CRM Basic Batch - Name + Company

Process up to 10 profiles concurrently in a single request with basic tier analysis.

When to Use

See the consolidated guidance: When to Use Each Batch Endpoint

Endpoint Details

Property	Value
Endpoint	POST /crm_basico_batch
Cost	1 credit × number of profiles
Max Profiles	10 per request
Tier Required	Basic or Complete
Processing Time	~8-10 seconds for 5 profiles

Overview

The CRM Basic Batch endpoint allows you to analyze multiple professional profiles simultaneously using name and company information. You only pay for successfully analyzed profiles (pay-per-success billing).

Key Features

• ✅ Concurrent Processing: Up to 10 profiles analyzed simultaneously
• ✅ Pay-per-Success: Only charged for successfully analyzed profiles
• ✅ Time Efficient: ~65% faster than sequential single requests
• ✅ Basic Analysis: Position, stakeholder type, buyer manual, impact language

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": "Optional context"
    },
    {
      "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

Example Request

cURL

curl -X POST "https://api.fluenceinsights.com/crm_basico_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_basico_batch"
API_KEY = "your-api-key-here"

profiles = [
    {"primeiro_nome": "Satya", "ultimo_nome": "Nadella", "empresa": "Microsoft"},
    {"primeiro_nome": "Sundar", "ultimo_nome": "Pichai", "empresa": "Google"},
    {"primeiro_nome": "Tim", "ultimo_nome": "Cook", "empresa": "Apple"}
]

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']}")
else:
    print(f"❌ Error: {response.json()}")

JavaScript/Node.js

const axios = require('axios');

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

async function analyzeBatch() {
  const profiles = [
    { primeiro_nome: 'Satya', ultimo_nome: 'Nadella', empresa: 'Microsoft' },
    { primeiro_nome: 'Sundar', ultimo_nome: 'Pichai', empresa: 'Google' },
    { primeiro_nome: 'Tim', ultimo_nome: 'Cook', empresa: 'Apple' }
  ];

  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(`⚡ Time: ${(batch_summary.processing_time_ms/1000).toFixed(1)}s`);
    console.log(`💰 Credits: ${billing_info.creditos_utilizados}`);
  } catch (error) {
    console.error('❌ Error:', error.response?.data || error.message);
  }
}

analyzeBatch();

Response

Success Response (200)

{
  "success": true,
  "batch_summary": {
    "total_profiles": 2,
    "successful": 2,
    "failed": 0,
    "processing_time_ms": 4698.18
  },
  "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",
        "manual_comprador": {
          "dores": [
            "Digital transformation challenges",
            "Cloud migration complexity"
          ],
          "ganhos_procurados": [
            "Innovation acceleration",
            "Business growth"
          ]
        },
        "linguagem_impacto": [
          "Innovation drives growth",
          "Empowering transformation"
        ]
      }
    },
    {
      "success": true,
      "profile_index": 1,
      "input": {
        "primeiro_nome": "Sundar",
        "ultimo_nome": "Pichai",
        "empresa": "Google"
      },
      "analise": {
        "cargo": "CEO",
        "tipo_stakeholder": "C-Level Executive",
        "manual_comprador": {...},
        "linguagem_impacto": [...]
      }
    }
  ],
  "billing_info": {
    "creditos_por_perfil": 1,
    "perfis_processados": 2,
    "creditos_utilizados": 2,
    "creditos_restantes": 198,
    "tipo_plano": "complete"
  }
}

Response Fields

Batch Summary

Field	Type	Description
total_profiles	integer	Total number of profiles in batch
successful	integer	Number of successfully analyzed profiles
failed	integer	Number of failed analyses
processing_time_ms	float	Total processing time in milliseconds

Individual Results

Field	Type	Description
success	boolean	Whether analysis was successful
profile_index	integer	Index of profile in original batch
input	object	Original input data for this profile
analise	object	Analysis results (if successful)
error	string	Error message (if failed)
error_code	string	Error code (if failed)

Billing Info

Field	Type	Description
creditos_por_perfil	integer	Credits charged per successful profile (1)
perfis_processados	integer	Number of profiles successfully processed
creditos_utilizados	integer	Total credits used in this batch
creditos_restantes	integer	Remaining credits after this batch
tipo_plano	string	Current subscription plan

Error Responses

Insufficient Credits (402)

{
  "error": "Insufficient credits",
  "message": "Batch requires 10 credits but you have 5",
  "profiles_requested": 10,
  "credits_per_profile": 1,
  "credits_available": 5
}

Batch Size Exceeded (400)

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

Partial Batch Failure (200)

{
  "success": true,
  "batch_summary": {
    "total_profiles": 3,
    "successful": 2,
    "failed": 1
  },
  "results": [
    {
      "success": true,
      "profile_index": 0,
      "analise": {...}
    },
    {
      "success": false,
      "profile_index": 1,
      "input": {"primeiro_nome": "John", "empresa": "Unknown"},
      "error": "Profile not found",
      "error_code": "PROFILE_NOT_FOUND"
    },
    {
      "success": true,
      "profile_index": 2,
      "analise": {...}
    }
  ],
  "billing_info": {
    "creditos_utilizados": 2
  }
}

Credit Calculation

Scenario	Calculation	Total Credits
5 profiles, all successful	1 × 5	5 credits
10 profiles, all successful	1 × 10	10 credits
10 profiles, 8 successful	1 × 8	8 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	~20-25 sec	~8-10 sec	60% faster
10 profiles	~40-50 sec	~15-20 sec	65% faster

Best Practices

1. Batch Size Optimization

• Recommended: 10 profiles per batch (maximum)
• For large datasets: Process in sequential batches of 10
• Add delays: 0.5-1s between batches for large-scale processing

2. Handling Partial Failures

def process_batch_results(batch_response):
    """Separate successful and failed results"""
    successful = []
    failed = []

    for result in batch_response['results']:
        if result['success']:
            successful.append({
                'input': result['input'],
                'analysis': result['analise']
            })
        else:
            failed.append({
                'input': result['input'],
                'error': result['error']
            })

    return successful, failed

3. Processing Large Datasets

def process_large_dataset(profiles, batch_size=10):
    """Process large datasets in optimized batches"""
    results = []

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

        if response.status_code == 200:
            batch_results = response.json()['results']
            results.extend(batch_results)
            print(f"Progress: {min(i + batch_size, len(profiles))}/{len(profiles)}")

        time.sleep(0.5)  # Rate limiting

    return results

When to Use This Endpoint

✅ Use CRM Basic Batch When:

• Processing 2-10 profiles at once
• You have name + company information
• Time efficiency is important
• Building sales intelligence databases
• Bulk CRM enrichment needed

❌ Use Single Endpoint When:

• Analyzing only 1 profile
• Real-time interactive analysis needed
• Immediate feedback required

Next Steps

• View CRM Complete Batch (2 credits) →
• View CRM Plus Batch (3 credits) →
• Learn about single profile analysis →
• Explore credit system →