Make messy product data usable at scale.
Flexible extraction pipelines for large product datasets, starting with a small, measurable proof for your use case.
Experience processing product data from
Source experience, not partnerships or endorsements
From a brittle black box to a pipeline the client controls.
The previous deterministic solution was expensive, unreliable, and difficult to modify. Phinest replaced it with a flexible extraction pipeline that made changes faster and reduced the cost of working with the system.
See the evidence and architectureStarted with a small client data sample, iterative demos, and comparison against existing validated values.
Expanded across 4.71M+ products and unlocked nine additional reliable attributes: most for EQV calculations, others for product understanding and potential matching.
Delivered client-owned code and configuration, plus an interface for reviewing and correcting extracted values.
Prove it before you fund production.
A short first engagement tests the solution on real client data before time and money go into architecture and scale.
Prove the hard part
Start with a representative sample, the attributes that matter, and an agreed evaluation method. Build enough to see what works and where it fails.
Build after the evidence
Invest in architecture, scale, integrations, review tooling, and operations only after the core extraction approach has proven itself.
The proof answers four questions.
Can the required attributes be recovered from the available data?
How well does the approach hold up against manually checked data?
Which source gaps and edge cases will remain?
Is the result strong enough to justify production investment?
More than a model call.
The result is a working product-data pipeline with extraction, validation, review, and integration designed together.
Flexible extraction
Extract product attributes from descriptions and images across inconsistent third-party data sources.
Validation and review
Check outputs against controlled vocabularies, detect bundles, and let reviewers correct values with an explanation.
Production integration
Run the pipeline in the enterprise data stack with logged runs, inspectable configuration, and ongoing maintenance.
Own the pipeline. See how it works. Change it quickly.
The Fortune 500 deployment replaced a provider-controlled black box with a system the client can inspect and configure.
Client-owned
The client holds the pipeline code and configuration rather than receiving results from an opaque provider.
Reviewable
A purpose-built interface lets users filter results, update extracted values, and record explanations.
Operable
Runs are logged with completion metadata, while Phinest can continue maintaining the working system.
Start with a small sample, not a large commitment.
Prove the extraction approach on real data before investing in production architecture, integrations, and scale.