Search and save free company data

Query a bounded free-company result and preserve its source identity before further enrichment.

Use the free company search contract for columns, limits, and snapshot semantics. These procedures query the shared company snapshot. they do not perform live web discovery.

Start from the CLI

Inspect the live tool schema first:

deepline describe free_simple_company_search --json

Then run one bounded query. This exact-domain lookup is the safest starting point for a CRM or CSV export that already contains normalized domains:

PAYLOAD=$(cat <<'JSON'
{
  "sql": "SELECT normalized_domain, domain, company_name, industry, location, employee_count, year_founded FROM companies WHERE normalized_domain = 'openai.com' LIMIT 5"
}
JSON
)

deepline tools execute free_simple_company_search --input "$PAYLOAD"
deepline tools execute free_simple_company_search --input "$PAYLOAD" --out companies.csv

The direct command returns matching rows. --out writes row-shaped results to CSV. It does not modify the Database or the shared snapshot.

Practical SQL patterns

Resolve and normalize a target list

Normalize website values to lowercase root domains before putting them in the IN list. The output gives you the snapshot’s company name and LinkedIn URL for review. It does not automatically overwrite CRM values.

SELECT normalized_domain, company_name, linkedin_url
FROM companies
WHERE normalized_domain IN ('openai.com', 'anthropic.com', 'vercel.com')
LIMIT 10

For a small list that already has LinkedIn company URLs, use exact equality or IN as well:

SELECT company_name, normalized_domain, linkedin_url
FROM companies
WHERE linkedin_url IN ('linkedin.com/company/openai')
LIMIT 5

Find candidates after an exact miss

Use a prefix only after an exact domain, LinkedIn URL, or company-name lookup misses. Inspect the returned domain before using a candidate downstream:

SELECT company_name, normalized_domain, location, employee_count
FROM companies
WHERE company_name ILIKE 'acme%'
LIMIT 25

Build a bounded pilot segment

First count a narrowly defined segment, then request a small page. Employee count is the upper bound of a source size bucket, not a live headcount.

SELECT COUNT(*) AS company_count
FROM companies
WHERE industry ILIKE '%software%'
  AND employee_count >= 50
LIMIT 1

SELECT normalized_domain, company_name, industry, location, employee_count, year_founded
FROM companies
WHERE location ILIKE 'san francisco%'
  AND year_founded >= 2020
ORDER BY employee_count DESC
LIMIT 100

Check coverage and source duplicates

Compare matched_domains with the number of unique normalized domains in your input file. Unmatched domains do not appear in the result, so this tool cannot label them as missing for you.

SELECT
  COUNT(*) AS source_records,
  COUNT(DISTINCT normalized_domain) AS matched_domains
FROM companies
WHERE normalized_domain IN ('openai.com', 'anthropic.com', 'vercel.com')
LIMIT 1

This query checks duplicate source records for the requested domains. It does not detect duplicate rows in your CRM or CSV.

SELECT normalized_domain, COUNT(*) AS source_record_count
FROM companies
WHERE normalized_domain IN ('openai.com', 'anthropic.com', 'vercel.com')
GROUP BY normalized_domain
HAVING COUNT(*) > 1
LIMIT 20

Query from the API

Use the normal tool execution endpoint:

curl -X POST "https://code.deepline.com/api/v2/integrations/free_simple_company_search/execute" \
  -H "Authorization: Bearer $DEEPLINE_API_KEY" \
  -H "Content-Type: application/json" \
  --data-binary @- <<'JSON'
{
    "payload": {
      "sql": "SELECT normalized_domain, company_name FROM companies WHERE normalized_domain = 'openai.com' LIMIT 5"
    }
}
JSON

The response wraps the SQL result in data:

{
  "data": {
    "schema": "public.companies",
    "command": "SELECT",
    "row_count_returned": 1,
    "truncated": false,
    "columns": [
      { "name": "company_name", "table_id": null, "data_type_id": null }
    ],
    "sql": "SELECT company_name FROM companies WHERE normalized_domain = 'openai.com' LIMIT 5",
    "executed_sql": "SELECT company_name FROM COMPANIES WHERE normalized_domain = 'openai.com' LIMIT 5",
    "limit": 5,
    "rows": [{ "company_name": "OpenAI" }]
  }
}

Use inside a Play

Inside a Play, call the tool through ctx.tools.execute. Row-shaped extracted list getters return a Dataset Handle. Pass the rows through the Play output or into another ctx.dataset stage.

import { definePlay } from 'deepline';

export default definePlay('free-company-search', async (ctx) => {
  const result = await ctx.tools.execute({
    id: 'free_company_search',
    tool: 'free_simple_company_search',
    input: {
      sql: "SELECT normalized_domain, company_name, industry, location FROM companies WHERE location ILIKE 'san francisco%' LIMIT 100",
    },
    description: 'Find matching companies from the free company corpus.',
    staleAfterSeconds: 0,
  });

  const companies = result.extractedLists.rows.get();

  return {
    companies,
    count: await companies.count(),
  };
});

After the Play run completes, use the emitted run ID to export the returned dataset:

RUN_ID_FILE="./free-company-search-run.json"
deepline plays run --file ./free-company-search.play.ts --watch --run-id-file "$RUN_ID_FILE"
RUN_ID="$(jq -er '.runId' "$RUN_ID_FILE")"
deepline runs export "$RUN_ID" --dataset result.companies --out companies.csv

Save matching companies to your workspace database

To save query results in your Database, use materialize_free_customer_companies. It runs the SQL query and upserts rows into enrichments.companies. The query must return only these company columns:

domain, company_name, industry, location, linkedin_url, employee_count, year_founded

Example:

PAYLOAD=$(cat <<'JSON'
{
  "sql": "SELECT normalized_domain AS domain, company_name, industry, location, linkedin_url, employee_count, year_founded FROM companies WHERE industry ILIKE '%software%' AND employee_count >= 50 LIMIT 1000"
}
JSON
)

deepline tools execute materialize_free_customer_companies --input "$PAYLOAD"

Then query the saved rows with query_customer_db:

deepline tools execute query_customer_db --input '{
  "sql": "SELECT domain, company_name, industry, employee_count FROM enrichments.companies ORDER BY updated_at DESC LIMIT 50"
}'

TAM builder guidance

For TAM work, use this source when the ICP fits its documented fields:

Exact domains or company names
LinkedIn company URLs
Industry or location
Employee count or founding year When the required signal is absent, inspect another supported source. Apollo requires an explicitly selected customer-credential route. Do not assume managed Apollo access.

Verify this task

Check selected columns and returned row scope. Keep direct source reads distinct from writes to your workspace customer tables. Ask your agent to explain a failed check before repeating work or expanding the scope.

​Start from the CLI

​Practical SQL patterns

​Resolve and normalize a target list

​Find candidates after an exact miss

​Build a bounded pilot segment

​Check coverage and source duplicates

​Query from the API

​Use inside a Play

​Save matching companies to your workspace database

​TAM builder guidance

​Verify this task

Start from the CLI

Practical SQL patterns

Resolve and normalize a target list

Find candidates after an exact miss

Build a bounded pilot segment

Check coverage and source duplicates

Query from the API

Use inside a Play

Save matching companies to your workspace database

TAM builder guidance

Verify this task