Cost-Optimized Engineering Team Size
Estimate engineering-team size via a free identity resolution and title roster, then a cost-ordered ladder: a limit=1 CrustData title count, CrustData company enrichment, and a final PDL company-enrich fallback.
Shared June 2026
- 1
Install & authenticate
$ npm install -g deepline@latest && npm exec --yes --package=deepline@latest -- deepline setup --json
- 2
Test out this play on my data
What this workflow does
Estimate engineering-team size via a free identity resolution and title roster, then a cost-ordered ladder: a limit=1 CrustData title count, CrustData company enrichment, and a final PDL company-enrich fallback.
Cost-Optimized Engineering Team Size (Deepline play "engineering-team-size", version 1). Estimate engineering-team size via a free identity resolution and title roster, then a cost-ordered ladder: a limit=1 CrustData title count, CrustData company enrichment, and a final PDL company-enrich fallback. Pipeline: 6 steps, 3 providers. Inputs: domain, company_headcount, method, waterfall_page_size. Outputs: domain, status, estimated_engineering_count, estimated_range, confidence, selected_method, estimated_count_relation, company_title_count, matched_engineering_title_count, matched_engineering_titles, crustdata_title_phrase_count, waterfall_exact_title_count, waterfall_count_relation, pdl_role_count, pdl_expanded_title_count, query_disagreement_ratio, company_headcount, engineering_to_company_ratio, review_reasons, evidence, provider_attempts.
Inputs you need
Bring these fields
- domain
- company_headcount
- method
- waterfall_page_size
Outputs you get
Produced by the run
- company_clean_identity_resolution
How it works
Step 1
Peopledatalabs
Peopledatalabs · company clean
Step 2
Conditional code
2 nested steps
Step 3
Conditional code
1 nested step
Step 4
Conditional code
2 nested steps
Cost/latency when available
This public page does not show cost or latency yet. Deepline only displays those metrics when the published package has safe, comparable run samples, and cost is always shown in Deepline credits.
Common edits
- Rename the copied play before the first run so it is owned by your workspace.
- Map your CSV headers into the expected input fields and add status or miss_reason columns.
- Keep stable step, fetch, dataset, and play-call ids so retries and durable receipts stay useful.
- Edit the final projection before scale so the output table matches your CRM or campaign import.
Use it when
- You need data from Peopledatalabs, Crustdata and Company in one pass.
- You want a repeatable pipeline instead of one-off lookups.
- You want an agent or teammate to run the same workflow on demand.
Related workflows
LinkedIn Employees from Company Domain
domain, max_items, profile_depth → domain, linkedin_company_url, rows
LinkedIn Profile from Personal Email
personal_email → personal_email, normalized_email, linkedin_url
LinkedIn Employees from Company Domain (HarvestAPI)
domain, max_items, profile_depth → domain, linkedin_company_url, rows
Run or fork with Deepline CLI
Cost-Optimized Engineering Team Size is a Deepline play. Inputs: domain, company_headcount, method, waterfall_page_size. Outputs: company_clean_identity_resolution.
# Inspect the contract
$ deepline plays describe prebuilt/engineering-team-size --json
# Copy to an owned scratchpad
$ deepline plays get prebuilt/engineering-team-size --source --out engineering-team-size-scratchpad.play.ts
# Check before running
$ deepline plays check engineering-team-size-scratchpad.play.ts
# Paste into Claude Code, Cursor, or Codex.
$ Use the deepline-gtm skill's plays recipe to turn the Deepline play "Cost-Optimized Engineering Team Size" (https://deepline.com/p/deepline/engineering-team-size) into an editable scratchpad play. Inputs: domain, company_headcount, method, waterfall_page_size. Outputs: company_clean_identity_resolution. The .play.ts file is the source of truth: copy the play, rename it for this user's workflow, encode input mapping/validation/status/output projection in the play, and iterate there instead of doing one-off CSV post-processing. Keep stable dataset names, dataset row keys, and step/fetch/play-call ids across edits. Provider tool calls reuse paid work by play, tool, semantic input, auth scope, provider action version, and cache policy unless you intentionally change those inputs or refresh stale data. # install Deepline if the CLI is missing npm install -g deepline@latest && npm exec --yes --package=deepline@latest -- deepline setup --json # inspect the live contract deepline plays describe prebuilt/engineering-team-size --json # copy to an owned scratchpad, then rename the play inside the file before first run deepline plays get prebuilt/engineering-team-size --source --out engineering-team-size-scratchpad.play.ts # edit engineering-team-size-scratchpad.play.ts: set an owned play name, add mappings/validation/status/miss_reason/final columns deepline plays check engineering-team-size-scratchpad.play.ts # run the scratchpad on your data deepline plays run --file engineering-team-size-scratchpad.play.ts --input '{"domain":"<value>","company_headcount":"<value>","method":"<value>","waterfall_page_size":"<value>"}' --watch # only for an exact one-off with no edits or custom output deepline plays run prebuilt/engineering-team-size --input '{"domain":"<value>","company_headcount":"<value>","method":"<value>","waterfall_page_size":"<value>"}' --watch Play spec (JSON): https://deepline.com/api/v2/shared-plays/prebuilt-engineering-team-size
Version
v1 · Shared June 2026 · Unlisted page