Product Data Enrichment Architecture
ENRIVAQ is designed as an enrichment layer between incomplete source data and the systems that ultimately manage or publish the product record. The architecture separates ingestion, research, AI processing, validation and export so each stage has a clear role in the workflow.
This is a high-level public architecture. It explains data movement and processing boundaries without exposing infrastructure secrets, credentials, internal hostnames or private network details.
The main processing boundaries, not the deployment
The public diagram shows where data enters, which stages act on it and where a validated record leaves. Internal deployment topology stays out of the published version.
Where existing records enter the workflow
The platform accepts only input methods that are actually supported in production. Each record is mapped into a predictable structure before enrichment begins.
Exact formats, field limits and endpoint behavior belong in Import & Export and API documentation, which remain authoritative.
Context in
REQUIREDCONTEXTCONTEXTPRODUCTION ONLY+ MAP
Predictable structure
REJECT / FLAGREADYBOUNDORCHESTRATEOne record, several coordinated stages
A record may need identification, research, extraction, normalization, validation, content generation and review. These stages are not presented as one opaque AI call.
PRODUCT CONTEXTIdentifiers + existing attributes + category
This context is used to search for documentation that plausibly describes the same product.
Research collects evidence for the next stage
Research output is not treated as final truth. It becomes evidence for extraction and validation.
The public architecture describes the role of research without exposing proprietary source-selection or ranking logic.
Model-assisted tasks remain inside defined boundaries
AI may assist identification, extraction, classification, normalization and content generation where those capabilities are actually implemented. Deterministic checks, schema mapping and export remain outside the model.
CONTEXT → CANDIDATE MATCHSOURCE → FIELD VALUESRECORD → TAXONOMYRAW → NORMALIZEDVALIDATED DATA → CONTENTNO MODEL INVOLVEDThe control layer before a record becomes final
Validation identifies missing required fields, structural problems, source conflicts, uncertainty and records that require human review.
A record that fails validation does not silently continue. It is held, flagged or routed to review, and unresolved fields stay empty.
State and evidence stay inspectable between stages
The working architecture may store job state, product records, source context, stage outputs and operational logs. Retention, encryption and region claims are not published here until verified.
See Data Privacy / DPA for the applicable retention terms.See Data Privacy / DPA for the applicable retention terms.See Data Privacy / DPA for the applicable retention terms.See Data Privacy / DPA for the applicable retention terms.See Data Privacy / DPA for the applicable retention terms.Validated records move back to the system that owns the workflow
Public architecture language stays simple: existing system → ENRIVAQ enrichment → validated record → downstream system.
CONFIGURED HANDOFFCONFIGURED HANDOFFENABLED DESTINATIONWHEN CONFIGUREDDEPLOYMENT CONTRACTRECEIVEDInput accepted by the configured workflow.PROCESSINGRequired enrichment stages are running.EXCEPTIONA processing or evidence issue requires handling.REVIEW / HOLDAmbiguous or conflicting evidence is kept out of accepted output.RESULT READYAccepted output is available for the configured handoff.FAILEDThe configured processing path did not complete.Catalog work is a job, not a prompt
Catalog enrichment is a workflow across many products, not a sequence of individual chat prompts. Where jobs or queues are used, processing is asynchronous and job state can be tracked.
Exact queue names, retry counts, concurrency and external state names are implementation details; any public API state contract is configured.
Where data enters, where it is processed, where it leaves
Boundaries are shown conceptually. Detailed controls belong on the Security page and describe only measures that are actually implemented.
CUSTOMER → PLATFORMPLATFORM → MODELPLATFORM INTERNALPLATFORM → DOWNSTREAMScale comes from controlled work distribution, not one oversized request
The public architecture can explain batch-first workflow, stage isolation, retry safety and controlled concurrency. Real throughput figures are published only when verified.
No public production figure without a run ledgerNo public production figure without a run ledgerNo public production figure without a run ledgerFailures are visible to the team running the platform
Monitoring makes processing failures, job health and exceptions visible to the operating team. Operational tooling and alerting details remain internal.
Alert thresholds, on-call rotation and internal dashboards are not published here.
INTERNAL OPERATIONAL DETAILINTERNAL OPERATIONAL DETAILINTERNAL OPERATIONAL DETAILINTERNAL OPERATIONAL DETAIL