Dadoji: The Rule Engine That Brings Wisdom to AI

 

🧭 Introduction

In the early days of Swarajya, before Shivaji Maharaj raised a sword or won a fort, there was a man guiding him with discipline, vision, and strategy.

That man was Dadoji Konddev — mentor, administrator, and steward of Shivaji’s early years.

In our Fraud-Free Swarajya system, dadoji is our rule engine — the voice of grounded wisdom that complements the instincts of AI (tanaji).
Because not every decision can be made by machine learning — sometimes, you need good old domain knowledge.

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🧠 Why Do We Need Rules When We Have AI?

AI is great at learning patterns.
But AI can also:

• Be overconfident

• Miss edge cases

• Require lots of data to get it right

That’s where business rules come in — to override, validate, or support AI decisions with deterministic logic like:

✅ "Reject any transaction over ₹2,00,000 unless user is verified"
✅ "Flag if payment method is 'gift card' AND location is flagged"
✅ "Approve only if AI score is < 0.3 AND no high-risk indicators exist"

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⚙️ How Dadoji Works

The dadoji component is implemented in Java and is integrated with sindhudurg via method calls for now.

🧩 Architecture

• Rules are modeled as Java interfaces (FraudRule).

• Each rule implements:

  java
  
  boolean evaluate(TransactionRequest request);
  String getExplanation();
  RiskIndicator getRiskIndicator();
  

• Multiple rules are evaluated by the RuleExecutor, which returns:

  • ✅ A list of triggered RiskIndicators

  • ✅ Additional explanations if any

  • ✅ Final recommendation (APPROVE, FLAG, or REJECT)

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🧪 Sample Rule: High Amount Rule

java

public class HighAmountRule implements FraudRule {
    private static final BigDecimal THRESHOLD = new BigDecimal("100000");

    public boolean evaluate(TransactionRequest request) {
        return request.getAmount().compareTo(THRESHOLD) > 0;
    }

    public String getExplanation() {
        return "High amount flagged by rule engine.";
    }

    public RiskIndicator getRiskIndicator() {
        return RiskIndicator.HIGH_AMOUNT;
    }
}

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🧠 AI + Rule Engine = Power Combo

Let’s say:

• tanaji gives a score of 0.92 and recommends REJECT

• dadoji runs and says "Wait, it’s high amount, but merchant is whitelisted"

→ The system now has explainable override logic
→ Logs every decision
→ Helps humans audit, trust, and refine the system

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🛡️ Trivia: Who Was Dadoji Konddev?

• 📜 He was appointed as administrator of Pune Jagir under Shahaji (Shivaji’s father).

• 🧠 Played a formative role in Shivaji’s education, strategy, and administrative foundations.

• 🏹 Ran the estate with fairness and discipline — no emotion, just principle.

• 🏛️ A rare combination of mentor, manager, and tactician.

In our system, Dadoji is the guardian of fairness, applying laws and boundaries before emotions or instinct kick in.

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🔍 When Rules Beat AI

Real-world examples:

• AI thinks it’s safe? Business says “No international payments on weekends.”

• AI misses pattern? Rule says “Block transactions from blacklisted IPs.”

By combining these two, we get:

• ✅ Predictability

• ✅ Customizability

• ✅ Trustworthy explainability

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✅ What’s Done

• Java-based modular Rule System

• Interfaces to add custom rules easily

• Enum-driven RiskIndicator system

• Seamless integration with sindhudurg response

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🧱 What’s Next?

🎯 We’ll soon work on santaji — the feedback loop.
It will learn from false positives/negatives and feed back into tanaji.

After that:

• 📩 Real-time alerts (via push/SMS)

• 🛡️ Security hardening

• 📊 Admin dashboard for observability

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🧠 Want to Try Adding a Rule?

Clone the repo and add your own:

java

public class VelocityCheckRule implements FraudRule {
   // Flag too many transactions in short time
}

We'll even accept solid rule contributions via PR!

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🔗 GitHub Repo

📦 https://github.com/pcm1984/fraud-free-swarajya

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💬 Closing Thought

Shivaji Maharaj needed Tanaji on the battlefield...
...but he needed Dadoji to prepare him for it.

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