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Solutions · Catalog Foundation

Complete and enrich every product record.

Agents that build the product records everything else reads from. SEO, translation and marketplace work all pull from the same fields, so this is where the work starts.

The problem

The catalog is the constraint

A product record starts life in three or four systems at once. An ERP with codes and no copy. Supplier files in whatever shape the supplier felt like sending. A PIM built to hold specifications rather than to sell them. A spreadsheet holding the hand-offs together, maintained by the one person who understands it.

None of that is wrong, and replacing it is not the job. The problem is narrower: the fields that decide whether a product is found and bought, fit, material, dimensions, the variant it belongs to, the category it sits in, are the fields no system owns. So they get typed in by hand, once per channel and once per market, by the team that is already the constraint.

The cost shows up in assortment. Every channel and every market that needs the same fields retyped is a channel that opens later, with less of the catalog on it. Everything above this layer reads from the same records, so fixing the record once is what makes the other seven areas cheap.

Common use cases

The jobs on the record

A few of the ones that run here. Each is a single workflow with its own output and its own numbers, and a job you do not see named is usually a variant of one that is. Run one, or run the set.

Data Enrichment

Builds publishable product pages from your own data or your suppliers, and recovers the specs they left out from sources online.Moves: time per product

Matching

Decides whether an incoming record is a product you already carry, links it to the canonical entry, flags duplicates.Moves: duplicate rate

Variant Grouping

Turns a flat SKU list into a family: one parent with the shared content, the children linked to it.Moves: PDPs per product

ERP Codification

Produces the codification your ERP needs before a supplier product reaches the PIM: code, dimensions, weight, packaging.Moves: days to sellable

Catalog Migrator

Maps an existing catalog onto a new platform schema, and surfaces the gaps that need a merchant decision.Moves: weeks of replatforming

Product Image Generation

Generates a catalog-grade image from the product’s own data, for the SKUs that have no photography at all.Moves: SKUs without a shoot

Product onboarding

A supplier file in, a sellable product out

Twenty suppliers, twenty formats: an XML feed, a CSV dropped on SFTP, an API, a spreadsheet somebody maintains by hand. Every field is mapped onto your own attribute model and normalized on the way in: currency on price, category paths flattened, image URLs rewritten, and a field with no home is flagged rather than quietly dropped. The specs the supplier left blank, fit, material, compatibility, get read off the product imagery and the datasheet, then recovered from sources online and cross-checked. Every value carries where it came from.

Data Enrichment

CommerceClarity agent · Apparel EU

Supplier feed · New products1
Enrich attributes · By family2
Score on golden set · Threshold 90%3
Write back to PIM · Akeneo4

The feedback loop

Every correction makes the next run better

Your team judges the output. A value is right, or it is wrong and gets fixed once. Each verdict changes the next run: the score on your own sample moves, and the agent starts from what the last one learned. So the share of values that need a person keeps falling, measured on your catalog and your rules rather than on an average of everybody else.

The platform flagging a product description as too generic for the brand tone, with the suggested rewrite and the two answers.

How we work

Live on your catalog in weeks, not months

We run all four phases with your team, on the stack you already have, and each one ends with a number you agreed in advance.

  1. 01

    Design

    We map how your data comes in and how the result goes back out, then agree the business KPI the agent has to move.

  2. 02

    Implement

    We build the agent and test it on a sample of your own catalog, agreed with you. Every change is scored against that sample, so the tuning runs on numbers.

  3. 03

    Prove

    Done means the KPI reached target on the full catalog, and you have seen the numbers yourself.

  4. 04

    Evolve

    We stay on the account. Your corrections keep improving the agent, the KPI holds where you need it, and the next use case starts from the context this one already built.

Enterprise, on a catalog you cannot break

It runs on the stack you have

It reads your PIM, ERP and DAM through their APIs and writes the result back to them.

A person approves everything that publishes

Every value is checked against your rules, and anything uncertain goes to review before it reaches a channel.

Your data stays yours

Your own isolated workspace. It is never pooled into shared models, and you can export it at any point.

Every run is logged

Each value carries its source, its run and its score, so you can see why a wrong one went wrong.

Questions

What retailers ask us

Who runs the agents, us or you?

We do, on your data, category by category, and we stay on the account after go-live. What you are buying is the outcome on your catalog, not a licence and a manual.

Do we have to replace our PIM or ERP?

No. The agents read your PIM, ERP and DAM and write back to them. We sit on top of the stack you already run, and mapping how data enters and leaves is the first thing we do.

What if the agent we need does not exist yet?

Most of these already run for another retailer and adapt to your catalog, and that adaptation is part of the service. Where a workflow is not covered, we design the agent with you and scope it separately.

How do you know the output is right?

The KPI, agreed before any build, says whether the business moved on the real catalog. Underneath it we score the work itself against your rules on a sample of your catalog, and that score re-runs on every change.

Whose data is it?

Yours. The corrections your team makes are what the agents learn from, and both the data and the corrections stay exportable at any point.

Start with one use case

One use case, run end to end on your own catalog. That is enough to see what it moves and what it costs per product, before you commit the rest.