Asking questions about your product data in plain language: what if your PIM finally answered?
The product data is there. Structured, centralized, governed in your PIM.
And yet getting to it is still far too often laborious.
This is one of the most common paradoxes in organizations managing complex product catalogs: the information exists, but using it remains slow and constrained.
A marketing team has to identify incomplete records before a launch. An e-commerce manager is looking for products with no images in a category. A product manager wants to spot outdated descriptions.
In most cases, that means navigating between several screens, using advanced filters, running an export, or asking a technical profile for help. All to reach data that is already in the PIM.
Plain-language querying lets every team ask questions of their PIM the way they would ask a colleague, with no filters and no technical queries. The platform analyses the question, queries the structured data and returns a usable answer in seconds. This is not magic: it is the result of an AI that knows your data model, your attributes, your completeness rules and your classifications.
What is plain-language querying in a PIM?
Plain-language querying lets you ask your PIM a question exactly as you would ask a colleague. The system turns the question into a query on the product data and returns a usable answer instantly.
A precise definition
Plain-language querying in a PIM is an artificial intelligence capability that lets you ask questions in everyday language and get directly usable answers from structured product data, with no complex filters and no technical queries.
This is not a generic layer. It is an AI that understands your data model, your product attributes, your completeness rules and your classifications. It queries your real data, not theoretical data.
The essential prerequisiteThis capability rests on one essential prerequisite: product data that is structured and governed in the PIM. The cleaner and better organized your data, the more precise and usable the answers. Discover why PIM structure is the real prerequisite of any AI project.
Why can a generic AI not answer questions about your product data?
Generic AI tools such as ChatGPT are useful for generating product content, rewriting descriptions or suggesting marketing copy. But ask them: “Which products in my catalog are incomplete?” They cannot answer. They have no access to your PIM, to your data model or to your business rules.
| Criterion | Generic AI | AI built into the PIM |
|---|---|---|
| Business context | None: answers in a vacuum | Understands your product data model |
| Catalog access | Impossible: data not accessible | Queries your real data directly |
| Answer quality | General, not usable | Precise, directly actionable |
| Governance | None: outside your workflows | Respects your rules and defined scopes |
| Scaling up | Unmanageable: manual copy-pasting | Instant processing across the whole catalog |
To go further on this distinction: generic AI vs AI built into the PIM, what you lose by confusing the two.
Concrete use cases for plain-language querying
Data quality control
Immediately identify incomplete or inconsistent records, by channel, by category or by status. What used to take an export and a manual analysis now takes seconds. Combined with the Product Data Score, it gives you continuous control over catalog quality.
Steering the product catalog
Analyse data quality by category, channel or period. Compare the state of the catalog before and after an enrichment campaign. Track how the teams progress on priority fields.
Tracking assets and media
Spot products with no images, with missing assets, or with assets that do not meet the specifications of the target distribution channel. Directly useful for teams preparing a launch or a catalog update.
Fast business analysis
Reach key information without going through exports or complex reports. Teams no longer depend on a technical profile to get a view of the state of their catalog.
What are the concrete benefits for each profile?
Marketing and content teamsInstantly identify the records to enrich before a launch, with no technical dependency. Prepare a campaign knowing exactly where the data stands.
E-commerce managersQuickly access data quality by category, by channel, by period. Detect anomalies before they hit sales or search rankings.
Product managersSpot missing data, inconsistencies and incorrect formats across their product families. Steer enrichment priorities without waiting for a report.
PIM administratorsAnswer business needs without building complex reports. Fewer requests, more time on governance and configuration.
What it changes for data governance
Beyond the time saved, plain-language querying changes the very nature of how product data is steered. Until now, catalog quality was measured periodically, on the basis of exports or scheduled reports. It was a snapshot, often out of step with reality.
With an AI answering in real time on structured data, steering becomes continuous. Anomalies are detected as they appear. Teams no longer wait for a weekly report to know where to concentrate their effort.
A natural evolution for PIM platforms
Plain-language querying marks a major shift in how PIM platforms are used, by making product data accessible to every profile, with no technical barrier.
PIM platforms first answered one central need: centralizing and structuring product data. Today the challenge is moving on: making that data genuinely accessible to every profile that needs it, at the moment they need it.
This is not a side feature. It is a new way of interacting with product data, and it sits squarely within the governance logic carried by a well-configured PIM.
Neo Datas: plain-language querying in Afineo
With Neo Datas, a module of the NEO AI assistant, Afineo natively integrates this capability into its platform. Teams ask their questions directly in the interface, get instant answers and stay in their workflow. Administrators keep control over the scope of accessible data and over the security of the environment.
Neo Datas works alongside Neo Editorial, which handles the correction, rewriting and generation of product content. Two modules, one single logic: an AI that knows your data and acts directly on it.
What if your PIM became searchable like a search engine?
Your product catalog already holds the answers. The real question is simple: do you have the means to reach them instantly today?
Armelle Allegret
Co-fondatrice d’Afineo, 25 ans d’expertise marketing au service des entreprises pour transformer leur gestion de l'information produit (PIM, DAM, MDM) en avantage concurrentiel.
Son obsession : des contenus produits clairs, fiables et efficaces.
Afineo, a key player in reference data management, offers a SaaS MDM, PIM and DAM platform to support the digital transformation of businesses. Our multi-domain cloud solutions make data management and data quality easier, while ensuring smooth integration with your digital ecosystem.
