalogue. Others are about how the PIM itself works. LEO is designed to handle both.
Its PIM-specific knowledge covers core concepts and workflows including categories, Data Schemas, Data Pools, products, variants, brands, options, properties, custom lists and tags, together with navigation guidance, common troubleshooting scenarios and access rules.
That means LEO can function not only as a way to retrieve product information, but also as an in-product guide to the PIM environment.
Natural-language product searches are not always neatly structured.
A user may describe the product they are looking for without using the exact terminology stored in the catalogue. At other times, the request may contain a precise condition — a particular brand, category, code or status — where exact matching matters.
LEO is designed to work across both situations.
Its search architecture combines semantic search with structured, parameter-based catalogue queries. Semantic search helps the system understand the meaning behind a request, while structured filtering helps preserve precision when the question contains specific product-data conditions.
The two approaches complement one another.
For product teams, the important part is not the underlying query syntax. It is the ability to express a request naturally while still connecting the answer to real catalogue information.
LEO then uses retrieved product data as context when generating its response, helping keep answers anchored to information available within the system rather than relying only on a language model's general knowledge.
A useful assistant needs more than the words typed into a chat box. It also needs to understand where the conversation is taking place.
LEO is designed with application and entity context in mind.
If a user is working within a particular area of Lidia PIM or viewing a specific product record, that context can become part of how the assistant interprets the question. This supports more natural follow-up interactions and reduces the need to repeatedly explain what the user is looking at.
Conversation history can also provide continuity for follow-up questions.
The objective is simple: make interaction with product information closer to the way people naturally ask questions, while keeping the PIM context behind those questions intact.
Product information is structured by nature, so an intelligent PIM experience should not be limited to paragraphs of generated text.
LEO is designed to return information using interface components suited to the result. Depending on the request, responses can include conversational explanations alongside structured listing cards, product-data completeness summaries and quality issue views.
This matters when moving from a question to an action.
A user asking for a definition may need a concise explanation. Someone searching the catalogue needs identifiable records. A product-data manager investigating catalogue quality may benefit more from a structured view of missing information or data issues.
By adapting the presentation of the response to the task, LEO can make AI interaction a more native part of product information workflows rather than a separate chat experience.
Artificial intelligence in enterprise software becomes most useful when it understands the environment in which people are working.
For Lidia PIM, that means understanding the language of product information itself: schemas and categories, products and variants, options and properties, publication workflows, data quality and the relationships connecting them.
LEO brings that domain knowledge together with natural-language interaction and access to catalogue information.
The potential benefit is broader than faster search. It can make PIM knowledge easier to access across teams, help users understand unfamiliar processes within their workflow and reduce the distance between a business question and the product data needed to answer it.
For organisations managing increasingly complex catalogues, this represents an important shift: from navigating product information to interacting with it.
And that is the idea behind Lidia PIM LEO — making sophisticated product information management easier to understand, easier to explore and more natural to use.
Lidia Commerce is a modular digital commerce platform supporting B2C, B2B and B2B2C business models. Its API-first architecture helps enterprises manage and scale commerce operations while integrating with existing business systems.
