Case Study: How Acme Corp Saved $50,000/Month on AI Costs

Current: $80K/month AI costs
Revenue per customer: $500/month
Number of customers: 300
Monthly Revenue: $150K

AI costs = 53% of revenue!

// Their usage breakdown
const breakdown = {
  customerChat: {
    requests: 450000,  // 450K requests/month
    model: 'gpt-4',    // Using GPT-4 for everything
    avgCost: 0.12,     // $0.12 per conversation
    totalCost: 54000   // $54K/month
  },
  ticketRouting: {
    requests: 180000,  // 180K requests/month
    model: 'gpt-4',    // Even for simple classification!
    avgCost: 0.08,
    totalCost: 14400   // $14.4K/month
  },
  summaryGeneration: {
    requests: 90000,   // 90K requests/month
    model: 'gpt-4',
    avgCost: 0.15,
    totalCost: 13500   // $13.5K/month
  }
}

console.log(\`Total: $\${(54000 + 14400 + 13500).toFixed(2)}\`)
// $81,900/month

// Before
const response = await openai.chat.completions.create({
  model: 'gpt-4',
  messages: conversationHistory  // Unlimited history!
})

// After
const response = await openai.chat.completions.create({
  model: 'gpt-4',
  messages: trimHistory(conversationHistory, 4000),  // Limit history
  max_tokens: 500  // Limit response length
})

class AcmeRouter {
  route(prompt: string, context: Context) {
    // Simple ticket routing → GPT-3.5
    if (context.feature === 'ticket_routing') {
      return {
        model: 'gpt-3.5-turbo',
        reasoning: 'Simple classification task',
        estimatedCost: 0.002
      }
    }

    // FAQ queries → GPT-3.5
    if (this.isFAQ(prompt)) {
      return {
        model: 'gpt-3.5-turbo',
        reasoning: 'Common question pattern',
        estimatedCost: 0.003
      }
    }

    // Complex support → GPT-4
    if (this.isComplexIssue(prompt)) {
      return {
        model: 'gpt-4-turbo',
        reasoning: 'Complex technical issue',
        estimatedCost: 0.12
      }
    }

    // Default: Claude 3 Sonnet (balanced)
    return {
      model: 'claude-3-sonnet',
      reasoning: 'Standard support query',
      estimatedCost: 0.04
    }
  }

  private isFAQ(prompt: string): boolean {
    const faqPatterns = [
      /how (do|can) i/i,
      /what is/i,
      /where (is|can)/i,
      /reset password/i,
      /pricing/i
    ]
    return faqPatterns.some(pattern => pattern.test(prompt))
  }

  private isComplexIssue(prompt: string): boolean {
    return prompt.length > 500 ||
           prompt.includes('bug') ||
           prompt.includes('error') ||
           prompt.includes('not working')
  }
}

import { SemanticCache } from '@reforge/semantic-cache'

const cache = new SemanticCache({
  similarityThreshold: 0.92,  // Strict threshold for accuracy
  ttl: 7200,  // 2 hours (support tickets change)
  embeddingModel: 'text-embedding-ada-002'
})

async function handleSupportQuery(query: string) {
  // Check semantic cache first
  const cached = await cache.get(query)
  if (cached) {
    console.log('Cache HIT - $0 cost')
    return cached
  }

  // Cache miss - call LLM
  const routing = await router.route(query, { feature: 'chat' })
  const response = await callLLM(routing.model, query)

  // Cache for future similar queries
  await cache.set(query, response, 7200)

  return response
}

// Ran A/B test for 2 weeks
const results = {
  control: {  // GPT-4 only
    userSatisfaction: 0.87,
    resolutionRate: 0.82,
    avgCost: 0.12
  },
  treatment: {  // Intelligent routing
    userSatisfaction: 0.86,  // -1% (not statistically significant)
    resolutionRate: 0.83,  // +1%!
    avgCost: 0.046  // -62% cost
  }
}

// Before: High hit rate, poor accuracy
const cache = new SemanticCache({ similarityThreshold: 0.85 })
// Hit rate: 72%, Error rate: 12%

// After: Lower hit rate, excellent accuracy
const cache = new SemanticCache({ similarityThreshold: 0.92 })
// Hit rate: 58%, Error rate: 2%

// Made routing opt-in initially
async function handleQuery(query: string, options?: { useRouting?: boolean }) {
  if (options?.useRouting) {
    const routing = await router.route(query)
    return callLLM(routing.model, query)
  }

  // Fallback to GPT-4
  return callLLM('gpt-4', query)
}

// After seeing results, made it default

const roi = {
  implementation: {
    engineeringTime: '4 weeks × 2 engineers',
    cost: 40000  // Loaded cost
  },
  platformFee: {
    monthly: 499,
    annual: 5988
  },
  savings: {
    monthly: 50000,
    annual: 600000
  },
  netAnnualSavings: 600000 - 40000 - 5988,  // $554,012
  roi: ((600000 - 40000 - 5988) / (40000 + 5988)) * 100  // 1,205% ROI
}

console.log(\`ROI: \${roi.roi.toFixed(0)}%\`)
// 1,205% ROI