Technische Implementierungsrichtlinien

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic();

async function analyzeLeadWithClaude(leadData: any) {
  const message = await client.messages.create({
    model: "claude-3-5-sonnet-20241022",
    max_tokens: 1024,
    messages: [
      {
        role: "user",
        content: `Analyze this B2B lead and provide insights:
${JSON.stringify(leadData, null, 2)}`
      }
    ]
  });

  return message.content[0].type === "text" ? message.content[0].text : "";
}

// Usage
const leadAnalysis = await analyzeLeadWithClaude({
  company: "TechCorp Inc",
  industry: "SaaS",
  revenue: "$50M",
  painPoints: ["Lead generation", "Sales automation"]
});

console.log(leadAnalysis);

-- BigQuery ML: Predictive Account Scoring
CREATE OR REPLACE MODEL `project.dataset.account_propensity_model`
OPTIONS(
  model_type='linear_reg',
  input_label_cols=['converted']
) AS
SELECT
  account_size,
  industry_vertical,
  engagement_score,
  email_opens,
  website_visits,
  converted
FROM `project.dataset.training_data`
WHERE date BETWEEN '2025-01-01' AND '2025-12-31';

-- Score new accounts
SELECT
  account_id,
  account_name,
  predicted_conversion_probability,
  RANK() OVER (ORDER BY predicted_conversion_probability DESC) as rank
FROM ML.PREDICT(
  MODEL `project.dataset.account_propensity_model`,
  (
    SELECT
      account_id,
      account_name,
      account_size,
      industry_vertical,
      engagement_score,
      email_opens,
      website_visits
    FROM `project.dataset.accounts`
  )
)
WHERE predicted_conversion_probability > 0.7
ORDER BY predicted_conversion_probability DESC
LIMIT 50;

// Grok X-PAS Framework Implementation
import { Grok } from "grok-sdk";

const grok = new Grok({
  apiKey: process.env.GROK_API_KEY
});

async function generateXNativeAd(targetAccount: any) {
  // Phase 1: eXpose - Scan X for pain points
  const painPoints = await grok.scanXConversations({
    keywords: targetAccount.industry_keywords,
    timeframe: "last_7_days",
    sentiment: "negative"
  });

  // Phase 2: Polarize - Create contrarian angle
  const adCopy = await grok.generateAd({
    framework: "X-PAS",
    painPoints: painPoints,
    tone: "contrarian",
    platform: "x"
  });

  // Phase 3: Signal - Add social proof
  const socialSignals = await grok.getXEngagementMetrics({
    accountHandle: targetAccount.twitter_handle
  });

  return {
    adCopy: adCopy,
    socialProof: socialSignals,
    nativeScore: calculateNativeScore(adCopy, socialSignals)
  };
}

// Usage
const xAd = await generateXNativeAd({
  company: "RevOps Inc",
  industry_keywords: ["pipeline", "churn", "forecasting"],
  twitter_handle: "@revopsinc"
});

console.log("Generated X Ad:", xAd);

# Mistral PEFT Fine-Tuning for Brand-Specific Ads
import torch
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model
model_name = "mistralai/Mistral-7B-Instruct-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Configure LoRA
lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

# Apply LoRA
model = get_peft_model(model, lora_config)

# Fine-tune on brand guidelines
from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
    output_dir="./brand_specific_model",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    learning_rate=2e-4,
    save_steps=100
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=brand_guidelines_dataset
)

trainer.train()

# Save LoRA weights
model.save_pretrained("./brand_lora_weights")