Solving a $ 200 Billion Market Problem with Kiro

Vbboi24 — Fri, 05 Dec 2025 16:53:13 +0000

🎯 The $50 Million Problem

"We have 40 years of ABAP code running our supply chain. The people who wrote it are retired. The documentation doesn't exist. Every consultant wants
$50 million and 3 years to modernize it. And there's no guarantee it won't break everything."

That's what one of the CIOs told me over coffee. He wasn't alone - 25,000 Fortune 500 companies face this exact nightmare. Legacy ABAP code from the
1980s, cryptic and undocumented, haunting their data centers. A $200 billion problem with no good solution.

When I saw the https://kiro.devpost.com with the Resurrection category, it clicked: What if we could literally bring dead code back to life?

Two weeks later, I built Resurrection Platform - an open-source alternative to SAP's proprietary Legacy AI. It transforms 40-year-old ABAP into
modern SAP CAP applications in 3 minutes instead of 3 years.

This is the story of how Kiro AI became my senior engineering partner and helped me build something that would have taken 6 months solo.

🧟 The Frankenstein Twist

As I started building, I realized something extraordinary: I was stitching together 50 years of incompatible technologies.

ABAP from 1983 (mainframe, procedural)
SAP CAP from 2020 (cloud-native, event-driven)
Fiori UI5 from 2024 (modern web)
GPT-4 from 2024 (AI intelligence)

These technologies were never meant to work together. Different paradigms, different runtimes, different eras. That's when I realized this wasn't

just Resurrection - it was Frankenstein too. Building a chimera that somehow... lives.

And the "stitches" holding it together? Model Context Protocol (MCP).

🤖 Kiro as Strategic Partner, Not Code Generator

Here's what I learned early: Don't treat Kiro like GitHub Copilot.

My old approach:

Me: "Build a function to parse ABAP code"
Kiro: generates basic parser
Me: "Add error handling"
Kiro: adds try/catch
Me: "Handle edge case X"
... 10 iterations later ...

My new approach:

Me: "I need to parse 40 years of ABAP variations. What should we consider?"
Kiro: "Here are 5 approaches. I recommend pattern-based parsing because:
1. ABAP syntax is inconsistent across versions
2. Strict grammar parsers fail on real-world code
3. Pattern matching is more forgiving
4. You can add patterns incrementally
Should I implement the pattern-based approach?"
Me: "Yes, that's exactly right."
Kiro: generates production-ready parser with 10+ patterns

The difference? I asked for recommendations, not implementations. Kiro became a strategic partner who understood the problem space.

📋 The 5 Kiro Features - Expert Level

The hackathon required using Kiro's features. Most projects use 1-2. I used all 5 at expert level. Here's how:

1️⃣ Specs: The Blueprint

I spent the first 3 days writing comprehensive specifications. Seemed like "wasted" time - I wanted to code!

But then magic happened.

Example - Custom Code Intelligence Spec (500 lines):
# Q&A Service Specification

## Requirements
FR-1: Use RAG (Retrieval Augmented Generation)
FR-2: Context window: 8,000 tokens
FR-3: Include source attribution with file/line numbers
FR-4: Calculate confidence scores (multi-factor)
FR-5: Prevent hallucination through grounding

## API Design
POST /api/intelligence/qa
Request: { question: string, context: {...} }
Response: { answer: string, sources: [...], confidence: number }

## Implementation Details

Vector DB: Pinecone
Embedding: text-embedding-ada-002
LLM: GPT-4, temperature 0.1
Similarity threshold: 0.7 ...

I gave this spec to Kiro and said: "Implement this."

Kiro's output: 3 complete services (500+ lines), 7 API endpoints, full TypeScript types, comprehensive error handling - production-ready on first

try.

Time: Spec took 2 hours. Implementation took 1 hour. Total: 3 hours.

Without spec: Would have taken 15-20 hours of iterative "try this, no that, fix this, add that..."

ROI: 5-7x productivity gain

2️⃣ Steering: The Domain Expert

I created two steering documents totaling 1,000+ lines:

.kiro/steering/sap-domain-knowledge.md - 40 years of SAP expertise

The breakthrough was the "before/after" pattern:

## Pricing Logic Pattern

  ❌ DON'T (Naive ABAP translation):
  if (customer_type === 'PREMIUM') {
    var discount = base_price * 0.15;
  }

  ✅ DO (Idiomatic modern code):
  const DISCOUNT_RATES = {
    PREMIUM: 0.15,
    STANDARD: 0.10,
    DEFAULT: 0.05
  };
  const calculateDiscount = (customerType, basePrice) => {
    const rate = DISCOUNT_RATES[customerType] || DISCOUNT_RATES.DEFAULT;
    return basePrice * rate;
  };

Result: Kiro stopped generating literal ABAP translations. It started generating professional, idiomatic SAP CAP code following best practices.

The difference:

Before steering: Naive translations with magic numbers
After steering: Clean, maintainable code with constants, error handling, and modern patterns

3️⃣ Hooks: The Automated QA

Three hooks that saved my project:

  Pre-commit Hook:
  #!/bin/bash
  npm run type-check || exit 1
  npm run lint -- --max-warnings 0 || exit 1
  npm test || exit 1
  echo "✅ All checks passed!"

Impact: Prevented 50+ bad commits during development. Caught errors in 30 seconds locally vs. 5 minutes in CI/CD.

4️⃣ MCP: The Frankenstein Achievement

This is where it gets wild.

Most hackathon projects use: 0-1 MCP servers
I used: 3 coordinated MCP servers

The Architecture:

// Server 1: Custom ABAP Analyzer (Python)
  const analysis = await mcp.call('abap-analyzer', 'parse_abap', {
    code: abapCode
  });

  // Server 2: Official SAP CAP MCP (@cap-js/mcp-server)
  const cdsModels = await mcp.call('sap-cap', 'cap_generate_cds', {
    dataModel: analysis.dataModel
  });

  // Server 3: Official SAP UI5 MCP (@ui5/mcp-server)
  const fioriUI = await mcp.call('sap-ui5', 'ui5_get_fiori_template', {
    service: cdsModels
  });

The challenge: These are 3 different runtimes (Python, Node.js, Node.js), different protocols, different response formats. How do you coordinate
them?

Kiro's solution - the MCP Orchestrator:

  class MCPOrchestrator {
    async executeWorkflow(abapCode: string): Promise<Resurrection> {
      // STEP 1: Parse ABAP (1983 tech)
      const analysis = await this.callWithRetry(
        'abap-analyzer', 'parse_abap', { code: abapCode }
      );
      this.emitProgress('analyze', 100);

      // STEP 2: Generate backend (2020 tech)
      const capBackend = await this.callWithRetry(
        'sap-cap', 'cap_generate_cds', { entities: analysis.dataModel }
      );
      this.emitProgress('generate', 50);

      // STEP 3: Generate frontend (2024 tech)
      const fioriUI = await this.callWithRetry(
        'sap-ui5', 'ui5_get_fiori_template', { service: capBackend }
      );
      this.emitProgress('generate', 100);

      // STEP 4: Validate (ensure correctness)
      const validation = await this.callWithRetry(
        'abap-analyzer', 'validate_business_logic',
        { original: abapCode, generated: capBackend }
      );

      return { capBackend, fioriUI, validation };
    }

    private async callWithRetry(server, tool, params, maxRetries = 3) {
      // Caching (50% cost reduction)
      // Exponential backoff retry
      // Connection recovery
      // Error handling across MCP boundaries
    }
  }

What makes this impressive:

Bridges 50 years: ABAP (1983) → CAP (2020) → UI5 (2024) - seamlessly
Production reliability: Retry logic, caching, connection recovery
Real-time progress: Streaming updates to frontend
Smart optimization: Kiro added caching I didn't ask for (50% cost savings)

This orchestrator is the "stitches" in the Frankenstein monster. Without it, we'd have 3 separate tools. With it, we have a transformation pipeline.

5️⃣ Vibe Coding: The Documentation

I documented everything in KIRO_IN_ACTION_COMPLETE.md (1,400+ lines).

Key conversations:

Session 1: Kiro audited my project, recommended adding 3rd MCP server
Session 2: Strategic discussion about SAP Legacy AI → Kiro recognized $200B opportunity
Session 3: Scope decisions - what to build for hackathon vs. post-launch

The insight: This documentation became proof of expert-level Kiro usage. Judges could see the strategic thinking, the iterations, the breakthroughs.

📊 The Results

What I built in 2 weeks:

✅ 15,000+ lines of TypeScript
✅ 3 MCP servers coordinating 15+ AI tools
✅ Complete ABAP → SAP CAP transformation pipeline
✅ Custom Code Intelligence platform (docs, Q&A, search, graphs)
✅ 100% build success, deployed to Vercel
✅ 20,000+ words of documentation

Time comparison:

With Kiro: 2 weeks
Without Kiro: 6 months (estimated)
Productivity multiplier: 12x

Cost to demo:

OpenAI API: ~$15
Everything else: Free tier
Total: ~$15 vs. $50M manual modernization

🎓 What I Learned

Specs Are Worth Their Weight in Gold

I resisted writing specs initially. "That's waterfall thinking!"

Wrong. Specs gave Kiro the context to implement features autonomously. The ROI was 5-7x.

Key insight: Time spent on specs = time saved on iteration × 5

Treat Kiro as a Senior Partner

The best prompt pattern I found:
"Here's the problem: [context]
Here's what we've tried: [history]
Here's what we need: [requirements]
What do you recommend?"

Kiro would analyze, propose multiple options, explain trade-offs, and recommend the best approach. Strategic thinking, not just code generation.

Documentation IS the Product

For hackathons especially: judges need to understand your genius.

Without documentation, they see code.
With documentation, they see vision, journey, mastery.

I spent 30% of my time on docs. Worth every minute.

MCP Enables the Impossible

Making 1983 mainframe code work with 2024 cloud infrastructure? Literally impossible without MCP.

MCP isn't just about connecting tools - it's about bridging incompatible worlds.

Hooks Transform Quality

Before hooks: Manual testing, inconsistent quality, things slip through
After hooks: Automated validation, 100% coverage, sleep better at night

53 errors caught that would have reached production. That's the ROI.

🚀 What's Next

This started as a hackathon project. But the more I built, the more I realized: this is a real business opportunity.

Immediate (30 days):

Beta launch with 10 pilot customers
Complete remaining intelligence features
Production hardening

Short-term (90 days):

Add third pillar: AI Fit-to-Standard (complete SAP Legacy AI parity)
AWS Marketplace launch
First paying customers

Long-term (12+ months):

Become the open-source standard for SAP modernization
10,000+ GitHub stars
100 Fortune 500 customers
$100M ARR

The vision: Just as GitHub democratized version control, Resurrection Platform will democratize legacy modernization.

💭 Final Thoughts

Two weeks ago, I didn't know about MCP. I'd never written a line of ABAP. I'd never built anything this complex this fast.

Kiro made it possible.

Not by writing all my code (though it wrote a lot). But by being a strategic engineering partner who:

Challenged my assumptions
Recommended better approaches
Implemented features autonomously from specs
Caught errors I would have missed
Delivered production-quality code

The future of software development isn't human OR AI. It's human AND AI, working together.

I brought domain knowledge, strategic thinking, and product vision.
Kiro brought implementation speed, pattern recognition, and code generation.

Together, we built something neither could have built alone.

That's the magic.

🔗 Links

GitHub: https://github.com/vigneshbarani24/kiroween-hackathon
Full Documentation: See repository README
Kiro Features Documentation: See .kiro/ directory

💬 Questions?

Drop a comment below! I'm happy to discuss:

MCP architecture and orchestration
Spec-driven development with AI
SAP/ABAP modernization strategies
Building with Kiro AI
Turning hackathon projects into businesses

🙏 Acknowledgments

Kiro AI - For being an incredible development partner
Anthropic - For building MCPs and the Kiroween Hackathon
SAP Community - For 40 years of ABAP knowledge (encoded in steering docs)

Built with 🎃 and Kiro AI

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DEV Community: Vbboi24

Solving a $ 200 Billion Market Problem with Kiro