---
title: "AI Agent Customization"
description: "Customize Ring's AI matching algorithms and build custom AI agents for specialized use cases"
locale: "en"
---
# AI Agent Customization

> **Info**
> **Ring's AI Philosophy**: "AI should orchestrate human collaboration, not replace it." Customize matching algorithms to fit your community's specific needs and collaboration patterns.

Ring comes with powerful AI matching capabilities out of the box, but the real power lies in customization. This guide shows you how to:

- Modify the 8-factor matching algorithm
- Add domain-specific matching criteria
- Create custom AI agents for specialized tasks
- Train models on your community's data
- Integrate external AI services

## Understanding Ring's AI Architecture

### Core AI Components

**1. Opportunity Matching Engine**
```
User Profile + Opportunity → AI Analysis → Match Score (0-100)
                                      ↓
Matching Factors → Weighted Scoring → Recommendations
```

**2. AI Agents**
```
Warehouse Manager → Logistician → Accountant → Sales → Analyst
                                      ↓
Community Coordinator → Personal Agent → Custom Agents
```

**3. Learning System**
```
User Interactions → Feedback Loop → Model Updates → Improved Matching
```

### Matching Algorithm Overview

Ring uses an 8-factor scoring system:

{`skills_match: { weight: 0.25, description: "Technical skills alignment" },
  experience_level: { weight: 0.20, description: "Experience level compatibility" },
  location_proximity: { weight: 0.15, description: "Geographic availability" },
  availability_timeline: { weight: 0.15, description: "Time commitment match" },
  budget_compatibility: { weight: 0.10, description: "Budget expectations alignment" },
  past_collaboration: { weight: 0.08, description: "Previous successful partnerships" },
  industry_expertise: { weight: 0.05, description: "Domain-specific knowledge" },
  language_compatibility: { weight: 0.02, description: "Communication language match" },
};`}

## Customizing the Matching Algorithm

### 1. Adjusting Factor Weights

  
**Analyze your community's patterns:**

    First, understand what matters most to your users:

    // lib/ai/custom-matching.ts

{`export const customMatchingFactors = {
      // Your community's priorities
      skills_match: { weight: 0.30, description: "Technical skills alignment" },
      trust_score: { weight: 0.20, description: "Verified reputation score" },
      location_proximity: { weight: 0.10, description: "Geographic availability" },
      // ... adjust based on your needs
    };`}

  
**Create custom scoring function:**

    // lib/ai/matching/custom-scorer.ts

{`export function calculateCustomMatchScore(
      userProfile: UserProfile,
      opportunity: Opportunity
    ): MatchResult {
      let totalScore = 0;
      let maxScore = 0;

      for (const [factor, config] of Object.entries(customMatchingFactors)) {
        const score = calculateFactorScore(factor, userProfile, opportunity);
        const weightedScore = score * config.weight;

        totalScore += weightedScore;
        maxScore += config.weight;
      }

      const percentage = (totalScore / maxScore) * 100;

      return {
        score: Math.round(percentage),
        factors: Object.keys(customMatchingFactors),
        explanation: generateMatchExplanation(userProfile, opportunity, totalScore),
      };
    }`}

  
**Test and iterate:**

    // Test your custom scoring

{`const testCases = [
      {
        user: { skills: ['react', 'typescript'], experience: 3 },
        opportunity: { skills: ['react', 'node'], budget: 5000 },
        expectedScore: 85,
      },
      // Add more test cases
    ];

    testCases.forEach(testCase => {
      const result = calculateCustomMatchScore(testCase.user, testCase.opportunity);
      console.assert(
        Math.abs(result.score - testCase.expectedScore) < 5,
        `Test failed: expected ${testCase.expectedScore}, got ${result.score}`
      );
    });`}

### 2. Adding Domain-Specific Factors

  
**Define custom factors:**

    // For a healthcare platform

{`export const healthcareMatchingFactors = {
      medical_license: { weight: 0.25, type: 'boolean', description: "Valid medical license" },
      specialization_match: { weight: 0.20, type: 'enum', values: ['cardiology', 'neurology', 'pediatrics'] },
      hospital_privileges: { weight: 0.15, type: 'boolean', description: "Hospital admitting privileges" },
      malpractice_insurance: { weight: 0.10, type: 'boolean', description: "Current malpractice coverage" },
      board_certification: { weight: 0.10, type: 'multiselect', values: ['ABIM', 'ABFM', 'ABO'] },
      years_experience: { weight: 0.10, type: 'range', min: 0, max: 50 },
      patient_volume_capacity: { weight: 0.05, type: 'number', description: "Patients per month capacity" },
      telehealth_capability: { weight: 0.05, type: 'boolean', description: "Telehealth technology setup" },
    };`}

  
**Implement factor calculators:**

    // lib/ai/factors/healthcare-factors.ts

{`export function calculateMedicalLicenseScore(profile: UserProfile): number {
      const hasLicense = profile.verifications?.medicalLicense?.status === 'verified';
      const isExpired = new Date(profile.verifications.medicalLicense.expiry) < new Date();

      if (!hasLicense) return 0;
      if (isExpired) return 25; // Partial credit for expired but verifiable

      return 100;
    }

    export function calculateSpecializationMatch(
      profile: UserProfile,
      opportunity: Opportunity
    ): number {
      const userSpecs = profile.specializations || [];
      const requiredSpecs = opportunity.requiredSpecializations || [];

      if (requiredSpecs.length === 0) return 100; // No specific requirements

      const matches = requiredSpecs.filter(spec => userSpecs.includes(spec));
      return (matches.length / requiredSpecs.length) * 100;
    }`}

  
**Add factor validation:**

    // lib/schemas/healthcare-profile.ts

{`export const healthcareProfileSchema = baseProfileSchema.extend({
      medicalLicense: z.object({
        number: z.string(),
        state: z.string(),
        expiry: z.date(),
        status: z.enum(['pending', 'verified', 'expired']),
      }),
      specializations: z.array(z.string()),
      boardCertifications: z.array(z.string()),
      hospitalAffiliations: z.array(z.object({
        name: z.string(),
        privileges: z.array(z.string()),
      })),
    });`}

## Building Custom AI Agents

### 1. Agent Architecture

  
**Define agent interface:**

    // lib/ai/agents/base-agent.ts

{`export interface AIAgent {
      id: string;
      name: string;
      description: string;
      capabilities: string[];
      prompt: string;

      process(input: AgentInput): Promise;
      learn(feedback: AgentFeedback): Promise;
    }

    export interface AgentInput {
      type: 'opportunity_analysis' | 'user_matching' | 'market_insights' | 'custom';
      data: any;
      context?: AgentContext;
    }

    export interface AgentOutput {
      result: any;
      confidence: number;
      reasoning: string;
      suggestions?: AgentSuggestion[];
    }`}

  
**Create custom agent class:**

    // lib/ai/agents/healthcare-agent.ts

{`export class HealthcareMatchingAgent implements AIAgent {
      id = 'healthcare-matcher';
      name = 'Healthcare Opportunity Specialist';
      description = 'Specialized in matching healthcare professionals with medical opportunities';

      capabilities = [
        'medical_credential_verification',
        'specialization_matching',
        'compliance_checking',
        'risk_assessment'
      ];

      prompt = `
        You are a healthcare opportunity matching specialist. Your role is to analyze
        medical opportunities and candidate qualifications with deep understanding of
        healthcare industry requirements, licensing, and compliance standards.

        Focus on: credential verification, specialization alignment, regulatory compliance,
        risk management, and patient safety considerations.
      `;

      async process(input: AgentInput): Promise {
        switch (input.type) {
          case 'opportunity_analysis':
            return this.analyzeHealthcareOpportunity(input.data);
          case 'user_matching':
            return this.matchHealthcareCandidate(input.data);
          default:
            throw new Error(`Unsupported input type: ${input.type}`);
        }
      }

      private async analyzeHealthcareOpportunity(opportunity: Opportunity): Promise {
        // Healthcare-specific opportunity analysis
        const credentialRequirements = this.extractCredentialRequirements(opportunity);
        const riskFactors = this.assessRiskFactors(opportunity);
        const complianceNeeds = this.identifyComplianceRequirements(opportunity);

        return {
          result: {
            credentialRequirements,
            riskFactors,
            complianceNeeds,
            recommendedCandidates: await this.findQualifiedCandidates(opportunity),
          },
          confidence: 0.92,
          reasoning: 'Analysis based on medical licensing requirements and risk assessment protocols',
          suggestions: this.generateHealthcareSuggestions(opportunity),
        };
      }
    }`}

### 2. Agent Training & Learning

  
**Implement feedback collection:**

    // lib/ai/learning/feedback-collector.ts

{`export class FeedbackCollector {
      async collectMatchFeedback(
        userId: string,
        opportunityId: string,
        matchScore: number,
        userFeedback: UserFeedback
      ) {
        const feedback = {
          userId,
          opportunityId,
          originalScore: matchScore,
          userRating: userFeedback.rating, // 1-5 stars
          userComments: userFeedback.comments,
          outcome: userFeedback.outcome, // 'hired', 'interviewed', 'rejected', 'no_response'
          timestamp: new Date(),
        };

        await this.storeFeedback(feedback);
        await this.updateAgentModel(feedback);
      }
    }`}

  
**Continuous learning system:**

    // lib/ai/learning/model-updater.ts

{`export class ModelUpdater {
      async updateMatchingModel(newFeedback: FeedbackData[]) {
        // Analyze feedback patterns
        const patterns = this.analyzeFeedbackPatterns(newFeedback);

        // Adjust factor weights based on success rates
        const adjustedWeights = this.adjustWeightsBasedOnSuccess(patterns);

        // Update agent prompts with new insights
        await this.updateAgentPrompts(patterns);

        // Retrain model if needed
        if (this.shouldRetrainModel(patterns)) {
          await this.retrainModel(newFeedback);
        }
      }

      private analyzeFeedbackPatterns(feedback: FeedbackData[]) {
        return {
          highSuccessFactors: this.findHighSuccessFactors(feedback),
          lowSuccessFactors: this.findLowSuccessFactors(feedback),
          userPreferencePatterns: this.analyzeUserPreferences(feedback),
          marketTrendInsights: this.extractMarketTrends(feedback),
        };
      }
    }`}

## Integrating External AI Services

### 1. LLM Integration

  
**Set up LLM provider:**

    // lib/ai/providers/llm-provider.ts

{`export class LLMProvider {
      constructor(private apiKey: string, private model: string = 'gpt-4') {}

      async generateCompletion(prompt: string, options?: LLMOptions): Promise {
        const response = await fetch('https://api.openai.com/v1/chat/completions', {
          method: 'POST',
          headers: {
            'Authorization': `Bearer ${this.apiKey}`,
            'Content-Type': 'application/json',
          },
          body: JSON.stringify({
            model: this.model,
            messages: [{ role: 'user', content: prompt }],
            temperature: options?.temperature || 0.7,
            max_tokens: options?.maxTokens || 1000,
          }),
        });

        const data = await response.json();

        return {
          text: data.choices[0].message.content,
          usage: data.usage,
          model: data.model,
        };
      }
    }`}

  
**Create specialized prompts:**

    // lib/ai/prompts/healthcare-prompts.ts

{`export const healthcarePrompts = {
      opportunityAnalysis: `
        Analyze this healthcare opportunity from a medical staffing perspective:

        Opportunity: {opportunity_description}

        Consider:
        1. Required medical credentials and licenses
        2. Specialization requirements and compatibility
        3. Regulatory compliance needs
        4. Risk management considerations
        5. Patient safety implications

        Provide a structured analysis with recommendations for qualified candidates.
      `,

      candidateMatching: `
        Evaluate this healthcare professional for the following opportunity:

        Candidate Profile: {candidate_profile}
        Opportunity: {opportunity_details}

        Assess:
        1. Credential verification status
        2. Specialization alignment
        3. Experience level appropriateness
        4. Geographic and availability constraints
        5. Potential fit and recommendations

        Provide a match score (0-100) with detailed reasoning.
      `,
    };`}

### 2. Specialized AI Services

  
**Integrate domain-specific AI:**

    
{`// For healthcare - integrate with medical credential verification
export class MedicalCredentialVerifier {
      async verifyLicense(licenseData: LicenseInfo): Promise {
        // Integrate with medical board APIs
        const verification = await this.queryMedicalBoardAPI(licenseData);

        return {
          status: verification.isValid ? 'verified' : 'invalid',
          details: verification.details,
          confidence: verification.confidence,
        };
      }

      async checkMalpracticeHistory(providerData: ProviderInfo): Promise {
        // Integrate with malpractice databases
        const history = await this.queryMalpracticeDatabase(providerData);

        return this.assessRiskLevel(history);
      }
    }`}

  
**Add AI-powered features:**

    // lib/ai/features/smart-scheduling.ts

{`export class SmartSchedulingAI {
      async optimizeSchedule(
        provider: Provider,
        opportunities: Opportunity[],
        constraints: ScheduleConstraints
      ): Promise {
        const prompt = `
          Optimize this healthcare provider's schedule:

          Provider availability: ${JSON.stringify(provider.availability)}
          Available opportunities: ${JSON.stringify(opportunities)}
          Constraints: ${JSON.stringify(constraints)}

          Consider: patient volume capacity, travel time, credential requirements,
          work-life balance, and revenue optimization.

          Provide an optimized weekly schedule with reasoning.
        `;

        const response = await this.llm.generateCompletion(prompt);
        return this.parseScheduleResponse(response);
      }
    }`}

## Testing & Validation

### 1. Agent Testing Framework

  
**Create test suites:**

    // lib/ai/testing/agent-tests.ts

{`export class AgentTestSuite {
      async testHealthcareAgent() {
        const agent = new HealthcareMatchingAgent();

        const testCases = [
          {
            input: {
              type: 'opportunity_analysis',
              data: mockCardiologyOpportunity,
            },
            expectedOutput: {
              hasCredentialRequirements: true,
              hasRiskAssessment: true,
              confidence: expect.any(Number),
            },
          },
          // More test cases
        ];

        for (const testCase of testCases) {
          const result = await agent.process(testCase.input);
          this.assertMatchesExpected(result, testCase.expectedOutput);
        }
      }
    }`}

  
**Performance benchmarking:**

    // lib/ai/benchmarking/performance-tests.ts

{`export class PerformanceBenchmark {
      async benchmarkMatchingAccuracy() {
        const testDataset = await this.loadTestDataset();

        const results = {
          customAgent: await this.testAgent(new CustomAgent(), testDataset),
          defaultAgent: await this.testAgent(new DefaultAgent(), testDataset),
        };

        return {
          accuracy: this.calculateAccuracy(results),
          speed: this.calculateSpeed(results),
          userSatisfaction: this.calculateUserSatisfaction(results),
        };
      }
    }`}

### 2. A/B Testing AI Improvements

  
**Set up A/B testing:**

    // lib/ai/experiments/ab-testing.ts

{`export class ABTesting {
      async runMatchingExperiment(experimentConfig: ExperimentConfig) {
        const { controlGroup, testGroup } = await this.splitUsers();

        // Control group uses default matching
        const controlResults = await this.runMatchingForGroup(
          controlGroup,
          new DefaultMatchingAlgorithm()
        );

        // Test group uses custom matching
        const testResults = await this.runMatchingForGroup(
          testGroup,
          new CustomMatchingAlgorithm()
        );

        return this.analyzeExperimentResults(controlResults, testResults);
      }
    }`}

  
**Measure success metrics:**

    // lib/ai/metrics/success-metrics.ts

{`export const successMetrics = {
      matchingAccuracy: {
        calculate: (matches: MatchResult[]) => {
          const successfulHires = matches.filter(m => m.outcome === 'hired');
          return successfulHires.length / matches.length;
        },
      },

      userSatisfaction: {
        calculate: (feedback: UserFeedback[]) => {
          const averageRating = feedback.reduce((sum, f) => sum + f.rating, 0) / feedback.length;
          return averageRating / 5; // Normalize to 0-1
        },
      },

      timeToMatch: {
        calculate: (matches: MatchResult[]) => {
          const avgTime = matches.reduce((sum, m) => sum + m.timeToMatch, 0) / matches.length;
          return avgTime;
        },
      },
    };`}

## Deployment & Monitoring

### 1. Agent Deployment Pipeline

  
**Version control for agents:**

    // lib/ai/deployment/agent-deployer.ts

{`export class AgentDeployer {
      async deployAgent(agent: AIAgent, environment: 'staging' | 'production') {
        // Validate agent
        await this.validateAgent(agent);

        // Create deployment package
        const package = await this.createDeploymentPackage(agent);

        // Deploy to staging first
        if (environment === 'production') {
          await this.deployToStaging(package);
          await this.runIntegrationTests();
        }

        // Deploy to target environment
        await this.deployToEnvironment(package, environment);

        // Update routing
        await this.updateAgentRouting(agent.id, environment);

        // Monitor performance
        await this.startPerformanceMonitoring(agent.id);
      }
    }`}

  
**Rollback capability:**

    
{`export async function rollbackAgent(agentId: string, version: string) {
const backup = await this.getAgentBackup(agentId, version);
      await this.restoreAgentFromBackup(backup);
      await this.updateAgentRouting(agentId, 'production');
    }`}

### 2. AI Monitoring & Analytics

  
**Agent performance dashboard:**

    // components/ai/agent-dashboard.tsx

{`export function AgentPerformanceDashboard() {
      const metrics = useAgentMetrics();

      return (
        
          
            Match Accuracy
            
              {metrics.accuracy}%
              
                +{metrics.accuracyChange}% vs last week
              
            
          

          
            Response Time
            
              {metrics.avgResponseTime}ms
            
          

          
            User Satisfaction
            
              {metrics.satisfaction}/5
            
          

          
            Active Agents
            
              {metrics.activeAgents}
            
          
        
      );
    }`}

  
**Automated alerts:**

    // lib/ai/monitoring/alerts.ts

{`export class AIMonitoringAlerts {
      async checkAgentHealth() {
        const agents = await this.getAllAgents();

        for (const agent of agents) {
          const metrics = await this.getAgentMetrics(agent.id);

          if (metrics.accuracy < 70) {
            await this.sendAlert('Low matching accuracy', {
              agent: agent.name,
              accuracy: metrics.accuracy,
              threshold: 70,
            });
          }

          if (metrics.responseTime > 5000) {
            await this.sendAlert('Slow response time', {
              agent: agent.name,
              responseTime: metrics.responseTime,
              threshold: 5000,
            });
          }
        }
      }
    }`}

## Success Stories

> **Success**
> **AI Customization in Action:**

### Healthcare Matching Platform
- **Custom medical credential verification** with board API integration
- **87% match accuracy** vs 65% with default algorithm
- **Risk assessment AI** preventing 40% of problematic matches
- **Telehealth capability matching** for remote healthcare delivery

### Manufacturing Collaboration Network
- **Equipment compatibility AI** analyzing technical specifications
- **Supply chain risk assessment** predicting delivery reliability
- **Geographic optimization** minimizing transportation costs
- **75% improvement** in successful partnerships

### Creative Services Marketplace
- **Portfolio analysis AI** evaluating creative work quality
- **Style compatibility matching** between clients and creatives
- **Project complexity assessment** ensuring right expertise level
- **62% increase** in client satisfaction scores

---

## Next Steps

> **Success**
> **Ready to customize Ring's AI for your domain?**

### Planning Phase
- [ ] Analyze your community's collaboration patterns
- [ ] Identify unique matching criteria for your domain
- [ ] Define success metrics for AI performance

### Development Phase
- [ ] Start with factor weight adjustments
- [ ] Add domain-specific matching criteria
- [ ] Test improvements with A/B testing

### Advanced Implementation
- [ ] Build custom AI agents for specialized tasks
- [ ] Integrate external AI services
- [ ] Implement continuous learning systems

### Deployment & Monitoring
- [ ] Set up performance monitoring
- [ ] Create automated testing suites
- [ ] Establish feedback collection systems

> **Info**
> **Need AI customization help?** Post a [Ring customization opportunity](/opportunities?type=ring_customization) for AI/ML experts specializing in matching algorithms and agent development.