01 · Challenge
Raw company lists often lack consistent context, making outreach and analysis slow and difficult to review.
Back to work
Enrichment · Structured research · Lead intelligence
Clay AI Company Research
A two-part Clay AI assignment that researches companies, enriches structured fields, and produces a reviewable dataset for outreach and business analysis.
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Case study brief
The problem, then the product response.
02 · Response
I completed a two-part Clay AI research assignment that enriches company records, normalizes useful fields, and preserves a structured dataset for human review.
System anatomy
How the system coordinates work.
The architecture is expressed as responsibilities and boundaries so the model, workflow logic, interfaces, and human controls remain inspectable.
01
Source records define the company-research targets and required fields
02
Enrichment steps gather and normalize company context
03
The final table keeps evidence reviewable for outreach or analysis
Implementation
What I built and the decisions behind it.
01
Defined the target companies and required research fields.
02
Collected and normalized company descriptions and context.
03
Produced a CSV that supports filtering and downstream outreach analysis.
04
Reviewed the enriched result for missing or inconsistent values.
Evidence & validation
What an evaluator can inspect.
Structured company-research CSV.
Enrichment and table screenshot evidence.
Assignment files and professional project post.
Reflection
Enrichment only becomes useful when the target schema is clear and a reviewer can trace which fields are incomplete or uncertain.
Next iteration
- →Attach source URLs and confidence fields to every record.
- →Add duplicate detection and quality rules.
- →Connect the final table to a controlled outreach workflow.
Outcomes
What the project delivered.
Two-part assignment combined into one evidence package
Structured CSV output with enriched company records
Visual project publication and report screenshot