1. Lead Research & Enrichment
Goal: Research a target company and build a complete profile of key contacts. Tools used: LinkUp (AI search) → Hunter (email finding) → Apollo (enrichment)# 1. Research the company with AI search
orth run linkup /search --body '{"q": "stripe company funding valuation news 2026", "depth": "standard"}'
# 2. Find contacts at the company
orth run hunter /domain-search --body '{"domain": "stripe.com"}'
# 3. Enrich the top contact (use an email from step 2)
orth run apollo /v1/people/match --body '{"email": "founder@stripe.com"}'
const ORTH_KEY = process.env.ORTHOGONAL_API_KEY;
async function run(api, path, body) {
const res = await fetch('https://api.orthogonal.com/v1/run', {
method: 'POST',
headers: {
'Authorization': `Bearer ${ORTH_KEY}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({ api, path, body })
});
return res.json();
}
async function researchLead(company) {
// Step 1: Research the company with AI search
const research = await run('linkup', '/search', {
q: `${company} company funding valuation news 2026`,
depth: 'standard'
});
// Step 2: Find contacts at the company
const contacts = await run('hunter', '/domain-search', {
domain: `${company}.com`
});
// Step 3: Enrich the top contact
const topContact = contacts.data?.data?.emails?.[0];
if (topContact?.value) {
const enriched = await run('apollo', '/v1/people/match', {
email: topContact.value
});
return { research: research.data, contact: enriched.data };
}
return { research: research.data, contacts: contacts.data };
}
const result = await researchLead('stripe');
import requests
import os
ORTH_KEY = os.environ['ORTHOGONAL_API_KEY']
def run(api, path, body):
return requests.post(
'https://api.orthogonal.com/v1/run',
headers={'Authorization': f'Bearer {ORTH_KEY}'},
json={'api': api, 'path': path, 'body': body}
).json()
def research_lead(company):
# Step 1: Research the company with AI search
research = run('linkup', '/search', {
'q': f'{company} company funding valuation news 2026',
'depth': 'standard'
})
# Step 2: Find contacts at the company
contacts = run('hunter', '/domain-search', {
'domain': f'{company}.com'
})
# Step 3: Enrich the top contact
emails = contacts.get('data', {}).get('data', {}).get('emails', [])
if emails:
enriched = run('apollo', '/v1/people/match', {
'email': emails[0]['value']
})
return {'research': research['data'], 'contact': enriched['data']}
return {'research': research['data'], 'contacts': contacts['data']}
result = research_lead('stripe')
2. Competitor Social Intelligence
Goal: Monitor a competitor’s social media presence and extract brand assets. Tools used: Shofo (social scraping) → Brand.dev (brand extraction) → Olostep (web scraping)# 1. Recent LinkedIn posts
orth run shofo /linkedin/company-posts --body '{"company_url": "https://linkedin.com/company/notionhq"}'
# 2. Brand assets - logos, colors
orth run brand-dev /v1/brand/retrieve --body '{"domain": "notion.so"}'
# 3. Scrape the pricing page
orth run olostep /v1/scrapes --body '{"url_to_scrape": "https://notion.so/pricing", "formats": ["markdown"]}'
async function analyzeCompetitor(domain, linkedinSlug) {
// Step 1: Get their recent LinkedIn posts
const posts = await run('shofo', '/linkedin/company-posts', {
company_url: `https://linkedin.com/company/${linkedinSlug}`
});
// Step 2: Extract brand assets - logos, colors
const brand = await run('brand-dev', '/v1/brand/retrieve', {
domain: domain
});
// Step 3: Scrape their pricing page
const pricing = await run('olostep', '/v1/scrapes', {
url_to_scrape: `https://${domain}/pricing`,
formats: ['markdown']
});
return {
socialActivity: posts.data,
brandAssets: brand.data,
pricingInfo: pricing.data
};
}
const intel = await analyzeCompetitor('notion.so', 'notionhq');
def analyze_competitor(domain, linkedin_slug):
# Step 1: Get their recent LinkedIn posts
posts = run('shofo', '/linkedin/company-posts', {
'company_url': f'https://linkedin.com/company/{linkedin_slug}'
})
# Step 2: Extract brand assets - logos, colors
brand = run('brand-dev', '/v1/brand/retrieve', {
'domain': domain
})
# Step 3: Scrape their pricing page
pricing = run('olostep', '/v1/scrapes', {
'url_to_scrape': f'https://{domain}/pricing',
'formats': ['markdown']
})
return {
'social_activity': posts['data'],
'brand_assets': brand['data'],
'pricing_info': pricing['data']
}
intel = analyze_competitor('notion.so', 'notionhq')
3. Event-Based Prospecting
Goal: Find people who recently engaged with relevant content and enrich them for outreach. Tools used: Fiber (people search) → Shofo (social activity) → Tomba (email finding)# 1. Find people matching your ICP
orth run fiber /v1/natural-language-search/profiles --body '{"query": "VP of Sales at Series B SaaS companies in San Francisco", "limit": 10}'
# For each profile from step 1, plug in its linkedin_url:
# 2. Check recent LinkedIn activity
orth run shofo /linkedin/user-posts --body '{"profile_url": "<linkedin_url>"}'
# 3. Find their email
orth run tomba /v1/linkedin --body '{"url": "<linkedin_url>"}'
async function findEngagedProspects(criteria) {
// Step 1: Find people matching your ICP
const prospects = await run('fiber', '/v1/natural-language-search/profiles', {
query: criteria,
limit: 10
});
const enrichedProspects = [];
for (const person of prospects.data?.profiles || []) {
// Step 2: Check their recent LinkedIn activity
const activity = await run('shofo', '/linkedin/user-posts', {
profile_url: person.linkedin_url
});
// Step 3: Find their email
const email = await run('tomba', '/v1/linkedin', {
url: person.linkedin_url
});
enrichedProspects.push({
...person,
recentPosts: activity.data?.posts?.slice(0, 3),
email: email.data?.data?.email
});
}
return enrichedProspects;
}
const prospects = await findEngagedProspects(
'VP of Sales at Series B SaaS companies in San Francisco'
);
def find_engaged_prospects(criteria):
# Step 1: Find people matching your ICP
prospects = run('fiber', '/v1/natural-language-search/profiles', {
'query': criteria,
'limit': 10
})
enriched_prospects = []
for person in prospects.get('data', {}).get('profiles', []):
# Step 2: Check their recent LinkedIn activity
activity = run('shofo', '/linkedin/user-posts', {
'profile_url': person['linkedin_url']
})
# Step 3: Find their email
email = run('tomba', '/v1/linkedin', {
'url': person['linkedin_url']
})
enriched_prospects.append({
**person,
'recent_posts': activity.get('data', {}).get('posts', [])[:3],
'email': email.get('data', {}).get('data', {}).get('email')
})
return enriched_prospects
prospects = find_engaged_prospects(
'VP of Sales at Series B SaaS companies in San Francisco'
)
4. Account-Based Marketing Research
Goal: Deep-dive on a target account - company intel, key people, and their social presence. Tools used: Brand.dev (company data) → Fiber (find employees) → Apollo (enrich contacts)# 1. Company brand info and products
orth run brand-dev /v1/brand/retrieve --body '{"domain": "figma.com"}'
# 2. Find decision makers
orth run fiber /v1/natural-language-search/profiles --body '{"query": "executives and VPs at figma.com company", "limit": 5}'
# For each profile from step 2, plug in its linkedin_url:
# 3. Enrich with contact details
orth run apollo /v1/people/match --body '{"linkedin_url": "<linkedin_url>"}'
async function abmResearch(targetDomain) {
// Step 1: Get company brand info and products
const company = await run('brand-dev', '/v1/brand/retrieve', {
domain: targetDomain
});
// Step 2: Find decision makers
const people = await run('fiber', '/v1/natural-language-search/profiles', {
query: `executives and VPs at ${targetDomain} company`,
limit: 5
});
// Step 3: Enrich each person with contact details
const enrichedPeople = [];
for (const person of people.data?.profiles || []) {
const enriched = await run('apollo', '/v1/people/match', {
linkedin_url: person.linkedin_url
});
enrichedPeople.push({
...person,
contact: enriched.data?.person
});
}
return {
company: company.data,
decisionMakers: enrichedPeople
};
}
const account = await abmResearch('figma.com');
def abm_research(target_domain):
# Step 1: Get company brand info and products
company = run('brand-dev', '/v1/brand/retrieve', {
'domain': target_domain
})
# Step 2: Find decision makers
people = run('fiber', '/v1/natural-language-search/profiles', {
'query': f'executives and VPs at {target_domain} company',
'limit': 5
})
# Step 3: Enrich each person with contact details
enriched_people = []
for person in people.get('data', {}).get('profiles', []):
enriched = run('apollo', '/v1/people/match', {
'linkedin_url': person['linkedin_url']
})
enriched_people.append({
**person,
'contact': enriched.get('data', {}).get('person')
})
return {
'company': company['data'],
'decision_makers': enriched_people
}
account = abm_research('figma.com')
5. Content & Trigger Monitoring
Goal: Monitor social channels for buying signals and company news. Tools used: Shofo (X/Twitter monitoring) → LinkUp (news search) → Riveter (structured extraction)# 1. Recent tweets from the company
orth run shofo /x/user-posts --body '{"username": "stripe"}'
# 2. Recent news and announcements
orth run linkup /search --body '{"q": "stripe.com announcement funding launch 2026", "depth": "standard"}'
# 3. Extract structured triggers from the news text (paste content from step 2)
orth run riveter /v1/run --body '{"input": "<news text>", "output_schema": {"type": "object", "properties": {"funding_events": {"type": "array", "items": {"type": "string"}}, "product_launches": {"type": "array", "items": {"type": "string"}}, "hiring_signals": {"type": "array", "items": {"type": "string"}}, "expansion_news": {"type": "array", "items": {"type": "string"}}}}}'
async function monitorTriggers(companyHandle, domain) {
// Step 1: Get recent tweets mentioning the company
const tweets = await run('shofo', '/x/user-posts', {
username: companyHandle
});
// Step 2: Search for recent news/announcements
const news = await run('linkup', '/search', {
q: `${domain} announcement funding launch 2026`,
depth: 'standard'
});
// Step 3: Extract structured triggers from the news
const triggers = await run('riveter', '/v1/run', {
input: news.data?.results?.map(r => r.content).join('\n\n'),
output_schema: {
type: 'object',
properties: {
funding_events: { type: 'array', items: { type: 'string' } },
product_launches: { type: 'array', items: { type: 'string' } },
hiring_signals: { type: 'array', items: { type: 'string' } },
expansion_news: { type: 'array', items: { type: 'string' } }
}
}
});
return {
socialActivity: tweets.data,
news: news.data,
triggers: triggers.data
};
}
const signals = await monitorTriggers('stripe', 'stripe.com');
def monitor_triggers(company_handle, domain):
# Step 1: Get recent tweets mentioning the company
tweets = run('shofo', '/x/user-posts', {
'username': company_handle
})
# Step 2: Search for recent news/announcements
news = run('linkup', '/search', {
'q': f'{domain} announcement funding launch 2026',
'depth': 'standard'
})
# Step 3: Extract structured triggers from the news
content = '\n\n'.join([r.get('content', '') for r in news.get('data', {}).get('results', [])])
triggers = run('riveter', '/v1/run', {
'input': content,
'output_schema': {
'type': 'object',
'properties': {
'funding_events': {'type': 'array', 'items': {'type': 'string'}},
'product_launches': {'type': 'array', 'items': {'type': 'string'}},
'hiring_signals': {'type': 'array', 'items': {'type': 'string'}},
'expansion_news': {'type': 'array', 'items': {'type': 'string'}}
}
}
})
return {
'social_activity': tweets['data'],
'news': news['data'],
'triggers': triggers['data']
}
signals = monitor_triggers('stripe', 'stripe.com')