- Find the product
- Open sources
- Copy values
- Normalize fields
- Write content
- Check result
Product familyBulk operationsBatch orchestratorone ruleset → many records → exceptions visible
Bulk catalog enrichment
Process Catalogs, Not One Product Prompt at a Time
The problem with manual AI workflows is not whether they can improve one product. It is what happens when the catalog contains thousands of products.
ENRIVAQ is designed around bulk product data enrichment: products enter a shared workflow, move through research, extraction and validation, and return as structured records while exceptions remain visible.
The objective is to apply consistent rules across the catalog rather than operate a separate prompt for every SKU.
Catalog batch controlBATCH REFERENCE VIEW
RUN-SPECIFICTotal records
RUN-SPECIFICProcessing
RUN-SPECIFICReview
RUN-SPECIFICReady
RecordProductBusiness stateProgress
AC820825Kverneland fan impellerREVIEW
RUN-SPECIFICCatalog recordRUN-SPECIFIC
RUN-SPECIFICCatalog recordRUN-SPECIFIC
RUN-SPECIFICCatalog recordRUN-SPECIFIC
Reference control field · counts and states are supplied by the active run
02
Scale problem
One-by-one enrichment breaks down quickly
A catalog team can manually research and edit a few products. At scale, the same workflow becomes a queue of repetitive tasks and quality becomes less consistent when different people apply different rules.
→
Bulk enrichment changes the unit of work from “one AI conversation” into “a product record moving through a defined pipeline.”
03
Batch import
Bring products into the workflow as a catalog
Bulk processing starts with structured intake using the formats supported by the implemented workflow.
RecordAvailable inputMapped to
ROW 001SKU · manufacturer · OEM · titleTARGET SCHEMA
ROW 002SKU · title · existing attributesTARGET SCHEMA
ROW 003identifier · supplier descriptionTARGET SCHEMA
…catalog records continueSAME RULESET
One agreed schema for the batch
Each record enters with what is already available. The workflow applies the agreed target structure rather than treating every product as an unrelated task.
- Required identifiers
- Defined by project
- Target attributes
- Defined by category
- Import method
- Website · API · File
04 · Catalog spine
Pipeline
Move each product through the same processing stages
A catalog-scale workflow needs visible business states. Internal orchestration details remain implementation detail.
01Import
02Identify
03Research
04Extract
05Normalize
06Validate
07Ready / Review
Business state, not infrastructure diagram · use actual status names from the implemented product.
05
Exceptions
Do not let difficult products block the whole catalog
Large catalogs contain ambiguous identifiers, poor source coverage, unusual attributes or conflicting values. Those records should become visible without stopping routine records.
Routine laneSUPPORTED EVIDENCE
Products satisfying agreed rules continue.
Review laneEXCEPTIONS
- ambiguous identifier
- source conflict
- critical field missing
- exceptional technical value
06
Progress monitoring
Know what is happening across the catalog
Catalog teams need batch-level visibility: what is processing, what is complete, what requires review, and where the exceptions are.
Catalog processing workspaceRUN-SPECIFIC STATUS VIEW
RUN-SPECIFICTotal
RUN-SPECIFICProcessing
RUN-SPECIFICReview
RUN-SPECIFICCompleted
RUN-SPECIFICFailed / held
RecordProductStatusStageAction
AC820825Kverneland fan impellerREVIEWValidationHold weight conflict
RUN-SPECIFICCatalog recordRUN-SPECIFICPipeline stageRun-specific
RUN-SPECIFICCatalog recordRUN-SPECIFICPipeline stageRun-specific
07
Export
Return the catalog in a form the next system can use
Bulk enrichment is complete only when approved results can leave the platform cleanly. Supported export methods and formats must match the implemented product.
Target product schemapreserved
Validated attributesincluded
Review-required fieldsheld / flagged
Content outputsConfigured when included in the project
08
Verified scale metrics
Show real throughput, not theoretical capacity
Scale metrics are published only from a defined sample with a consistent counting rule.
Products processed
Published totals require a defined run ledger or benchmark sample.
Catalogs / batches
Reported only with a defined counting rule.
Processing time
Do not mix machine time, implementation time and human review.
Average time per SKU
Use the same sample definition every time.
Review share
Only publish if review-state measurement exists.
Methodology must sit next to the metrics: sample size, category mix, start/end definition, exclusions and review treatment.
Batch orchestrator
Test bulk enrichment on a representative catalog segment
Start with a product group that contains enough variation to expose the real data problems: normal products, incomplete records and difficult exceptions.