ARC Advisor: Intelligent CRM Query Assistant for LLMs

🚀 Model Overview

ARC Advisor is a specialized advisory model designed to enhance Large Language Models' performance on CRM and Salesforce-related tasks. By providing intelligent guidance and query structuring suggestions, it helps LLMs achieve significantly better results on complex CRM operations.

✨ Key Benefits

X% Performance Boost: Improves LLM accuracy on CRM tasks when used as an advisor
Intelligent Query Planning: Provides structured approaches for complex Salesforce queries
Error Prevention: Identifies potential pitfalls before query execution
Cost Efficient: Small 4B model provides guidance to larger models, reducing overall compute costs

🎯 Use Cases

1. LLM Performance Enhancement

Boost your existing LLM's CRM capabilities by using ARC Advisor as a preprocessing step:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load ARC Advisor
advisor = AutoModelForCausalLM.from_pretrained("aman-jaglan/arc-advisor")
tokenizer = AutoTokenizer.from_pretrained("aman-jaglan/arc-advisor")

def enhance_llm_query(user_request):
    # Step 1: Get advisory guidance
    advisor_prompt = f"""As a CRM expert, provide guidance for this request:
    {user_request}
    
    Suggest the best approach, relevant objects, and query structure."""
    
    inputs = tokenizer(advisor_prompt, return_tensors="pt")
    advice = advisor.generate(**inputs, max_new_tokens=200)
    
    # Step 2: Use advice to enhance main LLM prompt
    enhanced_prompt = f"""
    Expert Guidance: {tokenizer.decode(advice[0])}
    
    Now execute: {user_request}
    """
    
    return enhanced_prompt

2. Query Optimization

Transform vague requests into structured CRM queries:

Input: "Show me our best customers from last quarter"
ARC Advisor Output: Structured approach with relevant Salesforce objects, filters, and aggregations
Result: Precise SOQL query with proper date ranges and metrics

3. Multi-Step Reasoning

Guide LLMs through complex multi-object queries:

Lead-to-Opportunity conversion analysis
Cross-object relationship queries
Time-based trend analysis
Performance metric calculations

🛠️ Integration Examples

With OpenAI GPT Models

import openai

# Get advisor guidance first
advice = get_arc_advisor_guidance(query)

# Enhanced GPT query
response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": f"CRM Expert Guidance: {advice}"},
        {"role": "user", "content": original_query}
    ]
)

With Local LLMs (vLLM)

# Deploy ARC Advisor on lightweight infrastructure
# Use output to guide larger local models
advisor_server = "http://localhost:8000/v1/chat/completions"
main_llm_server = "http://localhost:8001/v1/chat/completions"

📊 Performance Impact

When used as an advisor:

Query Success Rate: +X% improvement
Complex Query Handling: +X% accuracy boost
Error Reduction: X% fewer malformed queries
Time to Solution: X% faster query resolution

🔧 Deployment

Quick Start

# Using Transformers
from transformers import pipeline
advisor = pipeline("text-generation", model="aman-jaglan/arc-advisor")

# Using vLLM (recommended for production)
python -m vllm.entrypoints.openai.api_server \
    --model aman-jaglan/arc-advisor \
    --dtype bfloat16 \
    --max-model-len 4096

Resource Requirements

GPU Memory: 8GB (bfloat16)
CPU: Supported with reduced speed
Optimal Batch Size: 32-64 requests

🏆 Why ARC Advisor?

Specialized Expertise: Trained specifically for CRM/Salesforce domain
Efficient Architecture: Small model that enhances larger models
Production Ready: Optimized for low-latency advisory generation
Cost Effective: Reduce expensive LLM calls through better query planning

📚 Model Details

Architecture: Qwen3-4B base with specialized fine-tuning
Context Length: 4096 tokens
Output Format: Structured advisory guidance
Language: English

🤝 Community

Join our community to share your experiences and improvements:

Report issues on the model repository
Share your integration examples
Contribute to best practices documentation

📜 License

Apache 2.0 - Commercial use permitted with attribution

Transform your LLM into a CRM expert with ARC Advisor

aman-jaglan
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arc-advisor