SF-Bench for Companies

Evaluate AI coding tools objectively. Make data-driven decisions based on actual performance metrics.

🎯 Why Companies Need SF-Bench

The Problem

Companies investing in AI coding tools face uncertainty:

  • ❓ Which AI tool should we use for Salesforce?
  • ❓ What’s the ROI?
  • ❓ Will it work for our use cases?
  • ❓ How do we compare options?

The Solution

SF-Bench provides objective, data-driven answers:

  • ✅ Objective comparison of AI models
  • ✅ Real-world performance metrics
  • ✅ Salesforce-specific evaluation
  • ✅ Transparent methodology

💼 Business Case

The Opportunity

Salesforce development is expensive:

  • Senior Salesforce developers: $120K-$180K/year
  • Development time: Weeks to months per project
  • Maintenance costs: Ongoing

AI coding assistants can:

  • ⚡ Accelerate development by 30-50%
  • 💰 Reduce costs by automating routine tasks
  • 🎯 Improve quality with consistent patterns
  • 📈 Scale team productivity

The Challenge

Not all AI tools are equal:

  • Some work great for Python, but fail on Salesforce
  • Generic benchmarks don’t reflect Salesforce reality
  • No objective way to compare options

SF-Bench solves this by providing Salesforce-specific evaluation.

📊 ROI Calculator

Example: 10-Person Salesforce Development Team

Metric Without AI With Best AI Tool Improvement
Development Speed Baseline +40% 40% faster
Developer Cost $1.5M/year $1.2M/year $300K saved
Time to Market 3 months 2 months 1 month faster
Code Quality Baseline +20% Fewer bugs

Annual ROI: $300K+ in cost savings + faster time to market

Factors Affecting ROI

  1. Team Size: Larger teams = higher ROI
  2. AI Tool Performance: Better tools = higher ROI
  3. Use Case Fit: Salesforce-specific tools = higher ROI
  4. Adoption Rate: Higher adoption = higher ROI

🏢 Enterprise Features

What SF-Bench Offers Companies

1. Objective Evaluation

  • No vendor claims or marketing
  • Just facts and results
  • Transparent methodology

2. Salesforce-Specific

  • Tests actual Salesforce development
  • Validates functional outcomes
  • Production-ready code

3. Comprehensive Coverage

  • All Salesforce development types
  • Multiple difficulty levels
  • Real-world scenarios

4. Continuous Updates

  • New tasks added regularly
  • Latest model evaluations
  • Community-driven improvements

📈 How to Use SF-Bench

Step 1: Review Leaderboard

Check which models perform best: View Leaderboard →

Step 2: Run Your Own Evaluation

Test models on your specific use cases:

# Test with your preferred model
python scripts/evaluate.py \
  --model "your-model" \
  --tasks data/tasks/verified.json

Step 3: Compare Results

  • Compare with leaderboard
  • Analyze task-specific performance
  • Identify best fit for your needs

Step 4: Make Decision

  • Choose model based on data
  • Plan pilot program
  • Measure results

🎯 Use Cases

1. Tool Selection

Problem: Choosing between AI coding assistants

Solution: Use SF-Bench to compare:

  • Claude vs. GPT-4 vs. Gemini
  • Performance on Salesforce tasks
  • Cost vs. performance trade-offs

2. ROI Justification

Problem: Justifying AI tool investment

Solution: Use SF-Bench results to:

  • Show objective performance data
  • Calculate potential ROI
  • Build business case

3. Vendor Evaluation

Problem: Evaluating vendor claims

Solution: Test vendors’ models on SF-Bench:

  • Verify performance claims
  • Compare with competitors
  • Make data-driven decision

4. Team Training

Problem: Training team on AI tools

Solution: Use SF-Bench to:

  • Show best practices
  • Demonstrate capabilities
  • Build confidence

📋 Evaluation Checklist

Before Evaluation

  • Identify use cases
  • Set success criteria
  • Choose models to test
  • Set up evaluation environment

During Evaluation

  • Run SF-Bench evaluation
  • Test on your specific tasks
  • Measure performance
  • Document results

After Evaluation

  • Compare results
  • Analyze ROI
  • Make decision
  • Plan implementation

🏆 Success Stories

Coming soon: Case studies from companies using SF-Bench

💡 Best Practices

1. Start Small

  • Test with one model first
  • Run on subset of tasks
  • Validate approach

2. Measure Everything

  • Track performance metrics
  • Measure ROI
  • Document results

3. Involve Team

  • Get developer feedback
  • Test real use cases
  • Build consensus

4. Iterate

  • Start with pilot
  • Expand gradually
  • Optimize based on results

🔗 Resources

📞 Get Support

Ready to evaluate AI tools for your company? Start with our Quick Start Guide!