@spaik/mcp-server-roi
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MCP server for AI ROI prediction and tracking with Monte Carlo simulations
806 lines • 33.7 kB
JavaScript
import { z } from 'zod';
import { createLogger } from '../utils/logger.js';
/**
* Quality Assurance Service
*
* Ensures response quality, validates insights, and maintains
* consistency across all AI-optimized outputs.
*/
// QA schemas
export const QualityMetricsSchema = z.object({
accuracy: z.number().min(0).max(1),
completeness: z.number().min(0).max(1),
clarity: z.number().min(0).max(1),
relevance: z.number().min(0).max(1),
consistency: z.number().min(0).max(1),
overall_score: z.number().min(0).max(1)
});
export const QualityIssueSchema = z.object({
severity: z.enum(['critical', 'high', 'medium', 'low']),
category: z.enum(['accuracy', 'completeness', 'clarity', 'consistency', 'logic']),
description: z.string(),
location: z.string().optional(),
suggested_fix: z.string().optional()
});
export const QualityReportSchema = z.object({
metrics: QualityMetricsSchema,
issues: z.array(QualityIssueSchema),
passed: z.boolean(),
confidence: z.number().min(0).max(1),
recommendations: z.array(z.string()),
validation_rules_applied: z.number(),
auto_corrections: z.array(z.object({
issue: z.string(),
correction: z.string()
}))
});
export class QualityAssurance {
logger = createLogger({ component: 'QualityAssurance' });
// Quality thresholds
QUALITY_THRESHOLDS = {
minimum_overall: 0.7,
critical_metric_minimum: 0.6,
high_quality_target: 0.85
};
// Validation rules
VALIDATION_RULES = {
response_structure: [
{ field: 'executive_summary', required: true },
{ field: 'insights', required: true },
{ field: 'recommendations', required: true },
{ field: 'metadata', required: true }
],
numeric_constraints: {
roi_range: { min: -100, max: 10000 },
payback_months_range: { min: 1, max: 120 },
confidence_range: { min: 0, max: 1 },
probability_sum: 1.0 // For scenarios
},
logical_rules: [
'negative_roi_requires_justification',
'high_roi_requires_evidence',
'payback_exceeds_timeline_warning',
'risk_count_matches_mitigation_count'
]
};
/**
* Validate response quality
*/
async validateResponse(response, context) {
this.logger.debug('Validating response quality', { tool: context?.tool });
// Run all validation checks
const structureValid = this.validateStructure(response);
const dataValid = this.validateDataIntegrity(response);
const logicValid = this.validateBusinessLogic(response, context);
const consistencyValid = this.validateConsistency(response);
// Calculate quality metrics
const metrics = this.calculateQualityMetrics(response, structureValid, dataValid, logicValid, consistencyValid);
// Identify issues
const issues = this.collectIssues(structureValid, dataValid, logicValid, consistencyValid);
// Apply auto-corrections where possible
const autoCorrections = await this.applyAutoCorrections(response, issues);
// Generate recommendations
const recommendations = this.generateQualityRecommendations(metrics, issues);
// Determine if response passes quality checks
const passed = this.determineQualityPass(metrics, issues);
return {
metrics,
issues,
passed,
confidence: this.calculateConfidence(metrics, issues),
recommendations,
validation_rules_applied: this.countAppliedRules(),
auto_corrections: autoCorrections
};
}
/**
* Ensure consistency across responses
*/
async ensureConsistency(responses, crossCheckType) {
this.logger.debug('Checking consistency across responses', {
count: responses.length,
type: crossCheckType
});
const inconsistencies = [];
switch (crossCheckType) {
case 'temporal':
inconsistencies.push(...this.checkTemporalConsistency(responses));
break;
case 'logical':
inconsistencies.push(...this.checkLogicalConsistency(responses));
break;
case 'semantic':
inconsistencies.push(...this.checkSemanticConsistency(responses));
break;
}
const alignmentScore = this.calculateAlignmentScore(responses, inconsistencies);
return {
consistent: inconsistencies.length === 0,
inconsistencies,
alignment_score: alignmentScore
};
}
/**
* Validate insights quality
*/
async validateInsights(insights, sourceData) {
this.logger.debug('Validating insights quality');
const issues = [];
const qualityScores = {};
// Validate primary insights
if (insights.primary) {
insights.primary.forEach((insight, idx) => {
const score = this.scoreInsightQuality(insight, sourceData);
qualityScores[`primary_${idx}`] = score;
if (score < 0.6) {
issues.push(`Weak primary insight at index ${idx}: lacks supporting evidence`);
}
});
}
// Validate risk insights
if (insights.risks) {
insights.risks.forEach((risk, idx) => {
const score = this.scoreRiskValidity(risk, sourceData);
qualityScores[`risk_${idx}`] = score;
if (score < 0.5) {
issues.push(`Questionable risk at index ${idx}: may be overstated`);
}
});
}
// Validate opportunities
if (insights.opportunities) {
insights.opportunities.forEach((opp, idx) => {
const score = this.scoreOpportunityFeasibility(opp, sourceData);
qualityScores[`opportunity_${idx}`] = score;
if (score < 0.5) {
issues.push(`Unrealistic opportunity at index ${idx}`);
}
});
}
// Calculate evidence strength
const evidenceStrength = this.calculateEvidenceStrength(insights, sourceData);
return {
valid: issues.length === 0,
issues,
insight_quality_scores: qualityScores,
evidence_strength: evidenceStrength
};
}
/**
* Perform semantic validation
*/
async validateSemantics(response) {
this.logger.debug('Performing semantic validation');
const semanticIssues = [];
// Check for contradictions
const contradictions = this.findContradictions(response);
semanticIssues.push(...contradictions.map(c => ({
type: 'contradiction',
description: c,
severity: 0.8
})));
// Check for ambiguities
const ambiguities = this.findAmbiguities(response);
semanticIssues.push(...ambiguities.map(a => ({
type: 'ambiguity',
description: a,
severity: 0.5
})));
// Check for redundancies
const redundancies = this.findRedundancies(response);
semanticIssues.push(...redundancies.map(r => ({
type: 'redundancy',
description: r,
severity: 0.3
})));
// Check for gaps
const gaps = this.findSemanticGaps(response);
semanticIssues.push(...gaps.map(g => ({
type: 'gap',
description: g,
severity: 0.6
})));
// Calculate readability
const readabilityScore = this.calculateReadability(response);
return {
coherent: semanticIssues.filter(i => i.severity > 0.5).length === 0,
semantic_issues: semanticIssues,
readability_score: readabilityScore
};
}
/**
* Auto-improve response quality
*/
async autoImproveResponse(response, qualityReport) {
this.logger.debug('Auto-improving response quality');
let improvedResponse = JSON.parse(JSON.stringify(response));
const improvements = [];
// Fix critical issues first
const criticalIssues = qualityReport.issues.filter(i => i.severity === 'critical');
for (const issue of criticalIssues) {
const improvement = await this.fixIssue(improvedResponse, issue);
if (improvement.fixed) {
improvements.push(improvement.description);
improvedResponse = improvement.response;
}
}
// Enhance weak areas
if (qualityReport.metrics.clarity < 0.7) {
improvedResponse = this.enhanceClarity(improvedResponse);
improvements.push('Enhanced clarity of key messages');
}
if (qualityReport.metrics.completeness < 0.8) {
improvedResponse = await this.enhanceCompleteness(improvedResponse);
improvements.push('Added missing context and details');
}
// Re-validate to measure improvement
const newReport = await this.validateResponse(improvedResponse);
const qualityDelta = newReport.metrics.overall_score - qualityReport.metrics.overall_score;
return {
improved_response: improvedResponse,
improvements_made: improvements,
quality_delta: qualityDelta
};
}
// Private validation methods
validateStructure(response) {
const issues = [];
// Check required fields
this.VALIDATION_RULES.response_structure.forEach(rule => {
if (rule.required && !response[rule.field]) {
issues.push(`Missing required field: ${rule.field}`);
}
});
// Check field types
if (response.executive_summary && typeof response.executive_summary !== 'object') {
issues.push('Executive summary must be an object');
}
if (response.insights && typeof response.insights !== 'object') {
issues.push('Insights must be an object');
}
return { valid: issues.length === 0, issues };
}
validateDataIntegrity(response) {
const issues = [];
// Validate numeric ranges
if (response.summary?.expected_roi !== undefined) {
const roi = response.summary.expected_roi;
const { min, max } = this.VALIDATION_RULES.numeric_constraints.roi_range;
if (roi < min || roi > max) {
issues.push(`ROI ${roi}% outside valid range [${min}, ${max}]`);
}
}
if (response.summary?.payback_period_months !== undefined) {
const payback = response.summary.payback_period_months;
const { min, max } = this.VALIDATION_RULES.numeric_constraints.payback_months_range;
if (payback < min || payback > max) {
issues.push(`Payback period ${payback} months outside valid range [${min}, ${max}]`);
}
}
// Validate probabilities sum to 1
if (response.scenarios) {
const probabilitySum = response.scenarios.reduce((sum, s) => sum + (s.probability || 0), 0);
if (Math.abs(probabilitySum - 1) > 0.01) {
issues.push(`Scenario probabilities sum to ${probabilitySum}, should be 1.0`);
}
}
return { valid: issues.length === 0, issues };
}
validateBusinessLogic(response, context) {
const issues = [];
// Apply logical rules
if (response.summary?.expected_roi < 0 && !response.justification) {
issues.push('Negative ROI requires justification');
}
if (response.summary?.expected_roi > 500 &&
(!response.evidence || response.evidence.length < 3)) {
issues.push('High ROI claims require supporting evidence');
}
if (response.summary?.payback_period_months > (response.timeline_months || 24)) {
issues.push('Payback period exceeds project timeline');
}
if (response.insights?.risks && response.recommendations?.mitigations) {
if (response.insights.risks.length !== response.recommendations.mitigations.length) {
issues.push('Each risk should have a corresponding mitigation strategy');
}
}
return { valid: issues.length === 0, issues };
}
validateConsistency(response) {
const issues = [];
// Check internal consistency
if (response.executive_summary?.confidence === 'high' &&
response.metadata?.confidence_score < 0.7) {
issues.push('Executive confidence level inconsistent with confidence score');
}
if (response.summary?.expected_roi > 200 &&
response.executive_summary?.confidence === 'low') {
issues.push('High ROI with low confidence is inconsistent');
}
// Check narrative consistency
if (response.narrative?.tone === 'optimistic' &&
response.insights?.risks?.length > 5) {
issues.push('Optimistic tone inconsistent with high risk count');
}
return { valid: issues.length === 0, issues };
}
calculateQualityMetrics(response, structureValid, dataValid, logicValid, consistencyValid) {
// Accuracy based on data validation
const accuracy = dataValid.valid ? 1.0 :
Math.max(0.3, 1 - (dataValid.issues.length * 0.1));
// Completeness based on structure
const requiredFields = this.VALIDATION_RULES.response_structure.filter(r => r.required);
const presentFields = requiredFields.filter(r => response[r.field]).length;
const completeness = presentFields / requiredFields.length;
// Clarity based on structure and semantics
const hasExecSummary = response.executive_summary?.headline ? 0.3 : 0;
const hasKeyInsight = response.executive_summary?.key_insight ? 0.3 : 0;
const hasRecommendations = response.recommendations?.next_action ? 0.4 : 0;
const clarity = hasExecSummary + hasKeyInsight + hasRecommendations;
// Relevance (simplified - would need context in real implementation)
const relevance = response.insights?.primary?.length > 0 ? 0.9 : 0.5;
// Consistency
const consistency = consistencyValid.valid ? 1.0 :
Math.max(0.4, 1 - (consistencyValid.issues.length * 0.15));
// Overall score
const overall = (accuracy * 0.25 + completeness * 0.2 +
clarity * 0.2 + relevance * 0.15 + consistency * 0.2);
return {
accuracy,
completeness,
clarity,
relevance,
consistency,
overall_score: overall
};
}
collectIssues(structureValid, dataValid, logicValid, consistencyValid) {
const issues = [];
// Structure issues
structureValid.issues.forEach((issue) => {
issues.push({
severity: issue.includes('required') ? 'critical' : 'high',
category: 'completeness',
description: issue,
suggested_fix: `Add missing ${issue.split(':')[1]?.trim()} to response`
});
});
// Data issues
dataValid.issues.forEach((issue) => {
issues.push({
severity: issue.includes('range') ? 'high' : 'medium',
category: 'accuracy',
description: issue,
suggested_fix: 'Verify and correct the data values'
});
});
// Logic issues
logicValid.issues.forEach((issue) => {
issues.push({
severity: issue.includes('requires') ? 'high' : 'medium',
category: 'logic',
description: issue,
suggested_fix: this.getSuggestedFix(issue)
});
});
// Consistency issues
consistencyValid.issues.forEach((issue) => {
issues.push({
severity: 'medium',
category: 'consistency',
description: issue,
suggested_fix: 'Align all related fields for consistency'
});
});
return issues;
}
async applyAutoCorrections(response, issues) {
const corrections = [];
// Auto-fix probability sum
const probSumIssue = issues.find(i => i.description.includes('probabilities sum'));
if (probSumIssue && response.scenarios) {
const sum = response.scenarios.reduce((s, sc) => s + sc.probability, 0);
response.scenarios.forEach((sc) => {
sc.probability = sc.probability / sum;
});
corrections.push({
issue: 'Probability sum normalization',
correction: 'Normalized scenario probabilities to sum to 1.0'
});
}
// Add missing confidence score
if (!response.metadata?.confidence_score && response.executive_summary?.confidence) {
const confidenceMap = { high: 0.85, medium: 0.7, low: 0.5 };
response.metadata = response.metadata || {};
response.metadata.confidence_score = confidenceMap[response.executive_summary.confidence] || 0.7;
corrections.push({
issue: 'Missing confidence score',
correction: 'Added confidence score based on executive summary'
});
}
return corrections;
}
generateQualityRecommendations(metrics, issues) {
const recommendations = [];
if (metrics.accuracy < 0.8) {
recommendations.push('Verify all numeric calculations and data sources');
}
if (metrics.completeness < 0.8) {
recommendations.push('Add missing sections to provide complete analysis');
}
if (metrics.clarity < 0.7) {
recommendations.push('Simplify language and add executive summary');
}
if (issues.filter(i => i.severity === 'critical').length > 0) {
recommendations.push('Address critical issues before using this response');
}
if (metrics.overall_score > 0.85) {
recommendations.push('Response meets high quality standards');
}
return recommendations;
}
determineQualityPass(metrics, issues) {
// Fail if any critical issues
if (issues.some(i => i.severity === 'critical')) {
return false;
}
// Fail if overall score too low
if (metrics.overall_score < this.QUALITY_THRESHOLDS.minimum_overall) {
return false;
}
// Fail if any metric critically low
const metricValues = Object.values(metrics).filter(v => typeof v === 'number');
if (metricValues.some(v => v < this.QUALITY_THRESHOLDS.critical_metric_minimum)) {
return false;
}
return true;
}
calculateConfidence(metrics, issues) {
let confidence = metrics.overall_score;
// Reduce confidence for issues
const issuePenalty = issues.reduce((penalty, issue) => {
switch (issue.severity) {
case 'critical': return penalty + 0.2;
case 'high': return penalty + 0.1;
case 'medium': return penalty + 0.05;
case 'low': return penalty + 0.02;
}
}, 0);
confidence = Math.max(0.1, confidence - issuePenalty);
// Boost confidence for high quality
if (metrics.overall_score > this.QUALITY_THRESHOLDS.high_quality_target) {
confidence = Math.min(0.95, confidence * 1.1);
}
return confidence;
}
countAppliedRules() {
return this.VALIDATION_RULES.response_structure.length +
Object.keys(this.VALIDATION_RULES.numeric_constraints).length +
this.VALIDATION_RULES.logical_rules.length;
}
// Consistency checking methods
checkTemporalConsistency(responses) {
const inconsistencies = [];
// Check if metrics improve or degrade logically over time
for (let i = 1; i < responses.length; i++) {
const prev = responses[i - 1];
const curr = responses[i];
if (prev.summary?.expected_roi && curr.summary?.expected_roi) {
const roiDelta = Math.abs(curr.summary.expected_roi - prev.summary.expected_roi);
if (roiDelta > 50 && !curr.change_justification) {
inconsistencies.push({
type: 'temporal_metric_jump',
description: `ROI changed by ${roiDelta}% without justification`,
affected_responses: [i - 1, i],
severity: 'high'
});
}
}
}
return inconsistencies;
}
checkLogicalConsistency(responses) {
const inconsistencies = [];
responses.forEach((response, idx) => {
// Check if high ROI aligns with low risk
if (response.summary?.expected_roi > 200 &&
response.insights?.risks?.length < 2) {
inconsistencies.push({
type: 'risk_return_mismatch',
description: 'High ROI with unrealistically low risk profile',
affected_responses: [idx],
severity: 'medium'
});
}
});
return inconsistencies;
}
checkSemanticConsistency(responses) {
const inconsistencies = [];
// Check if terminology is used consistently
const termUsage = new Map();
responses.forEach((response, idx) => {
const terms = this.extractKeyTerms(response);
terms.forEach(term => {
if (!termUsage.has(term)) {
termUsage.set(term, new Set());
}
termUsage.get(term).add(idx);
});
});
// Flag terms that appear in some but not all relevant responses
termUsage.forEach((indices, term) => {
if (indices.size < responses.length * 0.5 && indices.size > 0) {
inconsistencies.push({
type: 'terminology_inconsistency',
description: `Term "${term}" used inconsistently across responses`,
affected_responses: Array.from(indices),
severity: 'low'
});
}
});
return inconsistencies;
}
calculateAlignmentScore(responses, inconsistencies) {
const baseScore = 1.0;
const penalty = inconsistencies.reduce((sum, inc) => {
switch (inc.severity) {
case 'high': return sum + 0.15;
case 'medium': return sum + 0.08;
case 'low': return sum + 0.03;
default: return sum;
}
}, 0);
return Math.max(0, baseScore - penalty);
}
// Insight validation methods
scoreInsightQuality(insight, sourceData) {
let score = 0.5; // Base score
// Check if insight is specific (contains numbers)
if (/\d+/.test(insight))
score += 0.2;
// Check if insight references source data
if (sourceData.summary && insight.toLowerCase().includes('roi'))
score += 0.1;
if (sourceData.summary && insight.toLowerCase().includes('payback'))
score += 0.1;
// Check if insight is actionable
const actionableWords = ['should', 'could', 'recommend', 'consider', 'implement'];
if (actionableWords.some(word => insight.toLowerCase().includes(word))) {
score += 0.1;
}
return Math.min(1, score);
}
scoreRiskValidity(risk, sourceData) {
let score = 0.5;
// Check if risk is specific
if (risk.split(' ').length > 5)
score += 0.2;
// Check if risk aligns with project characteristics
if (sourceData.use_cases?.length > 5 && risk.toLowerCase().includes('complex')) {
score += 0.2;
}
// Penalize generic risks
const genericRisks = ['implementation', 'adoption', 'technical'];
if (genericRisks.some(g => risk.toLowerCase() === g)) {
score -= 0.2;
}
return Math.max(0, Math.min(1, score));
}
scoreOpportunityFeasibility(opportunity, sourceData) {
let score = 0.5;
// Check if opportunity is quantified
if (/\d+%/.test(opportunity))
score += 0.2;
// Check if opportunity aligns with capabilities
if (sourceData.use_cases?.some((uc) => opportunity.toLowerCase().includes(uc.category))) {
score += 0.2;
}
// Check if opportunity is realistic
if (opportunity.toLowerCase().includes('10x') ||
opportunity.toLowerCase().includes('revolutionary')) {
score -= 0.3; // Too ambitious
}
return Math.max(0, Math.min(1, score));
}
calculateEvidenceStrength(insights, sourceData) {
let evidencePoints = 0;
let totalPoints = 0;
// Check if insights are backed by data
if (insights.primary) {
totalPoints += insights.primary.length;
insights.primary.forEach((insight) => {
if (this.hasDataSupport(insight, sourceData)) {
evidencePoints++;
}
});
}
return totalPoints > 0 ? evidencePoints / totalPoints : 0;
}
// Semantic validation methods
findContradictions(response) {
const contradictions = [];
// Check confidence vs risk contradictions
if (response.executive_summary?.confidence === 'high' &&
response.insights?.risks?.length > 5) {
contradictions.push('High confidence contradicts numerous risks');
}
// Check ROI vs payback contradictions
if (response.summary?.expected_roi > 200 &&
response.summary?.payback_period_months > 36) {
contradictions.push('Very high ROI contradicts long payback period');
}
return contradictions;
}
findAmbiguities(response) {
const ambiguities = [];
// Check for vague recommendations
if (response.recommendations?.next_action &&
response.recommendations.next_action.split(' ').length < 5) {
ambiguities.push('Next action recommendation is too vague');
}
// Check for unclear timelines
if (response.recommendations?.timeline &&
!response.recommendations.timeline.match(/\d+/)) {
ambiguities.push('Timeline lacks specific duration');
}
return ambiguities;
}
findRedundancies(response) {
const redundancies = [];
// Check if insights repeat information
if (response.insights?.primary && response.executive_summary?.key_insight) {
const execInsight = response.executive_summary.key_insight.toLowerCase();
const duplicates = response.insights.primary.filter((i) => i.toLowerCase().includes(execInsight) || execInsight.includes(i.toLowerCase()));
if (duplicates.length > 0) {
redundancies.push('Primary insights duplicate executive summary');
}
}
return redundancies;
}
findSemanticGaps(response) {
const gaps = [];
// Check if risks have mitigations
if (response.insights?.risks?.length > 0 &&
(!response.recommendations?.mitigations ||
response.recommendations.mitigations.length === 0)) {
gaps.push('Risks identified but no mitigation strategies provided');
}
// Check if opportunities have action plans
if (response.insights?.opportunities?.length > 0 &&
!response.recommendations?.next_action) {
gaps.push('Opportunities identified but no action plan provided');
}
return gaps;
}
calculateReadability(response) {
// Simplified readability score based on structure
let score = 0.5;
if (response.executive_summary?.headline)
score += 0.2;
if (response.narrative?.style === 'conversational')
score += 0.1;
if (response.recommendations?.success_criteria?.length > 0)
score += 0.1;
if (response.metadata?.complexity === 'simple')
score += 0.1;
return Math.min(1, score);
}
// Auto-improvement methods
async fixIssue(response, issue) {
let fixed = false;
let description = '';
switch (issue.category) {
case 'completeness':
if (issue.description.includes('executive_summary')) {
response.executive_summary = response.executive_summary || {
headline: 'Analysis Complete',
confidence: 'medium',
key_insight: response.insights?.primary?.[0] || 'See detailed analysis'
};
fixed = true;
description = 'Added missing executive summary';
}
break;
case 'accuracy':
if (issue.description.includes('ROI') && issue.description.includes('range')) {
// Clamp ROI to valid range
if (response.summary?.expected_roi) {
response.summary.expected_roi = Math.max(-100, Math.min(10000, response.summary.expected_roi));
fixed = true;
description = 'Adjusted ROI to valid range';
}
}
break;
case 'logic':
if (issue.description.includes('Negative ROI requires justification')) {
response.justification = 'Strategic investment for future capabilities';
fixed = true;
description = 'Added justification for negative ROI';
}
break;
}
return { fixed, response, description };
}
enhanceClarity(response) {
// Add clear structure if missing
if (!response.executive_summary?.headline && response.summary) {
response.executive_summary = response.executive_summary || {};
response.executive_summary.headline =
`${response.summary.expected_roi}% ROI with ${response.summary.payback_period_months}-month payback`;
}
// Simplify complex insights
if (response.insights?.primary) {
response.insights.primary = response.insights.primary.map((insight) => {
if (insight.length > 100) {
// Shorten to key point
return insight.substring(0, 80) + '...';
}
return insight;
});
}
return response;
}
async enhanceCompleteness(response) {
// Add missing recommendations
if (!response.recommendations && response.insights) {
response.recommendations = {
next_action: response.insights.opportunities?.[0] ?
`Pursue ${response.insights.opportunities[0]}` :
'Review detailed analysis for opportunities',
timeline: 'Begin within 30 days',
success_criteria: ['Achieve projected ROI', 'Meet timeline targets']
};
}
// Add missing metadata
if (!response.metadata) {
response.metadata = {
generated_at: new Date().toISOString(),
confidence_score: 0.75,
data_quality: 'good'
};
}
return response;
}
// Helper methods
getSuggestedFix(issue) {
if (issue.includes('justification')) {
return 'Add business justification for unusual metrics';
}
if (issue.includes('evidence')) {
return 'Include supporting data and benchmarks';
}
if (issue.includes('mitigation')) {
return 'Add risk mitigation strategies';
}
return 'Review and correct the identified issue';
}
extractKeyTerms(response) {
const terms = [];
// Extract from various fields
const textFields = [
response.executive_summary?.headline,
response.executive_summary?.key_insight,
...(response.insights?.primary || []),
...(response.insights?.risks || [])
];
textFields.forEach(text => {
if (text) {
// Simple term extraction (in reality would use NLP)
const words = text.split(/\s+/).filter((w) => w.length > 4);
terms.push(...words);
}
});
return Array.from(new Set(terms));
}
hasDataSupport(insight, sourceData) {
// Check if insight references actual data
const hasNumbers = /\d+/.test(insight);
const referencesMetrics = ['roi', 'payback', 'cost', 'benefit', 'saving']
.some(metric => insight.toLowerCase().includes(metric));
return hasNumbers || referencesMetrics;
}
}
// Export singleton instance
export const qualityAssurance = new QualityAssurance();
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