We gather all of a product's attributes, descriptions and translated texts in one centre, and produce the enriched version for each sales channel's own template from there. A field that is missing or breaks a rule is caught before the product goes out to the channel. Product data no longer lives in a file on somebody's desktop.
In most businesses, product data does not sit in one place. The stock code and price are in the ERP, the product description is in a presentation marketing prepared, the dimensions and the technical table are in a spreadsheet that came from production, and the English text lives on the export manager's computer. The same product exists in five different places in five different states, and most of the time nobody knows which one is current. When you open on a new marketplace, these files are merged one by one; when the person doing the merging takes leave, the work stops. This is not a problem of negligence. Product information is an area everybody touches but nobody owns; as long as no owner is defined, it will keep scattering whatever tool is used.
The bill for this scatter is usually presented in the sales channel. The marketplace does not list the product because a mandatory attribute of the category is empty. The customer returns the product because they saw the wrong dimensions. Sales reports do not add up because the same product is listed under two different names in two channels. When a few hundred product descriptions have to be updated during a campaign, the work turns into a copy-and-paste mobilisation that takes several people days, and in that mobilisation some are always missed. For companies selling abroad the picture is heavier still: every language means a separate text for every product, and after a while it becomes impossible to track which version a translation was made from. As the number of channels grows, you notice that this burden multiplies not per product but per channel; and that is usually where the real bottleneck becomes visible.
Product Information Management (PIM) means holding this data in one centre and in a defined structure. Which attributes are mandatory is set out for each product group from the start: pile height and yarn type for a carpet, power and voltage for a machine, ingredients and allergen information for food. The product record is filled in against this template; a missing field shows on the record and does not have to be hunted down separately. The data that accumulates at the centre is then derived per channel: your own site wants the long description, the marketplace wants a title that fits its own category tree and character limit, the dealer catalogue wants the technical table. The same truth goes out in different moulds, but the source is single and a correction is made in one place.
To be honest, a PIM does not improve product data by itself. If you put incomplete and inconsistent data into the system, orderly but still incomplete data comes out. The hardest part of the implementation is not the software but cleaning up the existing catalogue and settling the attribute templates; that work takes your team's time and nobody can make those decisions in their place. A PIM is also not a stock or order system: price and stock quantity continue to live in the ERP, and the PIM reads them and carries them to the channel. What we can say with confidence is this: once the data is set up properly, opening on a new channel stops being a preparation job done from scratch and comes down to a mapping job of a few days.
Businesses working at the same time with their own site, several marketplaces, a dealer panel and a printed catalogue. Because each channel has its own title rules, category tree and mandatory fields, the same product is prepared by hand over and over. The real gain appears when that preparation is derived from a single central record. Once the number of channels goes past three, in most businesses this becomes a full-time job for one person.
Firms whose product count runs quickly into the thousands through variants such as colour, size, dimension and pattern. When the shared and differing fields of variants are not separated out, each variant starts to be managed as a separate record; as the catalogue grows, so does the margin for error. The benefit of a central structure is seen most clearly at this scale. Separating the fields the parent product and the variant have in common is the first and most lasting correction to make in a catalogue.
Firms managing product content in more than one language. Unit conversion in technical tables, warning texts that change by country and tracking which Turkish version a translation was made from cannot be run by hand. Once translation status is visible on the product, the risk of going live with a missing language disappears. As the number of languages grows, this tracking stops being something a spreadsheet can carry, and the organisation's source of truth remains a file on one person's computer.
Firms where catalogue knowledge lives on one person's computer and in their memory. When that person takes leave or resigns, the process of opening new products stops. The first concrete gain is usually here: knowledge moves from the person to the organisation, and a new team member can start working by looking at the template. Every step taken without making this transition extends the dependence on that same person a while longer.
Mandatory and optional fields are defined for each product group; the field's type, unit and option list are tied to rules. A carpet and a machine do not fill in the same form. The schema can be extended later, but getting it right from the start largely removes the clean-up work further down the line. When a new product group is added, the template is copied from an existing one rather than defined from scratch.
The parent product and its variants are kept separate but linked; shared fields are entered once and only the fields that differ are stored on the variant. In a range with a thousand variants, a description change is made in one place. The relationship between barcode, GTIN and the ERP stock code is held in this structure too, so the product in the channel and the product in the warehouse are tied to the same record.
Each field's language-specific version is stored separately; which language has been translated, and which needs updating because the source text changed, shows on the product. The list that goes to the translation team is produced by the system rather than tracked by exchanging files. Mandatory country-specific texts can be defined as separate fields. When a new language is added, what needs translating comes out as a single list.
A separate output template is defined for each sales channel: character limits, category mapping, the list of mandatory fields and title rules. The central data is transformed against that template. You can adapt the text shown in the channel without touching the central record, and keep the difference on record as well. When a marketplace changes its rules, a single template is updated. Adding a channel therefore becomes defining a new template rather than starting a new project.
Rules run before a product goes out to a channel: is a mandatory field empty, are there enough images, is the unit of measure defined, does the description exceed the character limit. A product that breaks a rule does not go out for publication, and the reason is listed. The aim is for errors to be seen in your own system rather than through a marketplace rejection. Rules can be differentiated by product group; each channel gets its own checklist.
Approved content is pushed to marketplaces and e-commerce platforms by API; for channels that want a feed file, XML or CSV is produced. The transfer result is recorded per product, and the reason for rejected records is visible in the system. Reading changes made on the channel side back into the centre can also be set up. In a bulk transfer, which products went through and which stayed behind is listed, and corrected records can be retried.
Who changed which field and when is put on record; content can be put through approval before publication. When a bulk update goes wrong, it is possible to return to the previous version. For audit purposes and brand consistency, this history becomes one of the most frequently consulted parts of the system. Who can change which field is limited by role; a translation role that cannot touch the price field can be defined, for example.
We map where the existing product data sits, which rule applies in which channel and who updates what. At this stage the variety of attributes is counted as well as the number of products; that is what really determines scope. Discovery generally takes one to two weeks and is done with the people who actually use the data.
Mandatory fields, units and option lists are agreed by product group; an owner is assigned to each field. This step is a decision step, not a software step, and in most projects it is the most debated part. The schema is delivered as a written data dictionary and becomes the measure for all the work that follows.
Existing products are migrated onto the new schema; duplicate records are merged and missing fields are reported. We cannot do the clean-up on our own; your team decides which record is correct. Depending on catalogue size and how scattered the data is, this stage generally takes 4-8 weeks. During the clean-up it also emerges which fields are never actually used, and the schema gets simpler.
A single channel and a limited product group are connected first. The transfer, the rejection reasons and the read-back are tested with real data. The other channels are not opened until the pilot is validated; this order prevents the most common roll-out mistake. Rule corrections that come out of the pilot are written back into the schema.
The remaining channels and product groups are opened up in stages. Use of the system is handed over to your team; how the schema is to be extended and how a new channel is added are left in writing. After go-live, transfer errors are monitored and resolved under support. The source code and the data are delivered to you.
The ERP holds the commercial and financial side of the product: stock code, unit, price, stock quantity, accounting account. The PIM holds the side of the product that is described: title, description, technical attributes, translations, channel texts and image relationships. The two do not overlap, they connect to each other. In practice the ERP product card remains the master source; the PIM takes that card as its reference and builds the marketing data on top of it. We do not want you to change your ERP, we read the data from it.
No. A PIM is not something that replaces your e-commerce platform, it is the source that feeds it. If the platform you use offers an API, we connect directly; if it does not, we produce a feed file. The same applies to the marketplaces. And on the day you do want to change platform, because the product data is already yours and in good order, the migration has lost its hardest part. In practice that is one of the side benefits of putting a PIM in place.
No, the clean-up is part of the project and is generally done after the schema is settled; trying to clean up first and build afterwards usually means doing the same work twice. But we want to say from the start that the decisions belong to you: your team decides which of two records is correct and which description counts as valid. We provide the process, the tools and the reports; someone who knows the ground has to decide whether the content is correct.
It is possible, but we build it with its limits. A language model can produce a draft description and a channel title from the attribute data, and can prepare a first version in translation as well. Against that, the risk of it inventing technical values is real, and for that reason the generated text does not go straight to publication but passes through an approval step. Fields such as dimensions, material and compliance information are not left to the model; those come from the data fields. An implementation that does not make this distinction turns error into something that scales.
It does not, and we do not recommend that we take that on. Price and stock quantity are fast-changing data with financial consequences; their source should be your ERP or stock system. The PIM reads them, carries them to the channel and applies a channel-level price rule if needed; but it does not try to be the single source of truth. Implementations where both systems write the same data end up, after a while, at a point where nobody knows which is correct. Order picking and stock allocation are a separate subject too.
An attribute schema defined by product group and a written data dictionary; a central record where all products are gathered together with their variants; multilingual content and translation status tracking; channel templates and quality control before publication; marketplace and e-commerce transfers along with transfer result records; change history and an approval flow. Everything, including the source code, the database and the product data that accumulates, belongs to you.
In a 30-minute discovery call we listen to what you need and tell you honestly whether custom development or an off-the-shelf product is the better answer.
We bring photographs, video, catalogues and design files together in a single archive, and record which product each file belongs to, which version is approved and where it may be used. The sizes and formats each channel asks for are derived automatically from the original, so nobody spends their time resizing files.
Details →We bring the store's till, stock, promotions and shift handover together in a single system. The POS here is not the virtual POS on your website; it is the real till on the counter. In multi-branch structures, prices and promotions are managed centrally and the day closes with the same discipline in every branch.
Details →We turn the production plan into a sequence at machine and line level: tooling and set-up times, bottleneck capacity, the date material will be ready and priority rules are calculated together. The result is a schedule that can genuinely be applied on the shop floor and a defensible due date that can be given to the customer.
Details →We bring production, quality, maintenance and inventory operations together in a shared data model. The same stoppage, the same lot and the same shift meet in one record instead of four separate places; shift handover is put in writing, and management looks at a single set of indicators.
Details →We keep machines, plant, vehicles and equipment on a single record from the moment they are bought to the moment they are disposed of. Warranty, criticality, spare parts, maintenance cost and depreciation accumulate on the same asset card; the decision to replace stops being a guess and starts resting on the record that has built up.
Details →In companies that sell machinery and equipment, service work usually starts on the telephone and ends in a notebook. We build the whole chain in a single system — from the service request to technician assignment, from the mobile work order to the parts used and the customer's signature — and track warranty cover and SLA times automatically.
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