product_keyAC820825titleKverneland AC820825 Fan Impellerattributes.*— MISSINGvalidation.*— MISSINGIntegrations · CSV & Excel
Enrich Product Catalogs from CSV or Excel Files
You do not need a finished API integration to begin working with product data. Many catalogs already move between suppliers, teams and systems as CSV or Excel files.
The handoff is not limited to enriched attributes: the configured output can carry validated technical fields together with SEO keyword targets, unique product copy, meta title, meta description and final content in the target language.
ENRIVAQ can use a structured CSV or Excel catalog as input to the enrichment workflow: map existing columns, research missing information, build structured output and validate the result.
File input is supported. A standalone enriched CSV/XLSX export is not claimed as a generic current capability; approved output moves into the connected catalog/site workflow.
Map, enrich and validate
Map columns
SCHEMAEnrich records
DATAValidate output
GATEShape approved output
EXPORTproduct_keyAC820825attributes.*accepted structured valuescontent.*approved content fieldsvalidation.*Accepted / Review / RejectedUpload existing catalog
Start with the spreadsheet your team already uses
The source file can contain the product data as it exists today, including incomplete or inconsistent fields.
Cleaning it manually before the pilot can hide the actual work the enrichment workflow is supposed to solve.
Each row carries enough identity and catalog context to map the product to a target output record; required fields follow the product category and deployment.
USE AS IS
ACCEPT
AVOID
IDENTITYCan we resolve the row?SKU, MPN, manufacturer or another implemented identifier.ROW KEYCan we return the result?Every output must map back to the original record.SCHEMACan columns be mapped?The target structure is defined before enrichment starts.REALITYKeep the mess visibleInconsistency is evidence for what the workflow must solve.Required columns · optional fields
Define the minimum fields for reliable processing
Required columns are mapped from the implemented workflow and may differ across product categories and use cases.
The minimum for reliable processing
product_keyStable value that lets the result return to the correct source row.identity_signalEnough to resolve the row to one real product.target_scopeSelects which product category and target schema apply.category_variationRequirements may differ by category and use case.Required columns
Stable product identifier + matching contextKeep useful existing data
titleExisting product title or supplier naming pattern.descriptionMay contain facts worth extracting, but is not ground truth by default.attributes.*Known values show what is already filled and support validation.categories + IDsPreserved where relevant to the implemented workflow.Optional columns
Manufacturer/MPN · category · existing attributes · description · EANMapping
Map spreadsheet columns into the target product schema
Supplier files often use their own column names and structures. Before processing, those fields need to be mapped to the product schema expected by the enrichment workflow.
Several supplier columns may describe the same canonical attribute under different labels. Mapping gives the batch a consistent target structure before enrichment begins.
Supplier columnsRoute / typeTarget field
supplier_mpnproduct_codestring
manufacturer_part_numberONE CANONICAL ATTRIBUTEweightkg
weightTYPED VALUEouter_diameterdiameter_unitnumber + unit
outer_diameterNORMALIZED UNITProcessing
Process the catalog as a batch, not as separate AI prompts
Once the file is mapped, records can move through the product-data workflow: identification, research, extraction, normalization, validation and content preparation according to the configured scope.
Batch capability
Run-specific; no public production-volume claim
Processing timeMeasured per run; no public generic benchmark
Automation coverageMeasured per project/run
Quality report
Return more than a finished spreadsheet
Where the implemented workflow supports it, a quality report can help the customer see which records were completed, which still contain missing data and which require review.
Quality visibility is attached to the workflow so accepted, reviewed and unresolved data remain distinguishable.
Batch quality lens
Record outcome per batch
Uncertainty remains visible instead of being hidden inside the finished file.
STRUCTURE ONLY · NO FIGURES
Run-specific
Run-specific
Run-specific
Approved output handoff
Deliver validated structured output into the connected catalog/site workflow
The output contract preserves the relationship between the original product record and the enriched fields. Approved data is then handed to the connected catalog/site workflow; standalone enriched-file export is not presented as a current generic capability.
Column groupWhy it is in the outputOrigin
identity.*Preserve stable product and row identifiers.SOURCEattributes.*Return normalized technical fields required by the target schema.ENRICHEDcontent.*Include approved titles and descriptions only when in scope.IF IN SCOPEvalidation.*Expose approval, review or missing-data state where implemented.QUALITYBefore / after spreadsheet example
Show the exact columns added or improved
Use a real sample with a small number of rows and sensitive data removed.
FieldBefore.xlsxChangeAfter.xlsx
manufacturer_refAC820825PRESERVEDAC820825
compatibility—ADDEDOptima / Optima HD
supplier_descriptionKverneland AC820825 Fan ImpellerIMPROVEDfact-backed content output
validation_state—ADDEDREVIEW / HOLD for unresolved conflict
File handoff route
Start with the file you already have
Send a representative CSV or Excel catalog and the target fields you want to improve. That is enough to define a practical enrichment test without overcomplicating the first step.
01Representative file
02Product identifiers
03Target fields
04Destination format
→
Practical test