disco-like

Disco-Like

Lookalike company discovery. Give it 3-10 seed domains you know are a good fit; it returns hundreds of similar companies by domain, industry, and business characteristics. Useful for expanding from a small known-good list to a much bigger TAM without manual research.

When to use

You have 3-10 customer domains you love, want "more like these"
You want to expand a small client list into a full TAM
You have an ICP description but don't want to manually build Prospeo filters
Competitive / adjacent-market expansion

When NOT to use

You need PEOPLE, not companies (use Prospeo or Blitz after this)
Your ICP is extremely narrow or nascent (<5 seed examples exist)
Budget is tight — DiscoLike charges per call + per record; see cost section

Two search modes

Mode A — Seed domains (most common)

npx tsx scripts/discover.ts --domains "clay.com,apollo.io,outreach.io" --country US --limit 500 --out lookalikes.csv

DiscoLike finds companies with similar characteristics (industry mix, employee count range, business type, tech stack) to your seeds.

Mode B — Natural-language ICP

npx tsx scripts/discover.ts --text "B2B SaaS companies selling outbound sales software to RevOps teams" --country US --out lookalikes.csv

Uses DiscoLike's text matching. Less precise than seeds, but useful when you don't have named comparables.

Hybrid mode

npx tsx scripts/discover.ts --domains "clay.com" --text "outbound automation" --country US --out lookalikes.csv

Combines both — starts from seeds, expands via text semantics.

Negation (exclude existing customers / competitors)

npx tsx scripts/discover.ts \
  --domains "clay.com,apollo.io" \
  --negation-domains "yourcompany.com,yourbigcustomer.com" \
  --country US \
  --out lookalikes.csv

Always include your own domain + existing customers + known-unfit competitors. Saves enrichment cost downstream.

Inputs

DISCOLIKE_API_KEY (env) — from DiscoLike dashboard
Either --domains or --text (at least one required)
Optional: --negation-domains, --country, --limit, --max-companies

Outputs

CSV with columns: domain, company_name, industry, headcount_range, headcount, location_country, location_state, location_city, linkedin_url, description, source

All rows have source=discolike so you can mix with other list-builder outputs without collisions.

Cost

$0.10 per API call + $2.00 per 1,000 records returned
Default page size: 100 per call
A 500-company discovery = ~5 calls + 500 records ≈ $1.50
A 10,000-company discovery ≈ $10 + $20 = $30

Compare to Prospeo, which charges per export. DiscoLike is typically cheaper per company-discovered but more expensive per enriched contact (DiscoLike gives companies, not people).

Required step: Qualify with /icp-prompt-builder

This is a required step. Do not skip it.

Before pulling 5,000 companies, run DiscoLike on a small sample (50-100), then invoke /icp-prompt-builder:

Evaluate which of the 50 are actually good ICP fits
Refine your ICP description / negation list based on what DiscoLike returned
Only then scale to 5,000+

Why required: DiscoLike lookalike results are only as good as your seed domains. If 80% of the first 50 are wrong, you need to change seeds, not pay to pull more. At $0.10/call + $2/1K records, a wrong-seeded 10K pull costs $20-$30 in DiscoLike fees AND cascades into wasted email-finder fees downstream. Qualifying the first 50 catches bad seeds before they become expensive.

Recommended flow

/icp-onboarding → nail down seed companies (your best 5 customers)
/disco-like --domains="seed1,seed2,..." --limit=100 --out=sample.csv → sample run
/icp-prompt-builder → score the sample, tune ICP prompt
If sample quality is high, scale: /disco-like ... --limit=5000 --out=full.csv
/blitz-list-builder --domains-file=full.csv → find decision-makers at each
/list-builder (Phase 5, emails) → fill in emails
Upload to Smartlead

API details (reference)

Base URL: https://api.discolike.com/v1

Auth: x-discolike-key header

Endpoints:

Method	Path	Purpose
GET	`/count?domains=X&text=Y`	Total matching companies (before paying to pull)
GET	`/discover?domains=X&text=Y&country=Z&limit=100&offset=0`	Paginated lookalike results
GET	`/bizdata?domain=X`	Detailed data for a single domain

Data returned per company:

domain, name, description
industry_groups (weighted dict — script takes top industry)
employees (range string like "51-200")
address (country, state, city)
social_urls (script extracts LinkedIn company URL)

Rate limit: Conservative — script throttles at 5 concurrent, 10 req/sec. No 429s observed on normal runs.

Common gotchas

Seed domains must be clean bare domains. clay.com works, https://clay.com/ doesn't.
Text mode is fuzzier than you think. "Outbound sales" returns SaaS, agencies, consultancies — broad. Tighten with seeds.
No people data. DiscoLike is company-level. Always chain with Blitz or Prospeo for contacts.
Non-US coverage varies. US has deepest data. EU/APAC coverage is thinner; count may be misleading.
Check the count FIRST. Before paying for 10,000 records, run /count to confirm the universe actually has 10,000. Many narrow ICPs top out at 500-2000.

Scripts

scripts/discover.ts — main search + CSV output

What to do next

Run /icp-prompt-builder on your 50-company sample (required step above). Then either:

/blitz-list-builder to find owner contacts at each filtered domain, OR
/list-quality-scorecard directly if this is companies-only and you'll enrich another way

Or wait: if the 50-sample ICP fit was poor (<40% matches), don't scale. Change your seed domains and re-run with better inputs.

Related skills

/icp-onboarding — defines the seed domains you'll use
/icp-prompt-builder — quality-check the first 50 results before scaling
/blitz-list-builder — chain to find contacts at each discovered company
/list-builder (Phase 5, emails) — fill missing emails after Blitz
/cold-email-starter-kit → 06-list-building-prospeo.md for broader list-building patterns