> ## Documentation Index
> Fetch the complete documentation index at: https://deepline.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Lead Enrichment Pipeline

> Run a low-latency lead enrichment pipeline using Apify, Crustdata, and Apollo before outreach. Use it for a first run, result review, and safe scaling.

[Human guide: work with your agent →](https://deepline.com/docs/human/recipes/lead-enrichment.md)

> [!NOTE]
>
> Build a small lead-enrichment pilot with separate company, person, and
> validation outcomes.

> [!NOTE]
>
> Use this recipe when you already have raw leads and want fast, structured
> company + person context.

## Workflow

1. **Seed leads** from a CSV.
2. **Normalize company data** first.
3. **Enrich person/role fields** with a second pass.
4. **Add quality gates** (domain/email validation) before downstream automation.

> [!TIP]
>
> Keep each block single-purpose so failures are obvious and reruns are cheap.

## Claude Code Prompt

```text theme={"languages":{"custom":["/languages/deepline-prompt.json"]}}
I have leads.csv with person_first_name, person_last_name, and company_name.
Run a lead enrichment pipeline with this order:
1) company normalization
2) people enrichment
3) email validation

Start with rows 0-20 only and write output to leads-enriched.csv.
Before running, show the exact action plan and expected output columns.
After running, summarize:
- success/failure count by action
- 3 representative enriched rows
- any schema or quality risks before scaling
```

## Why This Layout Is Reliable

* Keeps each step **single-purpose**.
* Allows quick pilot runs before running the full file.
* Makes failures easy to diagnose because each block maps to a clear provider.

***

View source on [GitHub](https://github.com/getaero-io/gtm-eng-skills) | [I Have X, I Want Y →](https://deepline.com/docs/plays/decision-tree.md)

## Verify this task

Reconcile input IDs and each stage's output. Keep provider errors separate from missing or rejected fields. Inspect actual scoped usage.

Ask your agent to explain a failed check before repeating work or expanding the scope.


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