Enterprise Archaeology - Equilateral AI

The Digital Dig Site

Every mature enterprise system is an archaeological site. Layers of business logic accumulated over decades, each representing the technological and organizational context of its time. Like actual archaeologists, we must carefully excavate these systems to understand their structure, purpose, and the civilizations of developers who built them.

The code you're debugging was written by someone who knew something you don't know, in a context you don't understand, solving a problem you're not aware of.

Enterprise archaeology isn't just about understanding legacy code - it's about reconstructing the business rules, technical constraints, and organizational pressures that shaped the system over time.

Layers of Software Sediment

The Geological Timeline

Enterprise systems accumulate in distinct technological eras:

The Mainframe Era (1970s-1990s): COBOL, FORTRAN, centralized processing
The Client-Server Era (1990s-2000s): Three-tier architecture, stored procedures, fat clients
The Web Era (2000s-2010s): N-tier architecture, service-oriented design, early cloud
The Mobile/Cloud Era (2010s-2020s): Microservices, APIs, cloud-native design
The AI Era (2020s-present): ML integration, AI-assisted development, intelligent automation

Archaeological Evidence in Code

-- Year 2001: Y2K remediation artifacts
CREATE TABLE customer_backup_y2k (
  cust_id CHAR(8),
  birth_year CHAR(4), -- Fixed from CHAR(2) in 2000
  -- Original table had YY format, causing Y2K issues
  
-- Year 2008: Financial crisis business rule changes  
-- CONSTRAINT chk_credit_limit CHECK (credit_limit <= 50000), -- Removed 2008-09-15
  
-- Year 2012: GDPR preparation (implemented 6 years early)
  gdpr_consent_date DATE, -- Added 2012-03-15, required by 2018
  
-- Year 2020: COVID remote work modifications
  remote_access_flag CHAR(1) DEFAULT 'N' -- Emergency addition 2020-03-16
);
          

Excavation Techniques

The Code Stratigraphy Method

Understanding system layers through systematic analysis:

# Archaeological git analysis
# Find the oldest code still in active use
git log --follow --format="%ai %an %s" --reverse src/core/BusinessRules.java | head -10

# Identify major architectural shifts
git log --oneline --grep="refactor\|migration\|upgrade" --since="2015-01-01"

# Find abandoned architectural decisions
git log --oneline --grep="TODO\|FIXME\|HACK" --since="2020-01-01"
          

The Business Logic Forensics Approach

Reconstructing business rules from implementation artifacts:

// Archaeological evidence of business rule evolution
public class PricingEngine {
    public BigDecimal calculatePrice(Product product, Customer customer) {
        BigDecimal basePrice = product.getBasePrice();
        
        // 2003: Original volume discount system
        if (customer.getAnnualVolume() > 100000) {
            basePrice = basePrice.multiply(new BigDecimal("0.90"));
        }
        
        // 2007: VIP customer tier (merger with CompanyX)
        if (customer.getTier().equals("VIP")) {
            basePrice = basePrice.multiply(new BigDecimal("0.85"));
        }
        
        // 2012: Geographic pricing adjustments (EU expansion)
        if (customer.getRegion().equals("EU")) {
            basePrice = basePrice.multiply(new BigDecimal("1.20")); // VAT implications
        }
        
        // 2018: Regulatory compliance pricing
        // Can't discount below cost due to antitrust ruling
        BigDecimal minPrice = product.getCost().multiply(new BigDecimal("1.05"));
        return basePrice.max(minPrice);
    }
}
          

The Dependency Archaeology Method

Mapping the invisible relationships that hold systems together:

Database Dependencies: Foreign keys, triggers, stored procedures
Integration Dependencies: API contracts, message formats, file exchanges
Runtime Dependencies: Shared libraries, configuration files, environment variables
Tribal Knowledge Dependencies: Manual processes, institutional memory, documentation gaps

Common Archaeological Discoveries

The Ghost Tables

Database tables that are still populated but no longer used by application code:

-- Table appears in nightly ETL but hasn't been queried by apps since 2019
SELECT table_name, last_analyzed 
FROM user_tables 
WHERE table_name LIKE '%_STAGING_%'
  AND last_analyzed < DATE '2020-01-01';

-- Often contains valuable historical data or expensive ETL processes
-- that can safely be archived
          

The Configuration Fossils

Settings that made sense in a different technological context:

# config.properties - Archaeological layers visible
# 2005: Original single-server deployment
server.max.connections=50

# 2010: Load balancer addition (forgot to update connection pools)  
# server.max.connections=200  # Commented out but never removed

# 2015: Cloud migration (still using on-premise values)
server.max.connections=50  # Should be 500+ in cloud environment

# 2020: Kubernetes deployment (pod-level limits override this anyway)
# This setting is now effectively meaningless but still read by code
          

The Business Rule Mutations

Logic that evolved through incremental changes without overall design:

// 8 years of incremental business rule changes
function calculateDiscount(customer, order) {
    let discount = 0;
    
    // Original rule: 5% for orders over $1000
    if (order.total > 1000) discount = 0.05;
    
    // 2017: Special handling for legacy customers (CompanyY acquisition)
    if (customer.legacy_flag && order.total > 500) discount = 0.07;
    
    // 2019: Seasonal promotion logic (never removed)
    if (isHolidaySeason()) discount = Math.max(discount, 0.10);
    
    // 2020: COVID business continuity discounts  
    if (customer.industry === 'healthcare') discount = Math.max(discount, 0.15);
    
    // 2021: Supply chain adjustment (temporary became permanent)
    if (order.hasBackorderedItems()) discount = Math.min(discount, 0.03);
    
    // 2023: AI-recommended customer retention discount
    if (customer.churnRisk > 0.7) discount = Math.max(discount, 0.12);
    
    return discount; // Nobody knows the exact business logic anymore
}
          

Modernization Strategies

The Museum Approach

Some systems are too valuable to change and too risky to replace:

Preserve in Place: Maintain exactly as-is with minimal changes
Build Protective Layers: APIs and wrappers to isolate from modern systems
Document Extensively: Capture tribal knowledge before it's lost
Plan for Succession: Identify eventual replacement timeline

The Archaeological Restoration Approach

Carefully modernizing while preserving essential characteristics:

// Modern wrapper preserving legacy business logic
class LegacyPricingService {
  private legacyEngine: any; // The original implementation
  
  async calculatePrice(product: Product, customer: Customer): Promise {
    // Step 1: Data transformation to legacy format
    const legacyProduct = this.toLegacyProduct(product);
    const legacyCustomer = this.toLegacyCustomer(customer);
    
    // Step 2: Use original business logic (don't fix what works)
    const legacyPrice = this.legacyEngine.calculatePrice(legacyProduct, legacyCustomer);
    
    // Step 3: Transform result to modern format
    return this.toModernPrice(legacyPrice);
  }
  
  // Preserve exact business logic while improving interface
}
          

The Selective Excavation Approach

Extract valuable business logic while leaving infrastructure in place:

Business Rule Mining: Extract complex calculations and validations
Data Pattern Recognition: Understand data relationships and constraints
Integration Pattern Preservation: Maintain external API contracts
Performance Characteristic Documentation: Understand what makes it fast/slow

The Tribal Knowledge Problem

Capturing Institutional Memory

The most valuable archaeological artifacts aren't in the code - they're in people's heads:

"Oh, you can't change that field. It's connected to the mainframe batch job that runs every Tuesday. If it fails, payroll doesn't work. Dave knows how to restart it, but he's retiring next month."

Knowledge Extraction Techniques

Shadow Documentation: Follow experts around and document their tribal knowledge
Incident Post-Mortems: When systems break, understand why someone knew how to fix them
Code Walkthrough Sessions: Have experts explain not just what code does, but why it exists
Business Context Interviews: Understand the business changes that drove technical decisions

Creating Archaeological Documentation

# System Archaeological Report: Customer Management System

## Excavation Summary
- **System Age:** 23 years (initial development: 2001)  
- **Major Renovations:** 2008 (SOA), 2015 (Cloud), 2020 (API Gateway)
- **Archaeological Complexity:** High - spans 4 technological eras

## Significant Artifacts
- **Business Rule Concentration:** PricingEngine.java (2,400 lines, 8 years of changes)
- **Data Architecture Fossils:** 47 "temporary" tables from 2008 migration still in use
- **Integration Archaeology:** SOAP services wrapped in REST wrappers for legacy compatibility

## Modernization Recommendations
1. **Museum Preservation:** Core pricing logic - too complex and risky to change
2. **Selective Excavation:** Extract customer validation rules to microservice  
3. **Infrastructure Modernization:** Replace data access layer, preserve business logic

## Tribal Knowledge Holders
- **Dave Martinez:** Batch processing systems, mainframe integration (retiring Q2 2025)
- **Sarah Chen:** Pricing rule evolution, regulatory compliance context
- **Legacy Vendor:** CompanyX integration details (contract expires 2026)
          

AI-Assisted Archaeological Tools

Code Pattern Recognition

AI can help identify archaeological patterns humans might miss:

Business Rule Clustering: Group related logic scattered across files
Dead Code Detection: Identify code paths that are no longer reachable
Architectural Layer Analysis: Map system layers and their evolution
Dependency Risk Assessment: Identify fragile integration points

Historical Context Reconstruction

Using AI to correlate code changes with business context:

# AI-assisted archaeological analysis
def analyze_code_evolution(file_path):
    commits = git_history(file_path)
    business_events = get_business_timeline()
    
    for commit in commits:
        # Correlate code changes with business events
        context = find_business_context(commit.date, business_events)
        
        # AI analysis of change patterns
        change_type = classify_change(commit.diff)
        business_impact = assess_business_impact(commit.diff, context)
        
        print(f"{commit.date}: {change_type} - {business_impact}")
        # Example output:
        # 2008-09-15: Emergency Business Rule Change - Financial Crisis Response
        # 2020-03-16: Infrastructure Scaling - COVID Remote Work Surge
          

The Future of Archaeological Practice

Proactive Archaeology

Instead of reactive excavation, modern teams practice proactive archaeology:

Architectural Decision Records: Document why decisions were made
Business Context Tagging: Link code changes to business drivers
Deprecation Timelines: Plan archaeological preservation before building
Knowledge Transfer Protocols: Prevent tribal knowledge from forming

The Archaeological Mindset

Every line of code you write today will be archaeological evidence tomorrow:

Write code as if the person who maintains it is a violent archaeologist who knows where you live.

The archaeological mindset transforms how we approach legacy systems - from obstacles to overcome to historical treasures to preserve and understand.

🏛️ Enterprise Archaeology