Complete Examples
This section provides production-ready examples demonstrating complete Juggernaut implementations. All examples use the InputProcess API, Views SDK, and Plugins SDK.
Example 1: Market Evaluation Engine
Scenario: Automating market research and pricing analysis for expansion decisions. Based on the workflow described in the platform walkthrough.
Architecture Overview
4 sequential pipelines with 10 total steps. Input: raw market description. Output: strategic brief with financial model.
Process JSON Definition
{
"name": "Market Evaluation Engine",
"description": "Evaluates market expansion opportunities with financial forecasting and pricing strategy",
"defaultModel": "openai-gpt-4",
"defaultState": {
"currencyCode": "USD",
"riskThreshold": 0.7,
"projectionYears": 5
},
"inputs": [
{
"name": "marketQuery",
"type": "string",
"description": "Raw market scenario description (e.g., 'Expand logistics operations into Philippines')",
"required": true
},
{
"name": "targetVertical",
"type": "string",
"description": "Primary industry vertical (e.g., logistics, fintech, manufacturing)",
"required": true
},
{
"name": "geographicMarket",
"type": "string",
"description": "Target geographic region or country",
"required": true
},
{
"name": "currencyContext",
"type": "string",
"description": "ISO currency code for financial calculations (e.g., USD, EUR, PHP)",
"required": true
}
],
"pipelines": [
{
"name": "Market Intelligence",
"description": "Parse raw input and gather structured market data",
"type": "sync",
"steps": [
{
"name": "Parse Market Query",
"outputName": "structuredQuery",
"type": "Standard",
"taskType": "LLM",
"messages": [
{
"type": "system",
"content": "You are a market research analyst. Extract structured data from business expansion queries."
},
{
"type": "user",
"content": "Parse this market description: {{marketQuery}}. Target industry: {{targetVertical}}, Region: {{geographicMarket}}."
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Structured market analysis",
"properties": {
"marketSize": {
"type": "number",
"description": "Total addressable market (TAM) in millions USD"
},
"growthRate": {
"type": "number",
"description": "Annual market growth rate as decimal (e.g., 0.15 for 15%)"
},
"competitiveDensity": {
"type": "string",
"description": "Competitive landscape: low, medium, or high saturation"
},
"keyTrends": {
"type": "array",
"description": "List of 3-5 major market trends affecting entry",
"items": {
"type": "string",
"description": "Specific trend description with business impact"
}
}
}
}
}
},
{
"name": "Enrich Market Data",
"outputName": "enrichedData",
"type": "Standard",
"taskType": "LLM",
"messages": [
{
"type": "user",
"content": "Enrich this market data with recent financial metrics, regulatory environment, and technology adoption rates: {{structuredQuery}}"
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Enriched market intelligence",
"properties": {
"regulatoryBarriers": {
"type": "array",
"description": "Government or legal obstacles to entry",
"items": {
"type": "object",
"description": "Regulatory factor",
"properties": {
"type": {
"type": "string",
"description": "Category: licensing, compliance, ownership, etc."
},
"difficulty": {
"type": "string",
"description": "Barrier severity: low, medium, high"
},
"estimatedCost": {
"type": "number",
"description": "Cost to overcome in USD thousands"
}
}
}
},
"technologyReadiness": {
"type": "string",
"description": "Local tech infrastructure maturity: primitive, emerging, mature"
},
"talentAvailability": {
"type": "object",
"description": "Workforce analysis",
"properties": {
"availability": {
"type": "string",
"description": "Labor pool status: scarce, adequate, abundant"
},
"averageSalary": {
"type": "number",
"description": "Average annual salary USD for relevant roles"
}
}
}
}
}
}
}
]
},
{
"name": "Strategic Analysis",
"description": "Evaluate attractiveness, pricing, risk, and positioning",
"type": "async",
"steps": [
{
"name": "Market Attractiveness Score",
"outputName": "attractivenessScore",
"type": "Standard",
"taskType": "LLM",
"messages": [
{
"type": "user",
"content": "Calculate market attractiveness (0-100) based on: {{enrichedData}}"
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Attractiveness assessment",
"properties": {
"score": {
"type": "number",
"description": "Composite score 0-100, 70+ is attractive"
},
"factors": {
"type": "array",
"description": "Scoring breakdown",
"items": {
"type": "object",
"description": "Individual factor",
"properties": {
"name": {
"type": "string",
"description": "Factor category (market size, growth, competition)"
},
"weight": {
"type": "number",
"description": "Factor importance percentage"
},
"contribution": {
"type": "number",
"description": "Points contributed to total score"
}
}
}
}
}
}
}
},
{
"name": "Pricing Analysis",
"outputName": "pricingModel",
"type": "Standard",
"taskType": "LLM",
"messages": [
{
"type": "user",
"content": "Develop pricing strategy for {{targetVertical}} in {{geographicMarket}} using market data: {{structuredQuery}}"
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Pricing strategy parameters",
"properties": {
"recommendedPrice": {
"type": "number",
"description": "Suggested unit price in {{currencyContext}}"
},
"priceRange": {
"type": "object",
"description": "Min/max viable prices",
"properties": {
"min": {
"type": "number",
"description": "Floor price maintaining 20% margin"
},
"max": {
"type": "number",
"description": "Ceiling price before demand elasticity drops"
}
}
},
"margin": {
"type": "number",
"description": "Expected gross margin as decimal"
},
"pricingStrategy": {
"type": "string",
"description": "Approach: penetration, skimming, or parity"
}
}
}
}
},
{
"name": "Risk Assessment",
"outputName": "riskProfile",
"type": "Standard",
"taskType": "LLM",
"messages": [
{
"type": "user",
"content": "Assess operational, financial, and geopolitical risks for entering {{geographicMarket}}"
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Comprehensive risk analysis",
"properties": {
"overallRisk": {
"type": "string",
"description": "Aggregate level: low, medium, high"
},
"riskScore": {
"type": "number",
"description": "Risk score 0-1 (1 = highest risk)"
},
"categories": {
"type": "array",
"description": "Risk breakdown by category",
"items": {
"type": "object",
"description": "Risk category",
"properties": {
"category": {
"type": "string",
"description": "Risk type: political, economic, operational, competitive"
},
"severity": {
"type": "number",
"description": "0-10 severity rating"
},
"probability": {
"type": "number",
"description": "Likelihood 0-1"
},
"mitigation": {
"type": "string",
"description": "Strategy to reduce this risk"
}
}
}
}
}
}
}
},
{
"name": "Competitive Positioning",
"outputName": "positioning",
"type": "Standard",
"taskType": "LLM",
"messages": [
{
"type": "user",
"content": "Recommend market positioning strategy for {{targetVertical}} market in {{geographicMarket}}"
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Positioning strategy",
"properties": {
"recommendedPosition": {
"type": "string",
"description": "Market stance: leader, challenger, follower, niche"
},
"differentiation": {
"type": "string",
"description": "Unique value proposition vs competitors"
},
"targetSegments": {
"type": "array",
"description": "Priority customer segments",
"items": {
"type": "object",
"description": "Target segment",
"properties": {
"segment": {
"type": "string",
"description": "Customer segment name"
},
"priority": {
"type": "number",
"description": "Targeting priority 1-5"
},
"estimatedShare": {
"type": "number",
"description": "Expected market share percentage from this segment"
}
}
}
}
}
}
}
}
]
},
{
"name": "Forecasting and GTM",
"description": "Financial modeling and go-to-market planning",
"type": "sync",
"steps": [
{
"name": "Financial Forecast",
"outputName": "financialModel",
"type": "Standard",
"taskType": "LLM",
"messages": [
{
"type": "user",
"content": "Build 5-year financial forecast using pricing: {{pricingModel}}, market data: {{structuredQuery}}, risk profile: {{riskProfile}}"
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "5-year financial projection",
"properties": {
"revenueProjection": {
"type": "array",
"description": "Annual revenue forecast",
"items": {
"type": "object",
"description": "Yearly financials",
"properties": {
"year": {
"type": "number",
"description": "Year number 1-5"
},
"revenue": {
"type": "number",
"description": "Annual revenue USD thousands"
},
"expenses": {
"type": "number",
"description": "Operating expenses USD thousands"
},
"ebitda": {
"type": "number",
"description": "EBITDA USD thousands"
},
"growthRate": {
"type": "number",
"description": "YoY growth decimal"
}
}
}
},
"breakEvenMonth": {
"type": "number",
"description": "Month when cumulative profit turns positive"
},
"roi": {
"type": "number",
"description": "Return on investment percentage at year 5"
},
"capitalRequirements": {
"type": "object",
"description": "Funding needs",
"properties": {
"initial": {
"type": "number",
"description": "Startup capital USD thousands"
},
"workingCapital": {
"type": "number",
"description": "Ongoing working capital needs"
}
}
}
}
}
}
},
{
"name": "Go-to-Market Strategy",
"outputName": "gtmPlan",
"type": "Standard",
"taskType": "LLM",
"messages": [
{
"type": "user",
"content": "Create GTM plan for {{targetVertical}} in {{geographicMarket}} with positioning: {{positioning}}"
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Go-to-market execution plan",
"properties": {
"phases": {
"type": "array",
"description": "Launch phases",
"items": {
"type": "object",
"description": "Phase details",
"properties": {
"phase": {
"type": "number",
"description": "Phase sequence 1-4"
},
"duration": {
"type": "string",
"description": "Timeframe (e.g., 'Months 1-3')"
},
"focus": {
"type": "string",
"description": "Primary objective"
},
"tactics": {
"type": "array",
"description": "Specific activities",
"items": {
"type": "string",
"description": "Tactic description"
}
},
"budget": {
"type": "number",
"description": "Phase budget USD thousands"
},
"milestones": {
"type": "array",
"description": "Success criteria",
"items": {
"type": "string",
"description": "Measurable milestone"
}
}
}
}
},
"kpis": {
"type": "array",
"description": "Key performance indicators",
"items": {
"type": "object",
"description": "KPI definition",
"properties": {
"metric": {
"type": "string",
"description": "KPI name"
},
"target": {
"type": "number",
"description": "Goal value"
},
"timeline": {
"type": "string",
"description": "Time to achieve"
}
}
}
}
}
}
}
}
]
},
{
"name": "Output Assembly",
"description": "Compile final strategic brief",
"type": "sync",
"steps": [
{
"name": "Assemble Strategic Brief",
"outputName": "strategicBrief",
"type": "Standard",
"taskType": "LLM",
"messages": [
{
"type": "system",
"content": "Synthesize all analyses into executive summary format"
},
{
"type": "user",
"content": "Create final brief incorporating: Attractiveness {{attractivenessScore}}, Pricing {{pricingModel}}, Risk {{riskProfile}}, Forecast {{financialModel}}, GTM {{gtmPlan}}"
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Executive deliverable",
"properties": {
"executiveSummary": {
"type": "string",
"description": "One-paragraph recommendation with key rationale"
},
"marketScore": {
"type": "number",
"description": "Composite opportunity score 0-100"
},
"recommendation": {
"type": "string",
"description": "Final verdict: proceed_with_caution, enter_aggressively, do_not_enter, or wait_and_monitor"
},
"confidenceLevel": {
"type": "string",
"description": "Data reliability: high, medium, low"
},
"criticalAssumptions": {
"type": "array",
"description": "Key assumptions that must hold for success",
"items": {
"type": "object",
"description": "Assumption detail",
"properties": {
"assumption": {
"type": "string",
"description": "What is assumed"
},
"riskIfWrong": {
"type": "string",
"description": "Consequences if assumption fails"
},
"validationMethod": {
"type": "string",
"description": "How to verify this assumption"
}
}
}
},
"nextSteps": {
"type": "array",
"description": "Immediate action items if proceeding",
"items": {
"type": "object",
"description": "Action item",
"properties": {
"action": {
"type": "string",
"description": "Specific task"
},
"owner": {
"type": "string",
"description": "Responsible department"
},
"deadline": {
"type": "string",
"description": "Suggested timeframe (e.g., '30 days')"
}
}
}
}
}
}
}
}
]
}
]
}React Interface (Views SDK)
// components/MarketEvaluationDashboard.tsx
import {
JuggernautProvider,
useJuggernautJob,
JobStatus
} from '@juggernautlabs/views';
import {
LineChart,
BarChart,
RadarChart,
ScoreCard,
RiskMatrix
} from './charts';
interface MarketEvaluationOutputs {
strategicBrief: {
executiveSummary: string;
marketScore: number;
recommendation: string;
confidenceLevel: string;
criticalAssumptions: Array<{
assumption: string;
riskIfWrong: string;
validationMethod: string;
}>;
nextSteps: Array<{
action: string;
owner: string;
deadline: string;
}>;
};
financialModel: {
revenueProjection: Array<{
year: number;
revenue: number;
ebitda: number;
}>;
breakEvenMonth: number;
roi: number;
};
riskProfile: {
overallRisk: string;
riskScore: number;
categories: Array<{
category: string;
severity: number;
mitigation: string;
}>;
};
attractivenessScore: {
score: number;
factors: Array<{
name: string;
contribution: number;
}>;
};
pricingModel: {
recommendedPrice: number;
margin: number;
pricingStrategy: string;
};
}
function EvaluationContent() {
const { jobData, isLoading } = useJuggernautJob<MarketEvaluationOutputs>();
if (isLoading) return <LoadingState />;
const {
strategicBrief,
financialModel,
riskProfile,
attractivenessScore,
pricingModel
} = jobData.outputs;
return (
<div className="evaluation-dashboard">
<header>
<h1>Market Evaluation: {jobData.inputs.geographicMarket}</h1>
<div className="meta">
<JobStatus showCost={true} />
<span className={`confidence ${strategicBrief.confidenceLevel}`}>
Confidence: {strategicBrief.confidenceLevel}
</span>
</div>
</header>
<section className="executive-summary">
<ScoreCard
score={strategicBrief.marketScore}
max={100}
label="Market Opportunity Score"
color={strategicBrief.marketScore > 70 ? 'green' : 'orange'}
/>
<div className="recommendation">
<h2>Recommendation: {strategicBrief.recommendation.replace(/_/g, ' ')}</h2>
<p>{strategicBrief.executiveSummary}</p>
</div>
</section>
<div className="grid">
<section className="financials">
<h3>5-Year Financial Projection</h3>
<LineChart
data={financialModel.revenueProjection}
lines={[
{ key: 'revenue', label: 'Revenue', color: '#2563eb' },
{ key: 'ebitda', label: 'EBITDA', color: '#16a34a' }
]}
/>
<div className="metrics">
<div>
<label>Break-even</label>
<value>Month {financialModel.breakEvenMonth}</value>
</div>
<div>
<label>5-Year ROI</label>
<value>{financialModel.roi}%</value>
</div>
<div>
<label>Strategy</label>
<value>{pricingModel.pricingStrategy}</value>
</div>
</div>
</section>
<section className="risk-analysis">
<h3>Risk Profile</h3>
<RiskMatrix
overallRisk={riskProfile.overallRisk}
categories={riskProfile.categories}
/>
</section>
<section className="attractiveness">
<h3>Attractiveness Breakdown</h3>
<RadarChart
data={attractivenessScore.factors.map(f => ({
axis: f.name,
value: f.contribution
}))}
/>
</section>
</div>
<section className="action-plan">
<h3>Immediate Next Steps</h3>
<table>
<thead>
<tr>
<th>Action</th>
<th>Owner</th>
<th>Timeline</th>
</tr>
</thead>
<tbody>
{strategicBrief.nextSteps.map((step, idx) => (
<tr key={idx}>
<td>{step.action}</td>
<td>{step.owner}</td>
<td>{step.deadline}</td>
</tr>
))}
</tbody>
</table>
</section>
<section className="assumptions">
<h3>Critical Assumptions</h3>
{strategicBrief.criticalAssumptions.map((ass, idx) => (
<div key={idx} className="assumption-card">
<h4>{ass.assumption}</h4>
<p><strong>Risk if wrong:</strong> {ass.riskIfWrong}</p>
<p><strong>Validate by:</strong> {ass.validationMethod}</p>
</div>
))}
</section>
</div>
);
}
// App entry point
export function MarketEvaluationApp({ jobId }: { jobId: string }) {
return (
<JuggernautProvider
jobId={jobId}
apiKey={process.env.REACT_APP_JUGGERNAUT_KEY!}
>
<EvaluationContent />
</JuggernautProvider>
);
}Example 2: Bulk Lead Scoring with Iterative Processing
Scenario: Process 50 sales leads individually, score them, aggregate results.
Process Definition
{
"name": "Lead Scoring Engine",
"description": "Processes lead lists with AI scoring and prioritization",
"defaultModel": "openai-gpt-4",
"inputs": [
{
"name": "leads",
"type": "array",
"description": "Array of lead objects with contact info and notes",
"required": true
},
{
"name": "scoringCriteria",
"type": "string",
"description": "Specific factors to prioritize (e.g., 'budget authority, timeline')",
"required": true
}
],
"pipelines": [
{
"name": "Score Leads",
"description": "Iterative individual lead analysis",
"type": "sync",
"steps": [
{
"name": "Score Individual Lead",
"outputName": "append:scoredLeads",
"type": "Iterative",
"iterations": "@leads.length",
"taskType": "LLM",
"messages": [
{
"type": "system",
"content": "You are a sales analyst. Score leads 0-100 based on provided criteria."
},
{
"type": "user",
"content": "Score this lead ({{SYSTEM:STEPINDEX}} of {{leads.length}}): {{STEPLOOP(leads)}}. Criteria: {{scoringCriteria}}"
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Individual lead scoring result",
"properties": {
"leadId": {
"type": "string",
"description": "Identifier from input lead"
},
"score": {
"type": "number",
"description": "Quality score 0-100"
},
"tier": {
"type": "string",
"description": "Priority bucket: hot, warm, cold"
},
"rationale": {
"type": "string",
"description": "Brief explanation for score"
},
"recommendedAction": {
"type": "string",
"description": "Next step: immediate_call, nurture, or disqualify"
}
}
}
}
}
]
},
{
"name": "Aggregate and Prioritize",
"description": "Compile final report",
"type": "sync",
"steps": [
{
"name": "Generate Summary Report",
"outputName": "scoringReport",
"type": "Standard",
"taskType": "LLM",
"messages": [
{
"type": "user",
"content": "Analyze these {{scoredLeads.length}} scored leads and provide summary: {{scoredLeads}}"
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Aggregate scoring report",
"properties": {
"totalLeads": {
"type": "number",
"description": "Count processed"
},
"averageScore": {
"type": "number",
"description": "Mean score"
},
"distribution": {
"type": "object",
"description": "Count by tier",
"properties": {
"hot": {
"type": "number",
"description": "Count of hot leads"
},
"warm": {
"type": "number",
"description": "Count of warm leads"
},
"cold": {
"type": "number",
"description": "Count of cold leads"
}
}
},
"topPriority": {
"type": "array",
"description": "Top 5 leads requiring immediate action",
"items": {
"type": "object",
"description": "Priority lead summary",
"properties": {
"leadId": {
"type": "string",
"description": "Lead identifier"
},
"score": {
"type": "number",
"description": "Quality score"
},
"contactStrategy": {
"type": "string",
"description": "Recommended approach"
}
}
}
}
}
}
}
}
]
}
]
}Example 3: Conditional Routing for Content Processing
Scenario: Route documents to different processing pipelines based on detected type.
Process Definition
{
"name": "Smart Document Processor",
"description": "Classifies and routes documents to specialized extraction pipelines",
"defaultModel": "google-gemini-2.5-flash",
"inputs": [
{
"name": "document",
"type": "string",
"description": "Document text content or OCR result",
"required": true
},
{
"name": "source",
"type": "string",
"description": "Origin: email, upload, scan",
"required": true
}
],
"pipelines": [
{
"name": "Classification",
"description": "Determine document type",
"type": "sync",
"steps": [
{
"name": "Classify Document",
"outputName": "classification",
"type": "Standard",
"taskType": "LLM",
"messages": [
{
"type": "user",
"content": "Classify this document: {{document}}. Source: {{source}}"
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Classification result",
"properties": {
"documentType": {
"type": "string",
"description": "Detected type: invoice, contract, report, correspondence, other"
},
"confidence": {
"type": "number",
"description": "Classification confidence 0-1"
},
"priority": {
"type": "string",
"description": "Urgency: critical, high, normal, low"
}
}
}
}
}
]
},
{
"name": "Router",
"description": "Route to specialized pipeline",
"type": "conditional",
"conditions": {
"$classification.documentType == invoice": 2,
"$classification.documentType == contract": 3,
"$classification.documentType == report": 4,
"default": 5
}
},
{
"name": "Invoice Processing",
"description": "Extract invoice data",
"type": "sync",
"steps": [
{
"name": "Extract Invoice Data",
"outputName": "extractedData",
"type": "Standard",
"taskType": "LLM",
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Invoice fields",
"properties": {
"vendor": {
"type": "string",
"description": "Vendor name"
},
"amount": {
"type": "number",
"description": "Total amount"
},
"dueDate": {
"type": "string",
"description": "ISO date"
},
"lineItems": {
"type": "array",
"description": "Itemized charges",
"items": {
"type": "object",
"description": "Line item",
"properties": {
"description": {
"type": "string",
"description": "Item description"
},
"amount": {
"type": "number",
"description": "Line amount"
}
}
}
}
}
}
}
}
]
},
{
"name": "Contract Processing",
"description": "Extract contract terms",
"type": "sync",
"steps": [
{
"name": "Extract Contract Terms",
"outputName": "extractedData",
"type": "Standard",
"taskType": "LLM",
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Contract analysis",
"properties": {
"parties": {
"type": "array",
"description": "Contracting entities",
"items": {
"type": "string",
"description": "Party name"
}
},
"effectiveDate": {
"type": "string",
"description": "Contract start"
},
"value": {
"type": "number",
"description": "Contract value"
},
"keyTerms": {
"type": "array",
"description": "Important clauses",
"items": {
"type": "object",
"description": "Contract term",
"properties": {
"clause": {
"type": "string",
"description": "Clause name"
},
"summary": {
"type": "string",
"description": "Plain English explanation"
},
"risk": {
"type": "string",
"description": "Risk level: low, medium, high"
}
}
}
}
}
}
}
}
]
},
{
"name": "Report Processing",
"description": "Summarize report content",
"type": "sync",
"steps": [
{
"name": "Summarize Report",
"outputName": "extractedData",
"type": "Standard",
"taskType": "LLM",
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Report summary",
"properties": {
"title": {
"type": "string",
"description": "Report title"
},
"executiveSummary": {
"type": "string",
"description": "Key findings"
},
"actionItems": {
"type": "array",
"description": "Required actions",
"items": {
"type": "string",
"description": "Action item"
}
}
}
}
}
}
]
},
{
"name": "Generic Processing",
"description": "Standard extraction for unknown types",
"type": "sync",
"steps": [
{
"name": "Generic Extraction",
"outputName": "extractedData",
"type": "Standard",
"taskType": "LLM",
"messages": [
{
"type": "user",
"content": "Extract key information from this document: {{document}}"
}
],
"options": {
"outputFormat": "json",
"outputSettings": {
"type": "object",
"description": "Generic extraction",
"properties": {
"summary": {
"type": "string",
"description": "Brief summary"
},
"entities": {
"type": "array",
"description": "Named entities found",
"items": {
"type": "string",
"description": "Entity name"
}
}
}
}
}
}
]
}
]
}Best Practices Demonstrated
- Schema Discipline: Every example includes complete
descriptionfields for all properties - State Modifiers: Example 2 shows
append:usage for accumulating iterative results - Conditional Logic: Example 3 demonstrates index-based routing with
defaultfallbacks - Type Safety: TypeScript interfaces match JSON schemas exactly
- Cost Awareness: Examples use appropriate model tiers (flash for classification, pro for analysis)