CRM Plus Batch - AI-Powered Intelligence

Process up to 10 profiles concurrently with premium AI-powered insights including real-time market intelligence via Perplexity AI.

When to Use

See the consolidated guidance: When to Use Each Batch Endpoint

Endpoint Details

Property	Value
Endpoint	POST /crm_plus_batch
Cost	3 credits × number of profiles
Max Profiles	10 per request
Tier Required	Plus
Processing Time	~15-25 seconds for 5 profiles

Overview

The CRM Plus Batch endpoint provides the most comprehensive analysis available, combining complete personality insights with real-time AI-powered market intelligence from Perplexity AI. Process multiple profiles simultaneously with premium features.

Key Features

• ✅ Concurrent Processing: Up to 10 profiles analyzed simultaneously
• ✅ Pay-per-Success: Only charged for successfully analyzed profiles
• 🤖 AI-Powered Insights: Real-time market intelligence via Perplexity AI
• ✅ Complete Analysis: OCEAN, MBTI, buyer manual, and AI insights
• ✅ Market Context: Current industry trends and company news

Plus Additional AI-Powered Insights

• 🤖 Real-time Market Intelligence - Current industry trends and market dynamics
• 🤖 Company Context Analysis - Recent news, strategic initiatives, market positioning
• 🤖 Competitive Landscape - Industry positioning and competitive advantages
• 🤖 Enhanced Pain Points - AI-identified challenges based on current market conditions
• 🤖 Strategic Recommendations - AI-generated engagement strategies

Request

Headers

Header	Required	Value
Content-Type	Yes	application/json
x-api-key	Yes	Your API key

Request Body

{
  "profiles": [
    {
      "primeiro_nome": "Satya",
      "ultimo_nome": "Nadella",
      "empresa": "Microsoft",
      "contexto_adicional": "Interested in cloud solutions"
    },
    {
      "primeiro_nome": "Sundar",
      "ultimo_nome": "Pichai",
      "empresa": "Google"
    }
  ]
}

Parameters

Parameter	Type	Required	Description
profiles	array	Yes	Array of profile objects (max 10)
profiles[].primeiro_nome	string	Yes	First name
profiles[].ultimo_nome	string	Yes	Last name
profiles[].empresa	string	Yes	Company name
profiles[].contexto_adicional	string	No	Additional context for AI analysis

Example Request

cURL

curl -X POST "https://api.fluenceinsights.com/crm_plus_batch" \
  -H "Content-Type: application/json" \
  -H "x-api-key: your-api-key-here" \
  -d '{
    "profiles": [
      {"primeiro_nome": "Satya", "ultimo_nome": "Nadella", "empresa": "Microsoft"},
      {"primeiro_nome": "Sundar", "ultimo_nome": "Pichai", "empresa": "Google"}
    ]
  }'

Python

import requests

API_URL = "https://api.fluenceinsights.com/crm_plus_batch"
API_KEY = "your-api-key-here"

profiles = [
    {
        "primeiro_nome": "Satya",
        "ultimo_nome": "Nadella",
        "empresa": "Microsoft",
        "contexto_adicional": "Enterprise cloud transformation"
    },
    {
        "primeiro_nome": "Sundar",
        "ultimo_nome": "Pichai",
        "empresa": "Google"
    }
]

response = requests.post(
    API_URL,
    headers={
        "Content-Type": "application/json",
        "x-api-key": API_KEY
    },
    json={"profiles": profiles}
)

if response.status_code == 200:
    data = response.json()
    summary = data['batch_summary']
    print(f"✅ Processed {summary['successful']}/{summary['total_profiles']} profiles")
    print(f"⚡ Time: {summary['processing_time_ms']/1000:.1f}s")
    print(f"💰 Credits: {data['billing_info']['creditos_utilizados']}")

    # Access AI insights
    for result in data['results']:
        if result['success']:
            analise = result['analise']
            print(f"\n{result['input']['primeiro_nome']} - {result['input']['empresa']}:")
            print(f"  Market Trends: {analise['ai_insights']['market_trends']}")
            print(f"  Strategic Recommendations: {analise['ai_insights']['engagement_strategy']}")
else:
    print(f"❌ Error: {response.json()}")

JavaScript/Node.js

const axios = require('axios');

const API_URL = 'https://api.fluenceinsights.com/crm_plus_batch';
const API_KEY = 'your-api-key-here';

async function analyzeBatchWithAI() {
  const profiles = [
    {
      primeiro_nome: 'Satya',
      ultimo_nome: 'Nadella',
      empresa: 'Microsoft',
      contexto_adicional: 'Cloud transformation'
    },
    {
      primeiro_nome: 'Sundar',
      ultimo_nome: 'Pichai',
      empresa: 'Google'
    }
  ];

  try {
    const response = await axios.post(
      API_URL,
      { profiles },
      {
        headers: {
          'Content-Type': 'application/json',
          'x-api-key': API_KEY
        }
      }
    );

    const { batch_summary, results, billing_info } = response.data;
    console.log(`✅ ${batch_summary.successful}/${batch_summary.total_profiles} profiles`);
    console.log(`💰 Credits: ${billing_info.creditos_utilizados}`);

    results.forEach(result => {
      if (result.success) {
        const { analise, input } = result;
        console.log(`\n${input.primeiro_nome} - ${input.empresa}:`);
        console.log(`Market Trends: ${analise.ai_insights.market_trends}`);
      }
    });
  } catch (error) {
    console.error('❌ Error:', error.response?.data || error.message);
  }
}

analyzeBatchWithAI();

Response

Success Response (200)

{
  "success": true,
  "batch_summary": {
    "total_profiles": 2,
    "successful": 2,
    "failed": 0,
    "processing_time_ms": 18456.32
  },
  "results": [
    {
      "success": true,
      "profile_index": 0,
      "input": {
        "primeiro_nome": "Satya",
        "ultimo_nome": "Nadella",
        "empresa": "Microsoft"
      },
      "analise": {
        "cargo": "Chairman and CEO",
        "tipo_stakeholder": "C-Level Executive",
        "ocean": {
          "openness": 8.5,
          "conscientiousness": 9.0,
          "extraversion": 7.5,
          "agreeableness": 8.0,
          "neuroticism": 3.0
        },
        "mbti": {
          "tipo": "ENTJ",
          "descricao": "The Commander - Strategic, ambitious, natural leader",
          "pontos_fortes": [
            "Strategic thinking",
            "Decisive leadership",
            "Innovation focus"
          ],
          "comunicacao_preferida": "Direct, goal-oriented, data-driven"
        },
        "manual_comprador": {
          "dores": [
            "Digital transformation challenges",
            "Cloud migration complexity",
            "Competitive market pressure"
          ],
          "ganhos_procurados": [
            "Innovation acceleration",
            "Market leadership",
            "Business growth"
          ],
          "prioridades": [
            "Technology innovation",
            "Customer success",
            "Organizational culture"
          ]
        },
        "linguagem_impacto": [
          "Innovation drives growth",
          "Empowering transformation",
          "Customer-first approach"
        ],
        "ai_insights": {
          "market_trends": "Microsoft is focusing heavily on AI integration across all products, with significant investments in OpenAI and Azure AI services. The enterprise cloud market shows 25% YoY growth.",
          "company_context": "Recently announced major AI partnerships and Azure expansion into new regions. Microsoft reported strong Q2 earnings driven by cloud growth.",
          "competitive_landscape": "Leading position in enterprise cloud with Azure, competing directly with AWS and Google Cloud. Strong differentiation through AI and productivity tools integration.",
          "enhanced_pain_points": [
            "Balancing rapid AI innovation with enterprise security and compliance",
            "Managing multi-cloud strategies for enterprise customers",
            "Addressing talent shortage in AI and cloud engineering"
          ],
          "engagement_strategy": "Focus on AI-driven cloud transformation solutions. Emphasize security, compliance, and seamless integration with existing Microsoft ecosystem. Highlight ROI from productivity gains and innovation acceleration.",
          "key_initiatives": [
            "AI Copilot integration across Microsoft 365",
            "Azure OpenAI Service expansion",
            "Sustainability commitments and carbon-negative goals"
          ]
        }
      }
    },
    {
      "success": true,
      "profile_index": 1,
      "input": {
        "primeiro_nome": "Sundar",
        "ultimo_nome": "Pichai",
        "empresa": "Google"
      },
      "analise": {
        "cargo": "CEO",
        "tipo_stakeholder": "C-Level Executive",
        "ocean": {...},
        "mbti": {...},
        "manual_comprador": {...},
        "linguagem_impacto": [...],
        "ai_insights": {
          "market_trends": "Google is prioritizing AI-first product development, with Gemini AI competing directly with GPT models...",
          "company_context": "Recent Gemini AI launch and integration into Google Workspace...",
          "competitive_landscape": "Strong position in search and digital advertising, expanding into cloud and AI...",
          "enhanced_pain_points": [...],
          "engagement_strategy": "...",
          "key_initiatives": [...]
        }
      }
    }
  ],
  "billing_info": {
    "creditos_por_perfil": 3,
    "perfis_processados": 2,
    "creditos_utilizados": 6,
    "creditos_restantes": 194,
    "tipo_plano": "plus"
  }
}

Plus Tier Additional Fields

Field	Type	Description
ai_insights	object	AI-powered market intelligence
ai_insights.market_trends	string	Current industry trends and dynamics
ai_insights.company_context	string	Recent news and strategic initiatives
ai_insights.competitive_landscape	string	Industry positioning analysis
ai_insights.enhanced_pain_points	array	AI-identified current challenges
ai_insights.engagement_strategy	string	Strategic recommendations
ai_insights.key_initiatives	array	Current company priorities

Error Responses

Insufficient Credits (402)

{
  "error": "Insufficient credits",
  "message": "Batch requires 30 credits but you have 20",
  "profiles_requested": 10,
  "credits_per_profile": 3,
  "credits_available": 20
}

Batch Size Exceeded (400)

{
  "error": "Batch size exceeds maximum",
  "message": "Maximum 10 profiles per batch, received 15",
  "max_batch_size": 10,
  "profiles_received": 15
}

AI Service Unavailable (503)

{
  "success": true,
  "batch_summary": {
    "total_profiles": 2,
    "successful": 1,
    "failed": 1
  },
  "results": [
    {
      "success": true,
      "profile_index": 0,
      "analise": {...}
    },
    {
      "success": false,
      "profile_index": 1,
      "error": "AI insights temporarily unavailable",
      "error_code": "AI_SERVICE_UNAVAILABLE"
    }
  ]
}

Credit Calculation

Scenario	Calculation	Total Credits
5 profiles, all successful	3 × 5	15 credits
10 profiles, all successful	3 × 10	30 credits
10 profiles, 8 successful	3 × 8	24 credits

Note: You are only charged for successfully analyzed profiles. Failed analyses do not consume credits.

Performance Benchmarks

Profiles	Single Requests	Batch Request	Time Saved
5 profiles	~75-125 sec	~20-30 sec	75% faster
10 profiles	~150-250 sec	~40-60 sec	75% faster

Note: Plus tier takes longer due to real-time AI processing, but batch processing still provides significant time savings.

Use Cases

Enterprise Sales Intelligence

def analyze_enterprise_prospects(prospects):
    """Analyze enterprise prospects with AI insights"""
    batch_size = 10
    enriched_data = []

    for i in range(0, len(prospects), batch_size):
        batch = prospects[i:i + batch_size]
        response = requests.post(API_URL, json={"profiles": batch}, headers=headers)

        if response.status_code == 200:
            for result in response.json()['results']:
                if result['success']:
                    ai_insights = result['analise']['ai_insights']
                    enriched_data.append({
                        'name': f"{result['input']['primeiro_nome']} {result['input']['ultimo_nome']}",
                        'company': result['input']['empresa'],
                        'market_trends': ai_insights['market_trends'],
                        'engagement_strategy': ai_insights['engagement_strategy'],
                        'key_initiatives': ai_insights['key_initiatives'],
                        'pain_points': ai_insights['enhanced_pain_points']
                    })

        time.sleep(1)  # Rate limiting for AI-powered requests

    return enriched_data

Strategic Account Planning

def create_account_plan(executives):
    """Create strategic account plan with AI insights"""
    response = requests.post(
        API_URL,
        json={"profiles": executives},
        headers=headers
    )

    if response.status_code == 200:
        results = response.json()['results']

        account_plan = {
            'executives': [],
            'market_context': [],
            'strategic_recommendations': []
        }

        for result in results:
            if result['success']:
                analise = result['analise']
                account_plan['executives'].append({
                    'name': f"{result['input']['primeiro_nome']} {result['input']['ultimo_nome']}",
                    'position': analise['cargo'],
                    'mbti': analise['mbti']['tipo'],
                    'communication_style': analise['mbti']['comunicacao_preferida']
                })
                account_plan['market_context'].append(analise['ai_insights']['market_trends'])
                account_plan['strategic_recommendations'].append(
                    analise['ai_insights']['engagement_strategy']
                )

        return account_plan

Best Practices

1. Optimize for AI Processing

• Context is Key: Use contexto_adicional to provide relevant context for better AI insights
• Batch Size: 5-10 profiles per batch recommended for Plus tier
• Rate Limiting: Add 1-2s delay between batches due to AI processing

2. Leverage AI Insights

def extract_actionable_insights(batch_results):
    """Extract actionable insights from AI analysis"""
    insights = {
        'market_opportunities': [],
        'engagement_strategies': [],
        'common_pain_points': []
    }

    for result in batch_results['results']:
        if result['success']:
            ai = result['analise']['ai_insights']
            insights['market_opportunities'].append(ai['market_trends'])
            insights['engagement_strategies'].append(ai['engagement_strategy'])
            insights['common_pain_points'].extend(ai['enhanced_pain_points'])

    return insights

3. Credit Management

# Plus tier requires more credits - verify before processing
credits_needed = len(profiles) * 3  # 3 credits per profile
current_credits = check_credits(API_KEY)

if current_credits < credits_needed:
    print(f"⚠️ Need {credits_needed} credits, have {current_credits}")
    # Consider using Complete tier instead
    use_complete_tier = True
else:
    process_plus_batch(profiles)

When to Use This Endpoint

✅ Use CRM Plus Batch When:

• Need real-time market intelligence for multiple prospects
• Building strategic account plans
• Analyzing competitive positioning
• Enterprise sales requiring deep insights
• Creating personalized engagement strategies at scale

❌ Use Lower Tiers When:

• Basic contact enrichment is sufficient
• Working with limited budget
• Don't need real-time market data
• Processing high volumes (use Complete tier)

Comparison with Other Tiers

Feature	Basic Batch	Complete Batch	Plus Batch
Credits per profile	1	2	3
OCEAN scores	❌	✅	✅
MBTI type	❌	✅	✅
Buyer playbook	Basic	Complete	Complete
AI market insights	❌	❌	✅
Company context	❌	❌	✅
Engagement strategy	❌	❌	✅
Processing time	~8-10s	~8-10s	~15-25s

Next Steps

• View CRM Basic Batch (1 credit) →
• View CRM Complete Batch (2 credits) →
• Learn about single CRM Plus analysis →
• Explore credit system →