★★★★★
The Checkout Loom team were easy to work with and brilliant at the work itself. I highly recommend them to anyone who needs dependable automation.
IveraAutomation client
Verified
AI product data enrichment for ecommerce
AI product data enrichment for ecommerce that fills the attributes, categories and fields your filters and feeds depend on
AI product data enrichment for ecommerce solves the problem that sits underneath most catalogue complaints, which is that the structured data behind a product, its size, material, colour, category and identifiers, is missing, inconsistent or buried in a free-text title, so filters fail, shopping feeds are disapproved and search returns the wrong things. We extract attributes from your text and photos, map each product to the right taxonomy, normalise values to one format, fill the gaps with evidence and score every record so quality is measured and not assumed.
- Fixed price from $1,780, paid once
- Every value has evidence behind it
- A quality score for every record
- Platforms supportedShopify with its standard product taxonomy, WooCommerce attributes, BigCommerce and feeds for Google Merchant Center and marketplaces.
- Typical storeStores with hundreds to tens of thousands of products whose filters are thin, whose feeds show warnings or whose search misses obvious matches.
- Source dataTitles, supplier text, specification sheets, product photos and your existing attribute fields.
- Catalogues coveredOne catalogue per plan, with extra catalogues and languages available as priced options.
- OutputsExtracted attributes, taxonomy mapping, normalised values, identifiers, a quality score and a gap report.
- Live inTwo to three weeks for a first batch and up to eight weeks for tens of thousands of products.
The costly disconnect
Why incomplete product data quietly breaks filters, feeds and search
A shopper filtering for navy, waterproof and twenty litres finds nothing, a shopping feed is warned about missing attributes and an on-site search for rucksack skips every product titled daypack, and in each case the cause is not the design but the data.
Attributes trapped in titles
Size, colour and material are written inside the title or description as free text, so no filter can use them and no feed can read them. It is easy to overlook until a busy week exposes it, and by then the cost to ecommerce is already counted in refunds, hours or lost sales in the product data enrichment work.
Feeds that Google warns about
Merchant Center expects fields such as brand, identifiers, colour, size and category for many product types, and products without them are limited or disapproved. The fix is not more effort but a better rule, and that is exactly what the product data enrichment build for ecommerce puts in place.
Values that disagree
Navy, dark blue and midnight appear as three colours, and twenty litre, 20L and 20 l become three sizes, which splits every filter into fragments. Many owners only notice this problem when a customer complains, so catching it earlier is one of the quiet benefits of product data enrichment for ecommerce.
Wrong or missing categories
Products placed in a vague category or the wrong taxonomy node lose category filters, marketplace placement and shopping feed relevance. Left alone, this problem repeats with every order, which is why product data enrichment for ecommerce is built to handle it by rule and not by memory.
Different data for each channel
Each marketplace asks for its own attributes, and filling them by hand for every channel repeats the same work with different spelling. It is easy to overlook until a busy week exposes it, and by then the cost to ecommerce is already counted in refunds, hours or lost sales in the product data enrichment work.
No measure of data quality
Without a score per record, nobody knows how many products are incomplete, which makes the problem impossible to prioritise. The fix is not more effort but a better rule, and that is exactly what the product data enrichment build for ecommerce puts in place.
- 3spellings of one colour are common in a catalogue that grew without a value list, which fragments every filter.
- 93%of records have all key attributes after our enrichment, against a typical starting point well below half.
- 1quality score per record shows exactly which products are weak and which field is missing.
Sources: Google Merchant Center product data specification; Shopify product taxonomy documentation; completion rates come from catalogues we have enriched and are indicative.
What changes in your catalogue once product data is complete, consistent and scored
Every product carries the same structured fields in the same format, and a score shows what is still missing.
Your product data today
- Attributes sit inside titles and descriptions.
- Colours and sizes appear in many spellings.
- Categories are vague or wrong.
- Feeds show missing attribute warnings.
- Nobody knows how complete the catalogue is.
Your product data afterwards
- Attributes are extracted into their own fields with evidence for each value.
- Values are normalised to one list, so filters group correctly.
- Products are mapped to the right taxonomy category.
- Feeds carry the required fields for each channel.
- A quality score for each record tracks completeness over time.
What your catalogue report shows
- Filters that work.
- Approved shopping feed items.
- Search that finds the right products.
- Less manual attribute entry.
- A measured, improving catalogue.
What the system extracts and maps
- Extracting size, colour, material and other attributes from text and photos.
- Normalising values to one approved list per attribute.
- Mapping each product to the right taxonomy category.
- Filling missing identifiers and fields where evidence exists.
- Scoring each record and listing the gaps that remain.
What a data owner decides
- Approving the attribute list and value lists.
- Values the system marks as uncertain.
- Category decisions for ambiguous products.
- Identifiers that must come from the supplier.
- Which channels’ requirements come first.
Which online stores gain most from automated product data enrichment
If your filters feel thin or your feed shows warnings, the data is the cause, and enrichment fixes it at the source for every channel at once.
Stores with Merchant Center warnings
Complete required fields move items from limited to approved and improve shopping visibility. This is the kind of situation where a fixed-price build is easier to justify than a monthly subscription for product data enrichment for ecommerce.
Stores with large catalogues
Thousands of products cannot be completed by hand, and extraction scales. We look at this situation in the audit and give ecommerce an honest view of whether the build is worth doing now or later in the product data enrichment work.
Stores selling on marketplaces
One enriched record feeds each channel’s own attribute requirements. If this describes your business, the product data enrichment work for ecommerce is likely to pay back quickly, and we will tell you plainly if it will not.
Stores that merged suppliers
Different supplier vocabularies are reconciled into one set of values. Owners in this position often say the biggest change is the time returned each week, and product data enrichment is built to deliver that first for ecommerce.
Stores with weak site search
Complete, normalised attributes make on-site search and filters return the right products. This is the kind of situation where a fixed-price build is easier to justify than a monthly subscription for product data enrichment for ecommerce.
Stores preparing for AI shopping assistants
Structured, complete product data is what shopping agents and search features read first. We look at this situation in the audit and give ecommerce an honest view of whether the build is worth doing now or later in the product data enrichment work.
The events that start and finish each enrichment pass
Enrichment runs when new data arrives, when a record is changed and when a score falls below your threshold.
product_createdA product is added
The new record is extracted, mapped and scored before it is published. Because this trigger is handled separately from the others, a problem with it never delays the rest of the product data enrichment work for ecommerce.
supplier_file_receivedA supplier file arrives
New and changed rows are enriched and compared with existing values. You decide how much of this runs without approval, and for ecommerce we usually begin with a review step until the first results look right in the product data enrichment work.
record_scoredA record is scored
Completeness, consistency and evidence are scored, and records below your threshold are queued. Anything this trigger cannot decide safely is passed to you with the record attached, so product data enrichment for ecommerce never acts on a doubtful reading.
review_requestedA value is uncertain
Uncertain values go to a reviewer with the evidence beside them. We test this moment on your own records before launch, so the product data enrichment build for ecommerce has met it before it meets a real customer.
batch_publishedA batch is written back
Approved values are written to the store fields in batches with a rollback copy. The timing of this step is written down in the rule table, which means you can change when it happens without asking us to rebuild anything in the product data enrichment build for ecommerce.
requirements_changedA channel changes requirements
A new required attribute triggers a gap report and a fill pass for affected products. If the same event arrives twice, the second copy is recognised and ignored, so product data enrichment for ecommerce never repeats an action by accident.
One product followed from a bare title to a complete, scored record
This shows how a thin record becomes a complete one, with evidence and a human check on the uncertain parts.
- 01
The record is read
A product arrives with the title navy waterproof daypack 20L and a photo, but empty colour, material, volume and category fields. Because the steps are separate, a change to one of them, such as a new rule for ecommerce, does not disturb the others in the product data enrichment flow.
- 02
Attributes are extracted
The text gives volume and waterproof, and the photo shows a navy pack with a nylon texture and a hip belt, each value recorded with its evidence. The same step is repeated for every item in the batch, so the tenth record receives the same care as the first when product data enrichment runs for ecommerce.
- 03
Values are normalised
Navy is matched to the approved colour value, 20L becomes twenty litres in the standard format and material is set from the supplier sheet. Anything unusual at this point is flagged in plain words, which saves you from reading logs to understand what the product data enrichment work found for ecommerce.
- 04
The taxonomy is mapped
The product is placed in the hiking backpacks category, and the category’s required attributes are checked against the record. At the end of this stage you can pause, adjust a rule or carry on, so product data enrichment for ecommerce always moves at the pace that suits you.
- 05
Gaps are filled or flagged
The product weight is found in the supplier sheet, but the GTIN is missing and cannot be guessed, so it is flagged for the supplier. Every step like this is recorded, so the product data enrichment work for ecommerce can be traced from the first input to the final result without any gaps.
- 06
The record is scored
The record scores ninety-six, the one gap is listed and the approved values are written back in a batch with a rollback copy. If the step cannot finish, the record says why and waits for you, so product data enrichment for ecommerce never moves on with a half-completed task.
Pricing
Price your AI product data enrichment
Choose a plan, tell us how many products and channels you have, and add any extras, and the total and live date update as you go.
What AI product data enrichment costs and what raises the price
Our fee is fixed and paid once, while model usage and any PIM or feed tool subscriptions are billed by their own providers.
Number of products and attributes
More products and a longer attribute list mean more extraction and review. Owners who run product data enrichment for ecommerce often find this factor is the one that most changes the quote, which is why we ask about it first.
Categories and product types
Each product type needs its own attribute list and tests. We check this factor against your real records during the audit, so the number in the quote comes from your data and not from a guess in the product data enrichment build for ecommerce.
Channels and languages
Each marketplace and language adds attribute sets and checks. This is one of the factors we price in writing before work starts, so the product data enrichment quote for ecommerce reflects your real situation and not an average.
Image-based extraction
Reading attributes from photos adds vision processing and confidence review. If this factor is small for you, it adds little to the price, and the written quote shows exactly how much it did add for ecommerce in the product data enrichment work.
Review depth
Several reviewers or regulated attributes lengthen review. You can lower the cost by tidying this part first, and we say so plainly in the plan when the product data enrichment work for ecommerce would benefit.
Reporting depth
Catalogue dashboards and per-field scores take extra build time. Anything extra that this factor creates later is quoted before it is built, so the fixed price for product data enrichment never grows by surprise for ecommerce.
Model usage, PIM platforms and feed tool subscriptions are billed by those providers.
Next step
Ready to price AI Product Data Enrichment for Ecommerce?
AI product data enrichment for ecommerce reads titles, supplier text and product photos to extract attributes such as size, material and colour, maps each product to the right taxonomy category, fills the fields that filters and shopping feeds depend on, and scores every record so gaps are visible and fixed.
- One fixed price from $1,780, paid once
- Evidence stored for every value
- A quality score for every record
Three more data jobs the same enrichment can do
Once records are structured and scored, related work becomes straightforward.
Feed clean-up for Merchant Center
Titles, identifiers, categories and attributes are checked against feed requirements before submission. You can switch this job on later without rebuilding anything, so ecommerce can start with the core product data enrichment work and grow when the time is right.
Marketplace attribute mapping
One record is mapped to Amazon, Etsy and eBay attribute sets and category trees. Results from this job appear in the same weekly report as the main product data enrichment work, giving ecommerce one place to judge whether it earns its keep.
Search and filter tuning
Normalised values and synonyms improve on-site search and filter groups. We include this idea because owners who run product data enrichment for ecommerce often ask for it within a few months, and it is cheaper to plan for it early.
Which data the enrichment pipeline reads and writes and where each fact is the source of truth
Enrichment adds structure without losing the source, so every value is stored with where it came from.
| Store object | Fields we use | Direction | Source of truth |
|---|---|---|---|
| Source text | Titles, descriptions, supplier sheets | Read only | Catalogue and supplier files |
| Product images | Photos and image attributes | Read for extraction | Store media |
| Attribute schema | Attributes, allowed values, units | Read and enforced | Attribute list you approve |
| Taxonomy | Category tree and required attributes | Read for mapping | Shopify taxonomy or channel tree |
| Enriched values | Value, evidence, confidence, reviewer | Written by the pipeline | Enrichment store |
| Quality scores | Completeness, consistency, evidence | Calculated per record | Scoring log |
Shopify publishes a standard product taxonomy with categories and attributes, and the pipeline maps to it so that filters and feeds can use the same structure.
Edge cases in product data enrichment that we test before launch
Extraction is only useful when it is right, so we test the situations where machines most often guess.
Values that cannot be known
GTINs, certifications and exact materials are never invented, and missing ones are listed for the supplier. When the case appears, the workflow follows a written rule and records the choice it made, so you can review it afterwards if you wish in the product data enrichment build for ecommerce.
Conflicting sources
When the title, the sheet and the photo disagree, a source ranking decides and the conflict is flagged. If a rule cannot settle the case safely, it goes to you with the record attached, and product data enrichment for ecommerce waits instead of guessing.
Units and sizes by market
Measurements are stored in a base unit and displayed in each market’s units. Cases like this are the reason the product data enrichment build for ecommerce includes a trial run, because a tidy example never shows them but real records do.
Colour that depends on lighting
Colours from photos are matched to a value list and marked as lower confidence when the image is ambiguous. Your answer to this case is added to the rule table, so the same situation is handled automatically the next time it appears for ecommerce in the product data enrichment work.
Variants with partial data
Variants inherit shared attributes and carry their own differing ones, so each variant is complete. The weekly report counts how often this case occurs, which tells ecommerce whether it deserves a better rule or is simply rare in the product data enrichment work.
Products that fit several categories
Ambiguous products are given a primary category and flagged, so a person decides. Nothing about this case is hidden from you: it appears in the log with the date, the record and the rule that dealt with it in the product data enrichment build for ecommerce.
Manual attribute entry, a PIM platform and a built enrichment pipeline compared
Manual entry is accurate and slow, a product information management platform is powerful and heavy, and a built enrichment pipeline adds extraction, mapping and scoring to the store you already have.
| Manual entry | PIM platform | Checkout Loom build | |
|---|---|---|---|
| Scales to thousands of products | – | ✓ | ✓ |
| Extracts attributes from text and images | – | Partly | ✓ |
| Maps taxonomy and channel attributes | Partly | ✓ | ✓ |
| Works with your current store | ✓ | Partly | ✓ |
| Evidence stored for each value | – | Partly | ✓ |
| Cost pattern | Staff time | Licence and set-up | Our one-time fee, then model usage as you run it |
Stores that already run a PIM keep it, and we add the extraction and scoring steps in front of it.
How the enrichment pipeline is built and what each part costs
We build around the catalogue you have and show the monthly cost of every part beside our fixed fee.
An attribute schema and value lists
The attributes, allowed values and units you approve, held as a sheet or in your platform’s metafields. Access to this layer is granted from your own account and can be removed at any time, so you stay in control of the product data enrichment build for ecommerce.
Text and image extraction
A current language model and vision model that read titles, sheets and photos and return values with evidence and confidence. Its limits and prices are checked again at the start of each project, because tools change their plans, and ecommerce should not pay for last year’s terms in the product data enrichment work.
Taxonomy mapping
Mapping to Shopify’s standard taxonomy, Google’s product categories or a marketplace’s category tree, with required attributes checked. Everything this layer does for product data enrichment is logged, so you can see what ran, when it ran and what it cost on any given week for ecommerce.
Validation and scoring
Code that checks formats, units and value lists and calculates a completeness and consistency score for each record. We test the layer on your own data before launch, so product data enrichment for ecommerce is proven on real records and not on tidy examples.
A review screen
A view of uncertain values with their evidence, where a person approves, corrects or rejects. If the layer fails or changes its terms, the handover guide explains how to swap it, so ecommerce is never stranded by one supplier in the product data enrichment work.
Make, Zapier or n8n
The workflow layer that watches for new products and files, runs the pipeline and writes the log. Its role is written in plain language in the handover guide, which means a new team member at ecommerce can understand it in a few minutes in the product data enrichment work.
How we keep enriched data accurate and trustworthy
Wrong data is worse than missing data, so evidence and review come before volume.
Evidence for every value
A value without evidence is not written, and low-confidence values go to review. We compare the results of product data enrichment with your own records on a regular basis, so a small drift for ecommerce is repaired before it becomes a large one.
Rollback for every batch
The previous values are stored so a batch can be restored in one step. If a check fails, nothing is sent or changed for that record, and it waits in a short list for you, because a delay costs less than a wrong action in the product data enrichment build for ecommerce.
Tests against known products
The pipeline is checked against a set of products whose data you know to be correct. The safeguard is tested during the trial batch by feeding it deliberately awkward records, so ecommerce sees how it behaves before real customers are involved in the product data enrichment work.
Alerts that name the field
Each alert gives the product, the field and the reason. Everything the safeguard decides is written to the log with the reason, giving ecommerce a trail that is useful when someone asks what happened and why in the product data enrichment work.
Re-scoring on change
Records are re-scored when a supplier file or requirement changes. During the month of corrections after launch, any gap in this safeguard is fixed in writing and the fix is added to the handover guide in the product data enrichment build for ecommerce.
Plain explanations after errors
If a wrong value reaches the store, the owner explains in writing what happened and adds the check that now prevents it. Reports on how often the safeguard fires show ecommerce whether the product data enrichment work is getting steadier or whether a rule needs attention.
Permissions, ownership and supplier data in product data enrichment
Enrichment reads your catalogue and supplier files and writes structured fields back, so access is narrow and ownership is yours.
Least-privilege access
We request product and metafield scopes only, with write access limited to the fields in your schema. At the end of the engagement you receive a list of every connection that was made, so nothing stays open by accident in the product data enrichment build for ecommerce.
Your data, your ownership
The enriched data and value lists are yours and are not reused for other clients. For product data enrichment on ecommerce, access is limited to what the job needs and can be withdrawn in a minute, so ending the work leaves nothing behind.
Keys you can withdraw
The store and model keys we use are created in your accounts, so withdrawing them ends our access immediately. Nothing in this arrangement requires you to share a password, because access is granted from your own account in a form you can revoke in the product data enrichment build for ecommerce.
Supplier costs kept private
Cost prices and terms stay out of public fields and out of model input. We keep no copy of your keys, records or customer details in systems that belong to us, and the handover guide shows you how to confirm that in the product data enrichment build for ecommerce.
Images used only for extraction
Product photos are used to read attributes and are not stored or reused elsewhere. Only the fields that product data enrichment needs are read, and only for as long as the task takes, which keeps the exposure for ecommerce as small as it can be.
Nothing left connected
At the end you receive a list of the schema fields and connections we added, and our own logins are deleted. Every access is logged in your own accounts, so you can see who or what touched a record and when, without asking us for a report in the product data enrichment build for ecommerce.
What you receive with your AI product data enrichment
Each plan ends with a complete catalogue, a schema you own and a score that shows the work.
- An attribute schema with value lists and units that you approve.
- Attributes extracted from text and photos for every product in scope.
- Values normalised to your lists, with evidence stored for each.
- Products mapped to the right taxonomy category with required attributes checked.
- Identifiers and fields filled where evidence exists and flagged where it does not.
- A quality score for each record and a catalogue-level report.
- A review screen for uncertain values.
- Feed-ready outputs for the channels in your plan.
- Thirty days of fixes after the first batch is written back.
Verified client reviews and the data rules behind our enrichment builds
The unedited words of verified clients of the Checkout Loom team appear below, followed by the rules every enrichment build follows.
- 0values are written without evidence, and low-confidence ones go to review.
- 93%of records have all key attributes after enrichment in our builds.
- 30 daysof fixes follow the first batch so the pipeline settles before we step back.
Rules follow the Google Merchant Center specification and Shopify taxonomy documentation, confirmed again at build time.
★★★★★
Everything was configured exactly as discussed, and the team made sure the automation was stable before delivering it. It has been a huge time-saver for our whole team, and it runs without any manual work.
BellamyUnited States
Verified
★★★★★
This was excellent automation work, and it is clear that the team has strong experience with AI and automation. I highly recommend the Checkout Loom team to other businesses.
FinnUnited States
Verified
★★★★★
I am genuinely impressed with the work the Checkout Loom team did on my AI and automation project. They are extremely professional, skilled, friendly and easy to work with, and I am delighted with the final result.
DavidUnited States
Verified
★★★★★
The Checkout Loom team built an automation that simplified my workflow and worked perfectly from day one. Communication was excellent, delivery was fast, and I would happily recommend the service.
RebeccaUnited States
Verified
★★★★★
They set up my automation remarkably fast and executed everything perfectly. Communication and service were top tier, so I could not be happier with the results and I highly recommend the Checkout Loom team.
AbbieGermany
Verified
★★★★★
The automation was set up in a clean and practical way by the Checkout Loom team. It saved me a lot of manual work and made the whole process much easier.
JanoUnited Kingdom
Verified
★★★★★
I highly recommend ordering from the Checkout Loom team. I had slow responses and poor communication with other providers, but this team was great and I am very happy with their service.
HarmetUnited States
Verified
★★★★★
I am a returning customer because my first AI automation workflow worked so well. Once again everything was built exactly as requested, and the whole process was reliable from start to finish.
WilliamUnited States
Verified
How the enrichment build runs from data audit to a scored catalogue
We measure the data before we change it, because a score only means something against a baseline.
- 01
Audit the catalogue
We export your products, measure completeness by attribute and list the main inconsistencies and feed warnings. The step has a clear finish line, which means you always know whether the product data enrichment work for ecommerce is on time or running behind.
- 02
Design the schema
We agree the attributes, value lists, units and source ranking with you in writing. Questions that arise here are answered in writing, so there is a record of every decision made about product data enrichment for ecommerce.
- 03
Map the taxonomy
We map your categories to the taxonomy and channel trees and list the required attributes for each. We keep each step small enough to check, because large steps hide mistakes and make the product data enrichment build harder to correct for ecommerce.
- 04
Extract and normalise
We run extraction on a test set, compare it with data you know and tune it until it is accurate. At the end of this step you approve or ask for changes, and the next stage of product data enrichment starts only after your reply for ecommerce.
- 05
Write back in batches
We write approved values in batches with rollback and re-score the catalogue. The time this step takes depends on how quickly your records and answers arrive, and we tell ecommerce early what we need to keep moving in the product data enrichment work.
- 06
Hand over
You receive the schema, the report and a month of fixes, and we remove our access. We use plain language in every document from this step onward, so anyone at ecommerce can read the plan and understand it in the product data enrichment work.
Estimate the attribute entry time product data enrichment could save
Enter your product count and the attributes you need and the estimate shows the manual entry time the pipeline replaces.
Values to fill in total–
Hours of manual entry–
Cost of manual entry–
The estimate shows the manual cost of the work and not our fee, and about one value in ten still goes to a reviewer in the pipeline.
AI product data enrichment questions, answered in full
These are the questions store owners ask about extraction, taxonomy, feeds, accuracy and cost before they order product data enrichment.
What is AI product data enrichment for ecommerce?
It is a pipeline that extracts structured attributes from titles, supplier text and photos, maps products to the right taxonomy category, normalises values, fills missing fields with evidence and scores each record so completeness can be measured and improved.
What does ecommerce product data enrichment do for my filters and search?
Ecommerce product data enrichment puts size, colour, material and category into their own fields in one consistent format, so filters group correctly, on-site search finds the right products and feeds carry the attributes channels require.
How is AI catalog enrichment different from writing descriptions?
AI catalog enrichment produces structured data fields that machines read, such as colour or volume, whereas description writing produces text for people, and the two work best together because descriptions can then be built from complete, verified data.
What is product attribute enrichment in practice?
Product attribute enrichment is the step of finding and recording the missing values of each product, for example reading a navy colour and a nylon fabric from a photo and a supplier sheet and storing both with their evidence.
Can automated product data enrichment make mistakes?
Yes, which is why automated product data enrichment in our builds stores evidence for every value, sends low-confidence ones to review, never invents identifiers and tests against products whose data you already know to be correct.
How does product information enrichment help Google Merchant Center?
Product information enrichment supplies the fields that Merchant Center expects, such as brand, identifiers, colour, size and category, which reduces warnings and disapprovals and makes more items eligible to show in shopping results.
What is ecommerce catalog data enrichment for marketplaces?
Ecommerce catalog data enrichment maps one record to each marketplace’s category tree and required attributes, so Amazon, Etsy and eBay listings are filled from the same verified data instead of being re-keyed.
What does AI product taxonomy mapping involve?
AI product taxonomy mapping places each product in a node of a standard tree such as Shopify’s product taxonomy, Google’s product categories or a marketplace tree, and then checks that the attributes required for that node are present.
What is catalog attribute extraction and what is product metadata enrichment?
Catalog attribute extraction reads values out of text and images, and product metadata enrichment is the wider job of adding categories, identifiers, tags and quality scores around them, and our pipeline does both and keeps the source of every value. Product content enrichment then builds descriptions and pages from the same verified record.
How long until enriched data is written back and what must I supply first?
A first batch is usually written back in two to three weeks. We need an export of your products, your supplier files, photos or image links and platform access, and we send a written checklist after you order.
Fix the data behind your filters, feeds and search once and for every channel
Choose a plan in the pricing calculator and continue to checkout, or request a quote with your store address, the number of products and any feed warnings you see today. We reply in writing within one working day with a confirmed start date and a short checklist, so the data audit begins right away.
- One fixed price from $1,780, paid once
- Evidence stored for every value
- A quality score for every record