/targeted-prospecting

name: targeted-prospecting
description: Build a prospect list of companies with decision makers, verified contact info, and hiring/intent signals. Use when asked to find leads by industry, build an account list with specific titles, prospect companies that are actively hiring, or create a targeted outreach list filtered by company size, location, and hiring activity.
source: orthogonal

Targeted Prospecting — Industry + Decision Makers + Hiring Signals

Setup

Read your credentials from ~/.gooseworks/credentials.json:

export GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])")
export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))")

If ~/.gooseworks/credentials.json does not exist, tell the user to run: `npx gooseworks login`

All endpoints use Bearer auth: `-H "Authorization: Bearer $GOOSEWORKS_API_KEY"`

Build a prioritized prospect list for any industry. Finds companies matching your ICP, identifies decision makers by title, enriches with verified contact info, and layers on hiring/intent signals to prioritize who's ready to buy now.

Workflow

1. Parse the Request

Extract from the user's query:

• **Industry/vertical** (required) — e.g., staffing, fintech, healthcare IT, construction
• **Decision maker titles** (required) — e.g., COO, VP Engineering, Head of Marketing
• **Location** (optional, default: US) — country, state, city, or region
• **Company size** (optional) — employee count min/max, revenue floor
• **Hiring signal roles** (optional) — job postings that indicate buying intent (e.g., "Scheduling Coordinator" = ops pain, "DevOps Engineer" = infra investment)
• **Max results** (optional, default 15)
• **Company/product** (optional) — if user mentions what they're selling, triggers competitive intel in Step 7

2. Find Target Companies

Run 2-3 search strategies **in parallel**:

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

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/searchscraper"}'
  "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):

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/companies"}'
  "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
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"nyne","path":"/company/search","body":{"query":"{industry} companies {location} {size_qualifier}"}}'
# Step 2: Poll with GET using request_id
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"nyne","path":"/company/search","query":{"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
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/companies"}'
  "query": "{industry} companies in New York with {size}+ employees",
  "pageSize": 15
}'

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/companies"}'
  "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:

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Con