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Universal context infrastructure for AI applications. Connect any MCP-compatible platform to the largest library of machine-native content.
Free
7-Day Context
1,000
Stories/Month
4 Tools
MCP Functions
// Multi-dimensional precision targeting
synorb.get_stories({
stream_id: 889, // Andreessen Horowitz
entity_details: [
{
entity_type: "Topics",
entity_value: "AI"
},
{
entity_type: "people",
entity_value: "Ben Horowitz"
},
{
entity_type: "content_type",
entity_value: "Podcast"
}
]
})
// Surgical section extraction + provenance
synorb.get_stories({
stream_id: 889,
body_sections: [
"Key Insights",
"Signals"
]
}).then(res => {
res.stories.forEach(story => {
const citation = {
insight: story.story.body,
source: story.evidence_ref.source_urls[0],
confidence: story.classification_confidence,
pipeline: story.provenance?.pipeline_version
};
});
})
// Cross-domain VC + SEC validation
const [vcView, secData] = await Promise.all([
// VC perspective
synorb.get_stories({
stream_id: 889, // a16z
entity_details: [{
entity_type: "organization",
entity_value: "Acme Corp"
}]
}),
// SEC filings
synorb.get_stories({
stream_id: 786, // SEC 8-K
entity_details: [{
entity_type: "organization",
entity_value: "Acme Corp"
}]
})
]);
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Claude
Pro, Max, Team and Enterprise plans
Connect Synorb to Claude and start querying streams immediately.
1
Find your personalized URL in your welcome email
2
Paste it into Claude Connector
3
Test: "What streams do I have access to?"
Claude Setup Demo
ChatGPT
Developer Pro and Plus accounts
Connect via Developer Mode today. Standard ChatGPT support coming soon!
1
Find your personalized URL in your welcome email
2
Paste it into ChatGPT Connector
3
Test: "What streams do I have access to?"
MCP Tools Reference
Four powerful tools with rich metadata, multi-dimensional filtering, and surgical context extraction
TOOL
synorb-profile
Real-time quota tracking with usage analytics, overage monitoring, and subscription status
Response Schema
{
"status": "success",
"user_id": 430,
"access_token": "2f91a75b...",
"usage": {
"content_type": "Discovery",
"quota": 1000,
"quota_period": "month",
"usage": 370,
"subscription_start": "2025-09-17",
"quota_reset_date": "2025-10-17",
"overage": 0
}
}
TOOL
synorb-stream-catalog
Complete stream library with full metadata—domains, entity types, update cadence, and provenance
Stream Metadata
{
"id": 889,
"title": "Andreessen Horowitz",
"stream_class": "discovery",
"update_cadence": "Weekly",
"home_domain": "economics-business-work",
"cross_domains": ["engineering-technology"],
"primary_entity": {
"type": "org",
"name": "Andreessen Horowitz",
"role": "publisher",
"identifiers": {
"website": "a16z.com",
"acronym": "a16z"
}
}
}
TOOL
synorb-stream-details
Deep stream intelligence: activity metrics, dynamic body sections, multi-dimensional filters, NLQ examples
Activity Metrics
5
7 Days
45
30 Days
148
90 Days
Body Sections (15+ per story)
Overview
Key Insights
Notable Quotes
Actionable Takeaways
Announcement
Key Details
Big Picture
Key Arguments
Signals
Entities Mentioned
Links
Portfolio Mentions
Risks & Watchpoints
Resources Mentioned
Timestamps
Multi-Dimensional Filters
Topics
AI
Crypto
Enterprise
Biotech
Fintech
+5 more
Content Type
Blog Post
Podcast
Tweet Thread
People (60+ tracked)
Ben Horowitz
Marc Andreessen
Chris Dixon
+57 more
Natural Language Query Examples (25 per stream)
What is Andreessen Horowitz's view on AI regulation?
How does a16z evaluate crypto investments?
What are the latest AI startups funded by Andreessen Horowitz?
TOOL
synorb-stream-stories
Structured stories with multi-parameter filtering, granular section selection, evidence refs, and domain classification
Surgical Section Extraction
// Request only specific reasoning sections
{
"stream_id": 889,
"body_sections": [
"Key Insights",
"Actionable Takeaways"
]
}
Multi-Entity Boolean Filtering
// Boolean AND across multiple filters
{
"stream_id": 889,
"entity_details": [
{
"entity_type": "Topics",
"entity_value": "AI"
},
{
"entity_type": "people",
"entity_value": "Ben Horowitz"
},
{
"entity_type": "content_type",
"entity_value": "Podcast"
}
]
}
Response Structure with Provenance
{
"story_id": 1126730,
"filters": [/* What matched */],
"classification_confidence": 0.95,
"evidence_ref": {
"source_urls": ["https://a16z.com/..."]
},
"cross_domains": ["engineering-technology"],
"story": {
"body_sections": [/* Requested sections */],
"body": "# Markdown formatted..."
}
}
Real-World Implementation
Cross-domain intelligence patterns combining VCs, SEC filings, real estate, policy analysis, and public markets
Investment Due Diligence Pipeline
Multi-source company intelligence combining regulatory filings, earnings calls, and VC perspective
SEC 8-K
Earnings Calls
a16z
// Multi-source company research
async function researchCompany(companyName) {
const [secRisks, earnings, vcView] =
await Promise.all([
// 1. SEC regulatory risks
synorb.get_stories({
stream_id: 786, // SEC Form 8-K
entity_details: [{
entity_type: "organization",
entity_value: companyName
}],
body_sections: [
"Risks & Watchpoints",
"Key Details"
]
}),
// 2. Earnings call sentiment
synorb.get_stories({
stream_id: 1015, // Earnings Calls
entity_details: [{
entity_type: "organization",
entity_value: companyName
}],
body_sections: [
"Key Insights",
"Signals"
]
}),
// 3. VC perspective
synorb.get_stories({
stream_id: 889, // a16z
entity_details: [{
entity_type: "organization",
entity_value: companyName
}],
body_sections: [
"Portfolio Mentions",
"Big Picture"
]
})
]);
return {
regulatory_risks: secRisks.stories,
financial_health: earnings.stories,
vc_sentiment: vcView.stories
};
}
Real Estate Market Intelligence
Price trend analysis with loss pattern detection for market stress signals
Price Drops
Loss Sales
// Track market softness in Boston
const bostonAnalysis = async () => {
const [priceDrops, lossSales] =
await Promise.all([
// Recent price reductions
synorb.get_stories({
stream_id: 1011,
entity_details: [{
entity_type: "place",
entity_value: "Boston"
}],
body_sections: [
"Overview",
"Key Details"
],
published_date_from: "2025-09-01"
}),
// Properties sold at loss
synorb.get_stories({
stream_id: 1012,
entity_details: [{
entity_type: "place",
entity_value: "Boston"
}],
body_sections: [
"Key Insights",
"Signals"
],
published_date_from: "2025-01-01"
})
]);
const avgDrop = calculateAverage(
priceDrops.stories.map(s =>
extractDropPercentage(s)
)
);
return {
market_stress_score: avgDrop * lossSales.stories.length,
price_drops: priceDrops.stories,
loss_sales: lossSales.stories
};
};
Geopolitical Risk Monitor
Policy analysis correlated with investor response for sector impact assessment
Atlantic Council
Sequoia
// Track policy shifts and sector impact
const policyImpact = async (region, sector) => {
const [policy, vcReaction] =
await Promise.all([
// Policy analysis
synorb.get_stories({
stream_id: 521, // Atlantic Council
entity_details: [
{
entity_type: "place",
entity_value: region
},
{
entity_type: "topic",
entity_value: sector
}
],
body_sections: [
"Big Picture",
"Risks & Watchpoints"
]
}),
// VC response
synorb.get_stories({
stream_id: 989, // Sequoia
entity_details: [{
entity_type: "place",
entity_value: region
}],
body_sections: [
"Signals",
"Portfolio Mentions"
]
})
]);
return {
policy_analysis: policy.stories,
investor_response: vcReaction.stories,
correlation: analyzeCorrelation(
policy,
vcReaction
)
};
};
Crowdfunding Deal Flow
Early-stage funding landscape via SEC Reg CF and Reg A filings
SEC Reg CF
SEC Reg A
// Track early-stage tech funding
const crowdfundingPipeline = async () => {
const [regCF, regA] = await Promise.all([
// Regulation Crowdfunding
synorb.get_stories({
stream_id: 566,
entity_details: [{
entity_type: "topic",
entity_value: "Technology"
}],
body_sections: [
"Announcement",
"Key Details"
],
published_date_from: "2025-09-01"
}),
// Regulation A offerings
synorb.get_stories({
stream_id: 567,
entity_details: [{
entity_type: "topic",
entity_value: "Technology"
}],
body_sections: [
"Key Details",
"Signals"
],
published_date_from: "2025-09-01"
})
]);
const deals = [...regCF.stories, ...regA.stories];
return {
deals,
sectors: analyzeSectorDistribution(deals),
avgRaise: calculateAverageRaise(deals)
};
};
Cross-Sector Correlation Engine
Four-source convergence analysis across VCs, policy, public markets, and regulatory filings
General Catalyst
Atlantic Council
Earnings
SEC 8-K
// Multi-source sector intelligence
const crossSector = async (sector) => {
const [vc, policy, earnings, sec] =
await Promise.all([
// VC activity
synorb.get_stories({
stream_id: 947,
entity_details: [{
entity_type: "Topics",
entity_value: sector
}],
body_sections: [
"Portfolio Mentions",
"Signals"
]
}),
// Policy landscape
synorb.get_stories({
stream_id: 521,
entity_details: [{
entity_type: "topic",
entity_value: sector
}],
body_sections: [
"Big Picture",
"Risks & Watchpoints"
]
}),
// Public sentiment
synorb.get_stories({
stream_id: 1015,
entity_details: [{
entity_type: "topic",
entity_value: sector
}],
body_sections: [
"Key Insights",
"Signals"
]
}),
// Regulatory activity
synorb.get_stories({
stream_id: 786,
entity_details: [{
entity_type: "topic",
entity_value: sector
}],
body_sections: [
"Announcement",
"Key Details"
]
})
]);
return {
vc_positioning: vc.stories,
policy_headwinds: policy.stories,
public_sentiment: earnings.stories,
regulatory_activity: sec.stories,
convergence_score: calculateConvergence([
vc, policy, earnings, sec
])
};
};
Podcast Intelligence + SEC Validation
Verify VC podcast claims against regulatory filings for investment validation
20VC Podcast
SEC 10-K
// Fact-check VC claims with SEC data
const verifyVCClaims = async (company) => {
const [podcast, sec10K] =
await Promise.all([
// VC podcast mentions
synorb.get_stories({
stream_id: 1014,
entity_details: [{
entity_type: "organization",
entity_value: company
}],
body_sections: [
"Notable Quotes",
"Key Arguments"
],
published_date_from: "2025-09-01"
}),
// SEC 10-K verification
synorb.get_stories({
stream_id: 564,
entity_details: [{
entity_type: "organization",
entity_value: company
}],
body_sections: [
"Key Details",
"Risks & Watchpoints"
]
})
]);
const claims = extractClaims(podcast.stories);
const facts = extractFacts(sec10K.stories);
return {
podcast_narrative: claims,
sec_reality: facts,
alignment_score: compareNarratives(
claims,
facts
)
};
};
Advanced Filtering Patterns
Surgical precision techniques for multi-dimensional filtering, temporal control, and section-level extraction
1
Multi-Entity Boolean Filtering
Combine multiple filters with AND logic for precise targeting across Topics, People, Content Type, and Organizations
❌ Basic
// Single dimension - too broad
synorb.get_stories({
stream_id: 889,
entity_details: [{
entity_type: "Topics",
entity_value: "AI"
}]
})
// Returns ALL AI content
// 100+ stories, unfocused
✅ Advanced
// Multi-dimensional precision
synorb.get_stories({
stream_id: 889,
entity_details: [
{
entity_type: "Topics",
entity_value: "AI"
},
{
entity_type: "people",
entity_value: "Ben Horowitz"
},
{
entity_type: "content_type",
entity_value: "Podcast"
}
]
})
// Only Ben's AI podcasts
// ~5 highly relevant stories
Key Insight
Boolean AND across filters reduces noise by 95%. Use 2-3 filters for surgical precision without over-constraining results.
2
Surgical Section Extraction
Extract only specific reasoning sections from stories—eliminate fluff, get exactly what you need for RAG or analysis
❌ Basic
// Full story - 15+ sections
synorb.get_stories({
stream_id: 889,
published_date_from: "2025-09-01"
})
// Returns everything:
// Overview, Quotes, Details,
// Timestamps, Links, etc.
// ~3000 tokens per story
✅ Advanced
// Only reasoning sections
synorb.get_stories({
stream_id: 889,
published_date_from: "2025-09-01",
body_sections: [
"Key Insights",
"Big Picture",
"Signals"
]
})
// Returns only critical analysis
// ~800 tokens per story
// 73% token reduction
RAG Optimization
Section filtering reduces LLM context by 60-80% while preserving signal. Perfect for high-quality RAG pipelines with token constraints.
3
Temporal Precision Control
Query by exact date ranges or use activity metrics to detect and query hot streams dynamically
// Activity-based intelligence
const details = await synorb.get_stream_details({
stream_id: 889
});
if (details.stories_over_last_7_days > 10) {
// Hot stream - query recent only
const trending = await synorb.get_stories({
stream_id: 889,
published_date_from: getLastWeek(),
body_sections: ["Signals", "Big Picture"]
});
} else {
// Quiet stream - expand window
const recent = await synorb.get_stories({
stream_id: 889,
published_date_from: getLastMonth()
});
}
// Custom date ranges
synorb.get_stories({
stream_id: 889,
published_date_from: "2025-09-15",
published_date_to: "2025-09-30"
})
Dynamic Querying
Use 7/30/90-day metrics to adapt query windows. High-activity streams need narrow windows; quiet streams need broader ranges for sufficient signal.
4
Cross-Domain Intelligence Discovery
Find content that spans multiple domains for unique cross-sector insights and correlation analysis
// Find AI content across multiple domains
const catalog = await synorb.get_catalog();
// Filter for cross-domain AI streams
const crossDomainAI = catalog.streams.filter(stream =>
stream.cross_domains?.includes("engineering-technology") &&
stream.home_domain === "economics-business-work"
);
// Query each for recent AI content
const insights = await Promise.all(
crossDomainAI.map(async stream => {
const stories = await synorb.get_stories({
stream_id: stream.id,
entity_details: [{
entity_type: "Topics",
entity_value: "AI"
}],
body_sections: ["Key Insights"]
});
return {
source: stream.title,
insights: stories
};
})
);
// Compare perspectives across domains
const synthesis = synthesizePerspectives(insights);
Cross-Domain Pattern
Cross-referenced streams reveal unique angles. Business content (home_domain) with tech implications (cross_domain) provides investment-grade intelligence.
5
Provenance-Aware Filtering
Build trust into your pipeline by tracking source URLs, pipeline versions, and classification confidence
// Query with provenance tracking
const stories = await synorb.get_stories({
stream_id: 889,
published_date_from: "2025-09-01",
body_sections: ["Key Insights"]
});
// Filter by confidence and validate sources
const highConfidence = stories.stories.filter(story =>
story.classification_confidence > 0.9 &&
story.evidence_ref?.source_urls?.length > 0
);
// Build citation-ready output
const citableInsights = highConfidence.map(story => ({
insight: story.story.body,
source: story.evidence_ref.source_urls[0],
confidence: story.classification_confidence,
pipeline: story.provenance?.pipeline_version,
domains: {
primary: story.home_domain,
cross: story.cross_domains
}
}));
// Feed to LLM with full attribution
const prompt = `
Sources (confidence > 90%):
${citableInsights.map(i =>
`- ${i.source} (${(i.confidence * 100).toFixed(0)}%)`
).join('\n')}
Context: ${citableInsights.map(i => i.insight).join('\n---\n')}
`;
Trust & Attribution
Filter by classification_confidence > 0.9 for high-quality results. Always include source URLs for full transparency and citation capabilities.
Intelligence Workflows
Production-ready patterns for market research, competitive intelligence, investment validation, and RAG pipelines
Market Research Pipeline
Discover streams → Analyze activity → Extract insights → Generate synthesis report
1
Catalog Discovery
→
// Find relevant streams for your sector
const catalog = await synorb.get_catalog();
const aiStreams = catalog.streams.filter(s =>
s.subject_domains.includes("engineering-technology") &&
s.stream_class === "discovery"
);
Filter catalog by domain and stream class to find relevant intelligence sources
2
Activity Analysis
→
// Check which streams are hot
const streamActivity = await Promise.all(
aiStreams.map(async stream => {
const details = await synorb.get_stream_details({
stream_id: stream.id
});
return {
...stream,
activity_7d: details.stories_over_last_7_days,
activity_30d: details.stories_over_last_30_days
};
})
);
// Prioritize high-activity streams
const hotStreams = streamActivity
.filter(s => s.activity_7d > 5)
.sort((a,b) => b.activity_7d - a.activity_7d);
Use activity metrics to identify trending sources worth deeper investigation
3
Insight Extraction
→
// Extract insights from hot streams
const insights = await Promise.all(
hotStreams.slice(0, 5).map(async stream => {
const stories = await synorb.get_stories({
stream_id: stream.id,
entity_details: [{
entity_type: "Topics",
entity_value: "AI"
}],
body_sections: [
"Key Insights",
"Big Picture",
"Signals"
],
published_date_from: getLastWeek()
});
return {
source: stream.title,
insights: stories.stories
};
})
);
Query top streams with surgical section filtering for high-signal content
4
Synthesis Report
// Generate market intelligence report
const report = {
sector: "AI",
timeframe: "Last 7 days",
sources_analyzed: hotStreams.length,
total_stories: insights.reduce(
(sum, i) => sum + i.insights.length, 0
),
key_insights: insights.map(i => ({
source: i.source,
summary: extractKeyPoints(i.insights),
evidence: i.insights.map(s =>
s.evidence_ref.source_urls[0]
)
})),
signals: identifyTrends(insights)
};
Output
Structured market intelligence report with sourced insights, trend signals, and full provenance for investment decisions
Competitive Intelligence Monitor
Multi-source tracking → Cross-reference validation → Alert generation
1
Multi-Source Monitoring
→
// Track competitor across multiple sources
const competitor = "CompanyX";
const [vcMentions, secFilings, earnings, media] =
await Promise.all([
// VC perspective
synorb.get_stories({
stream_id: 889,
entity_details: [{
entity_type: "organization",
entity_value: competitor
}]
}),
// SEC activity
synorb.get_stories({
stream_id: 786,
entity_details: [{
entity_type: "organization",
entity_value: competitor
}]
}),
// Earnings calls
synorb.get_stories({
stream_id: 1015,
entity_details: [{
entity_type: "organization",
entity_value: competitor
}]
}),
// Industry podcasts
synorb.get_stories({
stream_id: 1014,
entity_details: [{
entity_type: "organization",
entity_value: competitor
}]
})
]);
2
Cross-Reference Validation
→
// Identify mentions across sources
const signals = detectSignals({
funding: extractFundingNews(vcMentions, secFilings),
product: extractProductNews(media, earnings),
leadership: extractPeopleChanges(secFilings, media),
financial: extractFinancials(earnings, secFilings)
});
// Flag corroborated signals
const validated = signals.filter(s =>
s.source_count >= 2 &&
s.confidence > 0.8
);
Cross-validate signals across sources to reduce false positives
3
Alert Generation
// Generate alerts for validated signals
const alerts = validated.map(signal => ({
priority: signal.confidence > 0.9 ? "HIGH" : "MEDIUM",
type: signal.category,
summary: signal.text,
sources: signal.sources,
timestamp: new Date(),
action_items: generateActionItems(signal)
}));
// Deliver via preferred channel
if (alerts.some(a => a.priority === "HIGH")) {
await sendSlackAlert(alerts);
}
Output
Real-time competitive alerts with confidence scoring, multi-source validation, and actionable recommendations
Investment Thesis Validation
VC narrative → Regulatory reality check → Public sentiment → Convergence score
1
VC Narrative Analysis
→
// Extract VC investment thesis
const vcThesis = await synorb.get_stories({
stream_id: 889,
entity_details: [{
entity_type: "Topics",
entity_value: "Enterprise AI"
}],
body_sections: [
"Key Arguments",
"Big Picture",
"Portfolio Mentions"
]
});
const claims = extractInvestmentClaims(vcThesis.stories);
2
Regulatory Reality Check
→
// Check SEC filings for validation
const secData = await synorb.get_stories({
stream_id: 564, // 10-K filings
entity_details: [{
entity_type: "topic",
entity_value: "Enterprise AI"
}],
body_sections: [
"Key Details",
"Risks & Watchpoints"
]
});
const facts = extractFinancialReality(secData.stories);
3
Public Market Sentiment
→
// Get earnings call sentiment
const publicSentiment = await synorb.get_stories({
stream_id: 1015,
entity_details: [{
entity_type: "topic",
entity_value: "Enterprise AI"
}],
body_sections: ["Key Insights", "Signals"]
});
const market = extractMarketView(publicSentiment.stories);
4
Convergence Analysis
// Calculate thesis validation score
const validation = {
vc_claims: claims,
regulatory_facts: facts,
market_sentiment: market,
alignment_score: calculateAlignment(
claims,
facts,
market
),
divergence_points: identifyDivergence(
claims,
facts
),
recommendation: generateRecommendation({
alignment: validation.alignment_score,
divergence: validation.divergence_points
})
};
Output
Investment validation report with convergence scoring, divergence analysis, and data-driven recommendation
RAG Context Extraction Pipeline
Query with sections → Filter by confidence → Build citations → Feed to LLM
1
Surgical Query
→
// Request only reasoning sections
const context = await synorb.get_stories({
stream_id: 889,
entity_details: [{
entity_type: "Topics",
entity_value: "AI Safety"
}],
body_sections: [
"Key Insights",
"Key Arguments",
"Big Picture"
],
published_date_from: getLastMonth()
});
Section filtering reduces tokens by 70% while preserving signal quality
2
Quality Filtering
→
// Filter by confidence and validate sources
const highQuality = context.stories.filter(story =>
story.classification_confidence > 0.9 &&
story.evidence_ref?.source_urls?.length > 0
);
Confidence threshold ensures only high-quality context enters your RAG pipeline
3
Citation Building
→
// Build citation-ready chunks
const chunks = highQuality.map(story => ({
content: story.story.body,
metadata: {
source_url: story.evidence_ref.source_urls[0],
confidence: story.classification_confidence,
published_date: story.published_date,
domains: {
primary: story.home_domain,
cross: story.cross_domains
},
provenance: story.provenance
}
}));
4
LLM Prompt Construction
// Build fully-attributed prompt
const prompt = `
Context from ${chunks.length} authoritative sources:
${chunks.map((c, i) => `
[Source ${i+1}] ${c.metadata.source_url}
Confidence: ${(c.metadata.confidence * 100).toFixed(0)}%
Published: ${c.metadata.published_date}
${c.content}
`).join('\n---\n')}
Question: What is the current thinking on AI safety at leading VC firms?
Instructions: Synthesize the above context and cite sources by number.
`;
const response = await llm.generate(prompt);
Output
Citation-ready LLM prompts with provenance tracking, confidence scoring, and full source attribution for trustworthy AI responses
Production Architecture
Technical specifications, performance patterns, and integration strategies for production deployments
Rate Limits & Quotas
Monthly story limits and overage pricing
Free Tier
1,000
stories/mo
Vibe Coder
100,000
stories/mo
Pro Developer (Individual)
1,000,000
stories/mo
Pro Developer (Enterprise)
10,000,000
stories/mo
// Monitor quota in real-time
const profile = await synorb.get_profile();
console.log({
quota: profile.usage.quota,
used: profile.usage.usage,
remaining: profile.usage.quota - profile.usage.usage,
reset_date: profile.usage.quota_reset_date
});
// Check if approaching limit
const percentUsed = (profile.usage.usage / profile.usage.quota) * 100;
if (percentUsed > 90) {
console.warn('Approaching quota limit');
}
Overage pricing: $0.001 per story beyond monthly limit. Monitor usage via synorb-profile tool.
Quick Reference
Key specifications and capabilities
30 sec
Setup Time
15+
Body Sections/Stream
25
NLQ Examples/Stream
7/30/90
Activity Windows (days)
5+
Filter Dimensions
2
Output Formats
Filter dimensions: Topics, people, organizations, places, content_type. Output formats: JSON, Markdown.
Performance Optimization
Maximize throughput and minimize latency
Parallel Queries
Query multiple streams concurrently using Promise.all for 3-5x speedup.
Section Filtering
Request only needed body_sections to reduce payload size significantly.
Pagination
Use page_num and page_size parameters to fetch stories in batches.
// Parallel stream queries
const streamIds = [889, 947, 989];
const results = await Promise.all(
streamIds.map(async id => {
return synorb.get_stories({
stream_id: id,
body_sections: ["Key Insights"], // Reduce payload
page_size: 100,
page_num: 0
});
})
);
// 3 streams queried in parallel
// Total time ≈ slowest query, not sum
Integration Patterns
Common deployment architectures
Stream Selection Logic
Use activity metrics (7/30/90-day counts) to prioritize high-velocity streams for real-time insights.
Batch Processing
Query multiple streams, aggregate results, then process in batches for efficiency.
Multi-Source Validation
Cross-reference stories from different streams (e.g., VC + SEC) for fact-checking pipelines.
// Activity-based stream selection
const details = await synorb.get_stream_details({
stream_id: 889
});
// Check if stream is "hot"
if (details.stories_over_last_7_days > 10) {
// High activity - query recent stories
const stories = await synorb.get_stories({
stream_id: 889,
published_date_from: getLastWeek()
});
}
Combine parallel queries, section filtering, and activity-based selection for optimal production performance.