Selected work

Case studies with real adoption pressure.

Two recent examples: one product that prepares enterprise data for AI agents, and one Fortune 500 UK FMCG engagement that used LLMs to turn unstructured product data into clean, structured fields.

B2B Product

Agent-Ready Databases

A productized metadata workflow that turns schemas, sample data, SQL templates, and steward feedback into rich AI-ready documentation for downstream agents.

3-5x

faster dominant workflow stages

Column, table, and schema semantics
Verified primary keys and relationships
Human-in-the-loop refinement that sticks

Fortune 500 UK FMCG

LLM Structured Data Extraction

An LLM-powered pipeline that turns unstructured product text and images into clean, validated structured fields across millions of products.

99%

data completeness

Vision and text attribute extraction
Validation against controlled vocabularies
Human-in-the-loop review for edge cases

Need AI that fits the work?

Bring the messy workflow. Phinest can shape the prototype, production path, and adoption plan around the people who need to use it.

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