PIM automation: how MaPS AI & Automation is reinventing the daily work of business teams

Profitability is no longer driven by revenue growth alone. Increasingly, it’s built on a company’s ability to absorb greater complexity without letting operational costs climb at the same pace. Produce more, launch faster, serve more markets — all while cutting the manual tasks, delays and inefficiencies that eat into margins: that’s the equation businesses now have to solve.

And it’s an equation that only gets harder as catalogues expand, sales channels multiply, launch cycles shrink, and personalisation and internationalisation become the norm. Marketing, e-commerce, data and digital teams are being asked to produce more content, in more formats, for more markets — without compromising on quality, consistency or economic performance.

In practice, a large share of their time is still swallowed up by repetitive tasks: completing product sheets, translating descriptions, checking attributes, cutting out images, extracting supplier data, or hunting down inconsistencies across thousands of SKUs.

Artificial intelligence can automate much of this work. But when it’s applied through disconnected tools, it often creates as much friction as it removes: more interfaces to juggle, data copied and pasted back and forth, instructions rewritten again and again, and results that have to be manually re-entered into the information system.

MaPS AI tackles the problem at the root: rather than bolting AI onto MDM, PIM and DAM processes, it builds it directly in. The platform orchestrates several specialised models and agents depending on the task at hand, and feeds their output straight into each company’s own workflows, quality rules and approval steps.

The goal isn’t to replace teams’ expertise, but to multiply their capacity — freeing up their time for the work that creates the most value: strategy, creativity, trade-offs and customer insight.

AI in the service of every business function

The use cases for artificial intelligence applied to product data are almost limitless.

They can span content creation, offer analysis, media processing, logistics checks, regulatory compliance, competitive intelligence, or the quality of data published across different channels.

The examples in this article are therefore not an exhaustive list. They illustrate how MaPS AI can combine a company’s data, the capabilities of AI models, and the business rules specific to each organisation.

The value doesn’t come from a model’s raw performance alone. It comes from its ability to act at the right moment, on the right data, within a well-governed process.

Why build AI into MaPS System rather than use standalone tools?

Plenty of AI tools already let you generate text, translate content or edit an image. What sets MaPS AI apart is that these capabilities are built directly into the product data repository and the operational processes themselves.

When a third-party tool is used separately from the PIM or DAM, users typically have to extract the information, write a prompt, copy the result, check it, and then manually paste it back into the right record and the right field. At scale, that approach simply doesn’t hold up.

With MaPS AI, processing can be embedded directly into MDM, PIM and DAM workflows without switching interfaces. The product’s context — its category, attributes, media, target markets and brand rules — can all be drawn on automatically.

This integration makes it possible to:

• Process hundreds or thousands of SKUs in a single operation;
• Apply the same rules across an entire catalogue;
• Write results straight into the relevant fields;
• Trigger automatic checks after each process;
• Route results through human validation;
• Track usage and model consumption (FinOps approach);
• Choose the artificial intelligence best suited to each need.

MaPS AI takes a vendor-agnostic approach. The platform can orchestrate different models depending on their capabilities, the level of quality required, the volume to be processed, or a company’s economic and technical constraints.

In strict compliance with confidentiality and GDPR standards, customer catalog data is never used to train third-party models. Furthermore, each company remains in full control of its own subscriptions, API keys, and management rules applied with each AI provider.

1. Marketing and e-commerce: generating content tailored to every context

Creating product sheets is often one of the biggest bottlenecks for marketing and e-commerce teams.

A single SKU may need several different descriptions depending on the brand, market, channel, language or customer type. Content built for a marketplace won’t meet the same requirements as a brand page, a B2B sheet or a mobile app.

MaPS AI can generate this content from the reliable data already held in the PIM.

Generating contextualised product descriptions

Based on the available attributes, MaPS AI can produce:

• A short description for a results list;
• A long description for a product page;
• Sales arguments and consumer benefits;
• Search-optimised titles and SEO metadata;
• Content tailored to a marketplace’s requirements;
• Distinct versions for B2B and B2C audiences.

Instructions can factor in the brand’s tone, words to use or avoid, expected length, and channel-specific constraints. So the AI isn’t producing generic text: it’s drawing on the product’s structured context and the company’s editorial rules.

Bringing an existing catalogue into line

In catalogues fed by multiple brands or suppliers, content is often inconsistent. Some entries are highly detailed, others incomplete. Tone varies, units aren’t standardised, and product benefits aren’t always expressed the same way.

MaPS AI can review this content, spot the discrepancies and propose catalogue-wide harmonisation.

This makes it possible to standardise:

• Title structure and description length;
• Vocabulary and editorial tone;
• How features are presented and units of measurement;
• Mandatory disclosures.

Adapting content across channels

Every channel comes with its own formats, character limits and publishing rules. MaPS AI can turn a single source of data into several tailored versions, without asking teams to rewrite every piece of text by hand.

A full description could, for example, be adapted into:

• A summary for a mobile app;
• A hook for a marketing campaign;
• Copy optimised for a marketplace;
• A technical spec sheet for a B2B portal;
• Talking points for sales teams;
• Structured content for search engines.

2. International expansion: translating without losing brand context

Translating thousands of product sheets is costly, and it often slows down entry into new markets.

A literal translation isn’t enough: it needs to respect industry terminology, local linguistic variations, brand tone and the target market’s conventions. MaPS AI can automate a first pass of contextualised translation based on content already in the PIM.

The translation can take into account:

• Brand glossaries and prohibited or mandatory terms;
• The differences between British and American English;
• Regional variants of the same language;
• Product context, category and technical specifications;
• The length allowed by the channel.

Workflows can then build in human review for the most sensitive markets, products or content. AI speeds up the groundwork, while local teams remain responsible for getting the final result right.

3. Creative studio and media teams: industrialising DAM processing

Producing media involves a lot of manual work: background removal, renaming, cropping, format conversion, quality checks, and adapting assets for different channels. Done by hand across thousands of files, these tasks eat up significant resources.

Built into the DAM, MaPS AI can support the entire media lifecycle.

Automatically classifying and documenting images

AI can analyse images to:

• Identify the product shown and recognise the type of shot;
• Distinguish a packshot from a lifestyle image;
• Generate keywords and alt text (ALT) for accessibility;
• Flag missing media and check visual consistency;
• Automatically link the file to the right product.

Automating background removal and format adaptation

MaPS AI can orchestrate automated processes such as:

• Removing or replacing a background (cut-outs);
• Cropping, resizing and format conversion;
• Adapting to the ratios required by marketplaces;
• Improving resolution and checking image quality.

Results can be stored in the DAM and automatically linked to the right product version.

Creating new visual variants

Depending on the models and services configured, AI can also help create new visual scenes based on existing assets. A packshot could, for instance, be placed into different visual settings or adapted for a seasonal campaign. These possibilities need to be governed by brand rules and validation steps.

4. Category management: analysing the offer and prioritising action

Category managers have to set priorities across thousands of SKUs. They need to identify which products need enriching, which segments are under-represented, where positioning gaps exist, and what signals are coming through in sales data or customer reviews.

MaPS AI can turn the data held in MaPS System into recommendations teams can act on straight away.

Prioritising enrichment

Rather than enriching an entire catalogue uniformly, AI can cross-reference several indicators:

• Sell-through rate, stock level and margin;
• Traffic, conversion rate and sheet completeness;
• Number of stores involved and seasonality.

Across a catalogue of 5,000 SKUs, MaPS AI can pinpoint products with strong commercial potential whose sheets are still incomplete. Teams then know exactly where to focus their efforts.

Analysing range diversity

MaPS AI can produce summaries of category composition: analysis by door type, noise level, wattage or features across a range. Results can be saved in MaPS System or presented as reports to support decision-making.

Making use of customer reviews

Manually reviewing thousands of comments simply isn’t realistic. MaPS AI can group them, summarise them and identify themes that come up most often (e.g. packaging hard to open, unclear instructions, sizing issues).

These insights feed directly into improving product sheets, requests sent to suppliers and assortment decisions.

5. Data management: checking, completing and standardising data

Data teams spend a significant chunk of their time spotting errors, reconciling information and filling in missing attributes.

MaPS AI can support them in this without replacing the deterministic rules already built into the MDM or PIM.

Detecting anomalies

AI can identify values that are statistically or semantically inconsistent, such as:

• A book weighing 50 kilograms or a shoe measuring two metres;
• A unit that looks wrong or a description that contradicts the attributes;
• An image that doesn’t match the stated colour;
• A price that’s oddly far removed from the rest of the range.

Converting and standardising units

Supplier data can arrive in different systems (kilograms, pounds, inches, centimetres). MaPS AI can help identify the units, convert them, and map them to the right attribute in the data model.

Classifying products

Based on a description or supplier documents, AI can automatically suggest a category, product family, logistics type or risk level before validation.

6. Logistics: getting the data that drives costs right

An error in weight or dimensions can lead to incorrect shipping costs, poor pick-and-pack decisions, or a higher rate of returns. MaPS AI helps logistics teams check data as soon as it’s brought in.

• Checking weights and dimensions: Automatic comparison against category norms, similar products and visual media.
• Recommending a mode of transport: Recommendations for packaging type, vehicle type or service level (standard vs specialist, temperature control).
• Flagging special requirements: Automatic detection of fragile items, hazardous materials, bulky goods or food products.

7. Procurement and suppliers: speeding up data onboarding at source

Buyers receive information in all sorts of formats: spreadsheets, catalogues, spec sheets and PDFs. These documents then need to be read, interpreted and entered into the PIM.

MaPS AI can automatically extract relevant information from these sources to pre-fill product sheets (SKUs, dimensions, weight, materials, regulatory and logistics data).

This automation drastically reduces supplier onboarding time and improves the quality of the information collected.

8. Compliance and regulation: identifying sensitive information

Regulatory requirements vary by product, market and channel. MaPS AI supports teams with:

• Identifying mandatory disclosures and warnings;
• Automatic reading of compliance documents;
• Spotting inconsistencies in customs codes;
• Matching a product to the rules that apply to a specific market.

9. Digital and channel management: checking the data after it’s published

Good-quality data in the PIM doesn’t always guarantee it’s displayed correctly on websites, marketplaces or reseller platforms. An outdated name or an old image can linger on a channel long after it’s been fixed at source.

MaPS AI monitors published data and compares live information against master data, generating channel-specific anomaly reports for digital teams.

10. Competitive intelligence: turning external data into insight

Using data collected within an authorised framework, MaPS AI helps analyse competitors’ pricing, range composition, sales messaging and customer reviews.

Ahead of launching a new product, teams can pinpoint expectations, pain points or features most valued by the market.

Thousands of scenarios, built from your data and business rules

The examples above represent only a fraction of the possible use cases. Every company has its own data, processes and priorities.

A bespoke use case can be built by combining:

• A trigger event;
• A source data set;
• One or more specialised AI models;
• Business rules and human-in-the-loop validation steps;
• An action carried out directly within MaPS System.

Cutting time-to-market without sacrificing quality

Automation isn’t just about producing more. It also speeds up the onboarding of new products and new brands.

On the Easypara project, fully onboarding a new brand and its products — a process that previously took more than a week — was cut to two days with MaPS System.

The goal is to strip out work that adds no value, speed up data preparation, and let teams focus on the checks and trade-offs that genuinely need their expertise.