The Challenge
The source file contained 9,527 references with uneven data quality, incomplete image coverage and product rows that could not all be resolved from a single identifier or source.
REAL-WORLD CASE STUDY
Product Catalog Recovery Across 9,527 References
A large supplier catalog contained thousands of product references with incomplete image coverage and uneven product signals. AIKIVORS applied a structured multi-source recovery workflow, parallel processing and quality control to turn the catalog into a more usable operational dataset.
CASE STUDY SNAPSHOT
REAL PROJECT DATA
9,527
SOURCE REFERENCES
7,479
REFERENCES WITH IMAGE
78.50%
IMAGE COVERAGE
6
PARALLEL WORKERS
Catalog references with recovered image
78.50%
THE PROJECT
Thousands of product references. One structured workflow.
The objective was not simply to find more images. The project required reliable correspondence between supplier rows, product identifiers and recovered assets, while keeping difficult records visible for additional processing.
The Approach
Product signals were evaluated through several recovery routes: direct identifier matching, multi-source refinement and targeted processing for valid or missing EAN situations, supported by six parallel workers.
The Result
7,479 references were associated with product images and structured into the working catalog. 2,048 references remained without image and stayed visible for further recovery rather than being hidden by an artificial completion rate.
BEFORE RECOVERY
The catalog already contained
valuable product signals.
Existing references, EAN values, designations and row-level metadata became the basis for recovery. Instead of replacing the source data, the workflow used it to guide matching, preserve traceability and determine which recovery route was appropriate for each product.
VERIFIED PROJECT METRICS
What the recovery workflow produced.
These figures reflect the recorded project state. They are presented without extrapolating a completion rate beyond the results actually obtained.
Source references
Total number of product rows in the working catalog.
References with image
Rows associated with a recovered or validated product image.
Image coverage
Share of the source catalog represented by the 7,479 image-backed references.
References without image
Rows retained for further recovery, equal to 21.50% of the source catalog.
ARTIFACT-VERIFIED BASELINE
The figures are tied to the audited final workbook.
The official M8 baseline reconciliation verifies the historical consolidated workbook against the immutable 9,527-row source. Source rows are preserved exactly, row-for-row and in order.
Verified final workbook metrics
Source rows preserved
Original source columns preserved exactly and in order.
9,527
Rows with local image
Non-empty historical local image filename.
7,479
Rows with remote image URL
Non-empty historical remote image URL.
7,479
Rows without image
Neither local image filename nor remote URL.
2,048
Image-backed rows represent 78.50% of the 9,527-row audited
historical final workbook. This percentage is calculated from
the artifact-verified counts above.
Additional verified quality indicators
Unique local filenames
Distinct local image filenames in the audited final workbook.
7,259
Unique remote URLs
Distinct remote image URLs.
7,320
Malformed remote URLs
Invalid remote URLs detected by the reconciliation audit.
0
Local / remote alignment gaps
Local-without-remote and remote-without-local.
0
Historical narrative figures such as 7,367 FTP rows and
8,265 post-recovery links remain stage-specific documented
claims and are not mixed into this artifact-verified baseline.
CONTROLLED SCALE
Volume increased.
The workflow stayed structured.
The catalog was divided into controlled processing ranges so several workers could operate in parallel. The objective was not speed alone: output consistency, traceability and the ability to revisit unresolved product rows remained part of the operating model.
THE HUMAN LAYER
Automation organizes the workload. Supervision protects relevance.
High-volume recovery still requires judgment around ambiguous references, unsuitable images and the practical usefulness of the final data structure.
Quality supervision
Recovered assets and difficult matches can be reviewed so obvious advertising visuals, logos, irrelevant images or weak product correspondence do not silently enter the final catalog.
Operational decisions
Unresolved product rows can be separated for another pass, different sources or manual attention rather than being treated as failures hidden inside a single completion statistic.
CASE STUDY FAQ
What this project demonstrates.
How large was the source catalog?
The official immutable source contains 9,527 non-empty product rows.
How many references ended with an associated image?
7,479 references were associated with an image in the recorded project state, representing 78.50% of the 9,527 source references.
Why were several recovery routes used?
Supplier product rows do not all contain the same quality of identifiers or designations. Direct EAN matching can resolve some references, while others require multi-source comparison or targeted recovery methods.
What happened to the references without images?
2,048 rows remained without either a local image filename or a remote image URL in the audited historical final workbook. Keeping these rows visible makes it possible to revisit them with additional sources or another recovery pass.
Can the same method be tested on a smaller sample?
Yes. The free 25-SKU audit is designed to test recoverability, matching quality and output structure on a representative sample before moving to a larger catalog.
YOUR CATALOG, NEXT
See what can be recovered from 25 of your own SKUs.
Send a representative product sample and evaluate the available recovery signals, image potential and output structure before committing to a larger catalog project.