Targeted Prospecting

2. Find Target Companies

Run 2-3 search strategies in parallel:

Strategy A — Scrapegraph searchscraper (primary — most targeted results):

orth run scrapegraph /v1/searchscraper --body '{
  "user_prompt": "top {industry} companies in {location} with company name, website, employee count, and headquarters",
  "num_results": 15
}'

Best source for industry-specific company lists. Returns targeted results from industry directories, Inc 5000 lists, and trade publications. In testing, returned 28 staffing companies in a single call vs Fiber's noisy mix of tech giants and staffing firms.

Strategy B — Fiber NL company search (co-primary — best structured data):

orth run fiber /v1/natural-language-search/companies --body '{
  "query": "{industry} companies in {location} with {employee_min}+ employees",
  "pageSize": 20
}'

Returns structured company data with employee counts, domains, LinkedIn URLs, and descriptions. Caveat: For niche industries (staffing, construction, etc.), Fiber NL search often returns broad/noisy results mixed with unrelated companies. Filter results by industry keywords from the description, li_industries, and crunchbase_categories fields. Use company names field (not name_consensus) for the company name.

Strategy C — Nyne company search (supplemental — attempt, may return errors):

# Step 1: POST to start search
orth run nyne /company/search -d '{"query": "{industry} companies {location} {size_qualifier}"}'
# Step 2: Poll with GET using request_id
orth run nyne /company/search -q request_id=REQUEST_ID

Nyne is async — POST returns a request_id, poll with GET until complete (5-20s). Note: Nyne company search can return 400 errors depending on query format. If it fails, proceed with Scrapegraph + Fiber results — don't block on Nyne.

Scaling Up

For 20+ results, run parallel searches by sub-region or sub-vertical:

# Parallel searches for different sub-regions
orth run fiber /v1/natural-language-search/companies --body '{
  "query": "{industry} companies in New York with {size}+ employees",
  "pageSize": 15
}'

orth run fiber /v1/natural-language-search/companies --body '{
  "query": "{industry} companies in California with {size}+ employees",
  "pageSize": 15
}'

3. Extract & Deduplicate

Merge results from all strategies. For each company, extract:

• Company name
• Domain / website URL
• Employee count (primary size proxy — revenue data is often unavailable)
• Headquarters / location
• LinkedIn company URL (if returned by Fiber/Nyne)
• Description / industry tags

Deduplicate by domain first, then by normalized company name. Apply user's size filters — use employee count as revenue proxy when revenue is unavailable (100+ employees ≈ $10M+ revenue as rough heuristic).

Enrich top companies with Brand.dev for industry context:

orth run brand-dev /v1/brand/retrieve --query 'domain={company_domain}'

4. Find Decision Makers

Best approach: one broad industry-wide search, then per-company fallbacks.

In testing, a single broad query like "COO at staffing companies in the US" returned 15 relevant profiles, while per-company queries (e.g., "COO at Robert Half") often returned 0 results. Start broad, then fill gaps.

Primary — Fiber NL profile search (broad industry query):

orth run fiber /v1/natural-language-search/profiles --body '{
  "query": "{title_1} or {title_2} at a {industry} company in {location}",
  "pageSize": 15
}'

This is the highest-yield approach. Returns decision makers across the industry with LinkedIn URLs, current titles, and company names.

Per-company fallback — Fiber NL profile search (for companies not covered by the broad search):

orth run fiber /v1/natural-language-search/profiles --body '{
  "query": "{title_1} or {title_2} at {company_name}",
  "pageSize": 5
}'

Per-company queries often return empty results, especially for large enterprises where C-suite profiles may not be indexed. Use this only for high-priority companies missing from the broad search.

Supplemental — Nyne person search (async, may return errors):

orth run nyne /person/search -d '{"query": "{title} at {company_name} {location}"}'
# Poll: orth run nyne /person/search -q request_id=REQUEST_ID

Fallback — Scrapegraph website scrape (scrape the company's leadership page):

orth run scrapegraph /v1/smartscraper --body '{
  "website_url": "https://{company_domain}/about",
  "user_prompt": "Extract names, titles, and any contact info for the leadership team. Identify anyone with these titles: {target_titles}"
}'

If /about returns 422, fall back to the homepage URL.

5. Enrich Contacts

For each decision maker found, run all of these in parallel:

Email discovery — Sixtyfour first (highest hit rate for small/mid-market domains):

# Sixtyfour AI email finder (PRIMARY — found 9/12 emails in testing)
orth run sixtyfour /find-email --body '{
  "lead": {"first_name": "{first}", "last_name": "{last}", "domain": "{company_domain}"}
}'

# Hunter email-finder (supplemental — often returns null for small company domains)
orth run hunter /v2/email-finder --query 'domain={company_domain}&first_name={first}&last_name={last}'

# Tomba email-finder (supplemental — similar limitations to Hunter on small domains)
orth run tomba /v1/email-finder --query 'domain={company_domain}&company={company_name}&first_name={first}&last_name={last}'

# Tomba LinkedIn-to-email (if LinkedIn URL found in Step 4)
orth run tomba /v1/linkedin --query 'url={linkedin_url}'

In testing, Sixtyfour found emails for 9 out of 12 prospects where Hunter and Tomba returned null. Sixtyfour is the most reliable source for small/mid-market company domains. Still run all sources in parallel — each occasionally finds emails the others miss.

Phone discovery:

orth run sixtyfour /find-phone --body '{
  "lead": {"first_name": "{first}", "last_name": "{last}", "company": "{company_name}"}
}'

Sixtyfour find-phone had a 100% hit rate in testing (10/10 prospects).

Deep enrichment (fire early, don't block — takes 30-60s):

orth run sixtyfour /enrich-lead --body '{
  "lead_info": {
    "first_name": "{first}", "last_name": "{last}",
    "company": "{company_name}", "linkedin_url": "{linkedin_url}"
  },
  "struct": {
    "work_email": "Work email",
    "personal_email": "Personal email",
    "phone": "Phone number",
    "title": "Current job title",
    "bio": "Short professional bio"
  }
}'

Fiber kitchen-sink enrichment (if LinkedIn URL available — may return 400):

orth run fiber /v1/kitchen-sink/person --body '{
  "profileIdentifier": "{linkedin_url}"
}'

Kitchen-sink can intermittently return 400 errors regardless of parameter format. If it fails, proceed with Sixtyfour + Hunter + Tomba results — don't block on kitchen-sink.

Triple email verification — verify ALL found emails with 3 services:

orth run hunter /v2/email-verifier --query 'email={email}'
orth run tomba /v1/email-verifier --query 'email={email}'
orth run fiber /v1/validate-email/single --body '{"email": "{email}"}'

Take the consensus. Label each email as verified/unverified. Collect both work and personal emails.

6. Hiring / Intent Signals

Only run this step if the user specified hiring signal roles. This is the key differentiator for prioritization.

Primary — Scrapegraph searchscraper for hiring signals:

orth run scrapegraph /v1/searchscraper --body '{
  "user_prompt": "{industry} companies hiring {signal_role} in {location}, list company name, job title, and location",
  "num_results": 15
}'

Supplemental — Tavily for job board coverage:

orth run tavily /search --body '{
  "query": "{industry} {signal_role} job opening {location}",
  "max_results": 10,
  "include_answer": false
}'

Then scrape top job board results for company names:

orth run scrapegraph /v1/smartscraper --body '{
  "website_url": "{job_board_url}",
  "user_prompt": "Extract all company names hiring for {signal_role}, with job title and location"
}'

Optional — Fiber job search (attempt, may be unreliable):

orth run fiber /v1/job-search --body '{
  "searchParams": {
    "job_titles": ["{signal_role}"],
    "industries": ["{industry}"]
  },
  "pageSize": 20
}'

Note: Fiber job-search with searchParams filters can return 400 errors. Attempt it but don't rely on it — Scrapegraph is the primary method for hiring signals.

Cross-reference: Match companies found hiring signal roles against the company list from Step 2. Matches become High Priority prospects. Companies hiring for signal roles that weren't in your original list are bonus leads — add them.

Growth signals: Check Fiber company data (from Step 4 kitchen-sink results) for headcount growth percentage. Companies growing >20% YoY are additional high-priority signals.

7. Competitive Intel (Optional)

Only run if the user mentioned their product/company. Research what the user sells and check prospects for competing solutions.

# Research user's product
orth run scrapegraph /v1/smartscraper --body '{
  "website_url": "https://{user_company_domain}",
  "user_prompt": "What does this company sell? Describe the product in one sentence."
}'

# Find competitors
orth run scrapegraph /v1/searchscraper --body '{
  "user_prompt": "competitors and alternatives to {user_product} for {industry}",
  "num_results": 5
}'

# Check each prospect's website for competing products (parallel)
orth run scrapegraph /v1/smartscraper --body '{
  "website_url": "https://{prospect_domain}",
  "user_prompt": "Does this company use or mention: {competitor_1}, {competitor_2}, {competitor_3}? Check page content, footer, and embedded widgets."
}'

Flag prospects: Greenfield (no competitor detected) > Competitive displacement (uses a competitor — note which one) > Unknown.

8. Present Results

Output a prioritized table with full URLs (not markdown links — users need to copy-paste):

## Prospect List: {Title} at {Industry} Companies in {Location}

Found {N} companies with {M} decision makers identified.

### High Priority — Hiring Signal Detected
| # | Company | Website | Employees | Decision Maker | Title | Email | Email Status | Phone | Signal |
|---|---------|---------|-----------|---------------|-------|-------|-------------|-------|--------|
| 1 | Acme Staffing | https://acmestaffing.com | 250 | Jane Smith | COO | [email protected] | Verified | (555) 123-4567 | Hiring Scheduling Coordinator |

### Medium Priority — Matches ICP, No Signal Detected
| # | Company | Website | Employees | Decision Maker | Title | Email | Email Status | Phone | Notes |
|---|---------|---------|-----------|---------------|-------|-------|-------------|-------|-------|
| 5 | Beta Corp | https://betacorp.com | 180 | John Doe | VP Ops | [email protected] | Verified | — | Growing 25% YoY |

### Lower Priority — Limited Data or Below Target Size
| # | Company | Website | Employees | Decision Maker | Title | Email | Phone | Notes |
|---|---------|---------|-----------|---------------|-------|-------|-------|-------|
| 10 | Small Co | https://smallco.com | 85 | — | — | — | — | Below 100 employee threshold |

### Summary
- **Companies found**: {N}
- **Decision makers identified**: {count}/{N}
- **With verified email**: {count}
- **With phone**: {count}
- **High priority (hiring signal)**: {count}
- **Medium priority (right profile)**: {count}
- **Lower priority (limited data)**: {count}

APIs Used

Examples

Example 1 — Staffing/recruiting (the Clay use case):

"Find COOs at US staffing firms with 100+ employees that are hiring Scheduling Coordinators"

# Step 2: Find staffing companies (parallel)
orth run fiber /v1/natural-language-search/companies --body '{
  "query": "staffing and recruiting companies in the United States with 100 or more employees",
  "pageSize": 20
}'

orth run nyne /company/search -d '{"query": "staffing recruiting firms US 100+ employees"}'

orth run scrapegraph /v1/searchscraper --body '{
  "user_prompt": "top staffing and recruiting companies in the US with company name, website, employee count, and headquarters",
  "num_results": 15
}'

# Step 4: Find COOs (parallel, per company)
orth run fiber /v1/natural-language-search/profiles --body '{
  "query": "COO or Chief Operating Officer or Head of Operations at {company_name}",
  "pageSize": 3
}'

# Step 5: Enrich (parallel, per person)
orth run hunter /v2/email-finder --query 'domain={domain}&first_name={first}&last_name={last}'
orth run sixtyfour /find-email --body '{"lead": {"first_name": "{first}", "last_name": "{last}", "domain": "{domain}"}}'
orth run sixtyfour /find-phone --body '{"lead": {"first_name": "{first}", "last_name": "{last}", "company": "{company}"}}'

# Step 6: Hiring signals
orth run scrapegraph /v1/searchscraper --body '{
  "user_prompt": "staffing companies hiring Scheduling Coordinator or Recruiting Coordinator in the US, list company name, job title, and location",
  "num_results": 15
}'

Example 2 — SaaS sales (fintech):

"Find VP Engineering or CTO at fintech startups with 50-200 employees in the US that are hiring DevOps engineers"

# Companies
orth run fiber /v1/natural-language-search/companies --body '{
  "query": "fintech startups in the United States with 50 to 200 employees",
  "pageSize": 20
}'

# Decision makers (per company)
orth run fiber /v1/natural-language-search/profiles --body '{
  "query": "VP Engineering or CTO at {company_name}",
  "pageSize": 3
}'

# Hiring signal
orth run scrapegraph /v1/searchscraper --body '{
  "user_prompt": "fintech companies hiring DevOps Engineer or Site Reliability Engineer in the US, list company name and job title",
  "num_results": 15
}'

Example 3 — Recruiting (healthcare in Texas):

"Find HR Directors at healthcare companies in Texas with 500+ employees"

# Companies
orth run fiber /v1/natural-language-search/companies --body '{
  "query": "healthcare companies in Texas with 500 or more employees",
  "pageSize": 20
}'

# Decision makers
orth run fiber /v1/natural-language-search/profiles --body '{
  "query": "HR Director or VP Human Resources at {company_name}",
  "pageSize": 3
}'

Example 4 — Simple, no hiring signals (construction):

"Build a prospect list of construction companies in California with Head of Safety as decision maker"

# Companies
orth run fiber /v1/natural-language-search/companies --body '{
  "query": "construction companies in California",
  "pageSize": 15
}'

# Decision makers
orth run fiber /v1/natural-language-search/profiles --body '{
  "query": "Head of Safety or Safety Director or VP Safety at {company_name}",
  "pageSize": 3
}'

Error Handling

• Fiber NL company search returns noisy results — For niche industries, Fiber often returns unrelated companies mixed in (e.g., tech giants alongside staffing firms). Filter results by li_industries, crunchbase_categories, or keywords in short_description. If too noisy, use Scrapegraph searchscraper as primary source instead
• Fiber NL profile search returns empty per-company — Per-company queries often return 0 results, especially for large enterprises. Use a broad industry-wide query instead (e.g., "COO at a staffing company in the US") which yields 10-15x more results
• Fiber kitchen-sink returns 400 — Can fail intermittently regardless of parameter format (profileIdentifier, slug, or full URL all tested). This appears to be an API reliability issue, not a format issue. Proceed with Sixtyfour + Hunter + Tomba for enrichment
• Nyne returns 400 — Nyne company and person search can return 400 errors. Query format sensitivity is unclear. Don't block on Nyne — proceed with Scrapegraph + Fiber results
• Fiber job-search returns 400 — Known issue with searchParams filters. Use Scrapegraph searchscraper for hiring signals instead
• Smartscraper 422 on /about path — Fall back to scraping the homepage URL (no path appended)
• Hunter/Tomba return null for email — Expected for small/mid-market company domains. In testing, Hunter and Tomba returned null for most staffing firms while Sixtyfour found 9/12. Always run Sixtyfour as primary email source
• No hiring signal found — Not every industry/location has active job postings for specific roles. Mark as "No signal detected" — these are still valid medium-priority prospects

Tips

• Scrapegraph is the best company finder for niche industries — In testing, Scrapegraph returned 28 targeted staffing companies vs Fiber's noisy mix. Use Scrapegraph as primary for industry-specific lists, Fiber as co-primary for structured data (employee counts, domains)
• Broad profile search beats per-company search — One query for "COO at staffing companies in the US" returned 15 profiles. The same search run per-company (8 companies) returned only 2 profiles total. Always start with a broad industry-wide NL profile search
• Sixtyfour is the #1 email finder — Found 9/12 emails in testing where Hunter and Tomba returned null. For small/mid-market company domains, Sixtyfour's AI approach dramatically outperforms pattern-based tools. Still run all sources in parallel for maximum coverage
• Sixtyfour find-phone is highly reliable — 100% hit rate in testing (10/10 prospects). Always include phone discovery
• Hiring signals are the #1 prioritization tool — A company actively hiring for a role your product replaces/supports is 3-5x more likely to buy. Scrapegraph searchscraper is the best source — found Randstad and Robert Half hiring for Scheduling Coordinators in a single call
• Employee count is the best size proxy — Revenue data is rarely available from APIs. Use employee count: 50+ ≈ established, 100+ ≈ mid-market, 500+ ≈ enterprise
• Fiber kitchen-sink may be unreliable — Can return 400 errors intermittently. Don't depend on it as the sole enrichment source — always have Sixtyfour running in parallel as fallback
• LinkedIn URLs dramatically improve enrichment — When Fiber NL profile search returns LinkedIn URLs, feed them into Tomba-LinkedIn for email and Sixtyfour enrich-lead for deep context
• Deduplicate aggressively — Multiple search strategies will return overlapping results. Dedup by domain first (most reliable), then by normalized company name
• Hunter email-verifier is fast and reliable — Even when Hunter email-finder returns null, Hunter email-verifier is excellent for verifying emails found by Sixtyfour. Every email verified came back with score 89-100
• Include title variations — Search for "COO OR Chief Operating Officer OR Head of Operations" to catch different title formats at the same level
• Filter Fiber company results by industry — Use li_industries, crunchbase_categories, or keywords in short_description to filter out irrelevant companies from Fiber NL results