Data and catalog
Ingestion and standardization of sell-out data (retailer sales to end consumers), with SKU matching through rules, embeddings, language models and review.
Sales files from different sources can use incompatible columns, descriptions and codes, which makes it hard to get a consistent view of sell-out.
A data pipeline with intake via API or file upload, per-client isolation and normalization into a common format. Catalog matching combines rules and AI, with a review queue for the cases that need analysis.
The pipeline handles data intake and its transformation into a common format. Files and API calls can arrive with different fields and conventions; normalization organizes that information before the next steps. The documented architecture covers per-client separation, processing batches and storage in S3 and Parquet.
Another challenge is recognizing when different codes stand for the same product. SKU matching combines rules, embeddings and language models, with review of the cases that need analysis. The intended outcome of a deployment is a dataset ready for analytics, with known matching criteria and open questions visible to the team that owns the catalog.
FROM PROJECT TO OPERATIONS
Illustrative scenarios to picture how this work can be applied in a company’s day-to-day operations.
GROUP / Data and catalog
Choose the starting point closest to your challenge. Each offer proposes a scope of work based on the experience of this project.
Who it’s for Sales teams that consolidate files sent by retail chains and partners.
Define and implement intake for a set of sources, with a data contract, column mapping and organization of the incoming batches.
Who it’s for Catalog managers and analysts who reconcile product descriptions.
Build the link between external codes and the master catalog, combining deterministic criteria, similarity and review of the cases without a clear match.
Who it’s for Data teams that need to make sell-out data available for analysis.
Design storage and access for the normalized data on a scoped AWS architecture, taking into account the sources, expected usage and environment costs.
We can combine offers and adapt the deliverables. Data, integrations, acceptance criteria, timeline and investment are defined in the proposal for your company.
Let’s design your scope