1. Laying the Foundation: Smart Data Collection

Let's start with the backbone of your system - intelligent data collection. Think of this as building a sophisticated nervous system that captures every nuance of compensation data across your organization.

Your first task is implementing Bindbee's API endpoints strategically:

But here's what makes this implementation special - you're not just collecting data, you're building context. When a customer's HR system connects through Bindbee's magic link, your application should:

• First, capture base compensation data using the hris_compensation model
• Then, enrich it with role context from hris_employment
• Layer in employee attributes from hris_employee
• Finally, validate against actual payments from hris_employee_payroll_run

1. Building Your AI Brain

This is where your system starts getting smart. You're creating an intelligence layer that doesn't just process numbers - it understands compensation patterns and market dynamics.

Your AI engine needs three key components:

Market Analysis Engine:

• Build on top of hris_compensation.pay_rate data
• Factor in hris_employee.custom_fields for unique role attributes
• Use hris_employment.job_title for role matching

Why this matters: When your users need to benchmark a specific role, your system isn't just matching job titles - it's understanding the true nature of each position through multiple data points.

Pattern Recognition System:

• Track compensation trends using historical pay_rate data
• Analyze promotion patterns through employment.effective_date
• Monitor market adjustments through payroll_run data

1. Creating Dynamic Learning Loops

Here's where your system gets really interesting. Instead of static benchmarks, you're building a system that learns and adapts:

Implement continuous learning by:

• Tracking compensation changes through webhooks
• Analyzing the impact of role changes on pay rates
• Identifying emerging compensation patterns

Pro Tip: Use hris_employee.custom_fields to capture unique factors that influence compensation - this is often where the most valuable insights hide.

1. Making Intelligence Actionable

The final piece is turning all this intelligence into actionable insights. Your system should:

• Generate real-time market rate predictions
• Identify compensation anomalies
• Suggest adjustment ranges based on multiple factors