Fortune 500 FMCG Case Study
Transforming messy data into a useful abstraction at scale
Comparing consumer goods items' equivalized volume (EQV) across providers requires a pipeline capable of normalizing pack sizes and units, with useful details scattered across text and images.
- Products processed
- 4.71M+
- Additional product attributes
- 9
What Phinest built
A pipeline reads product text and images, extracts price, packaging and unit information, and writes structured records to the database. It captures details such as pack count, size and product form, including bundled products.
The pipeline checks extracted values against allowed terms. Reviewers can inspect and correct the output. We handle setup; the client retains the code, config, and infrastructure.
What it enables
The pipeline processed 4.71M+ products and added nine attributes. Most support downstream equivalized volume (EQV) calculations: putting different pack sizes and units on a common basis to compare consumer goods items across providers.
The work began with a client data sample and iterative demonstrations. Early outputs were compared with existing validated values before production investment.
Bring us your hardest data problems
Use the first 14 days of a scoped engagement to assess a two-week-sized sample and prove the approach, without a six-month upfront commitment.