Three months ago, I sat with the Head of Engineering at a major e-commerce platform. Their architecture:
"We tried GitHub Copilot," he said. "It's great for writing functions, but useless for understanding our system. It can't even tell me which services call our payment API."
He pulled up their architecture diagram. It looked like someone had thrown spaghetti at a wall and called it a microservices architecture.
"When a developer asks 'How does checkout work?', the answer spans 30 repositories. No AI tool can handle that."
Challenge accepted.
Traditional AI coding tools are file-myopic. They see:
current_file.js
maybe_some_imports.js
That's like trying to understand New York City by looking at one apartment.
Real enterprise architecture looks like this:
📦 payment-service (Go)
↓ calls
📦 order-service (Java)
↓ publishes event
📦 inventory-service (Python)
↓ updates
📦 warehouse-api (Rust)
↓ triggers
📦 shipping-calculator (Node.js)
↓ notifies
📦 notification-service (C#)
↓ sends webhook
📦 analytics-pipeline (Scala)
Each arrow represents a network call, a potential failure point, a contract that must be maintained. Change one, break seven.
Traditional AI sees none of this.
Zencoder doesn't just index code—it understands relationships. We call this the "organizational brain": a living map of how your entire system connects, communicates, and depends on itself.
When you connect your repositories, Zencoder:
The magic isn't just in the indexing—it's in the inference.
When you ask "How does payment processing work?", here's what happens in 200ms:
Your Query: "How does payment processing work?"
↓
Step 1: Service Discovery
→ Finds: payment-service, stripe-adapter, payment-gateway
↓
Step 2: Flow Tracing
→ Maps: Frontend → API Gateway → Payment Service → Stripe → Database
↓
Step 3: Context Assembly
→ Gathers: Service code, API contracts, schemas, recent changes, errors, docs
↓
Step 4: Intelligent Response
→ AI sees the complete picture across all repositories
The result? Your AI doesn't just see the payment service—it sees:
All assembled, prioritized, and presented to the AI in under a second.
Remember that e-commerce platform with 487 repositories? Here's what happened when we deployed Zencoder:
# Initial setup
zen init --org "massive-ecommerce-corp"
zen index --all-repos --parallel 20
# Output
Discovering repositories... found 487
Parsing code structure... 2.3M files
Building service graph... 1,247 services identified
Mapping dependencies... 14,523 connections found
Indexing complete in 4 hours 23 minutes
Their lead architect's first test:
"Show me all services that would be affected if we change
the Order schema from v2 to v3"
Traditional approach: 3 architects, 2 days of analysis
Zencoder's response (8 seconds):
27 services directly affected:
- order-service (owns schema)
- payment-processor (reads order.total)
- inventory-manager (updates order.items)
- shipping-calculator (uses order.address)
[... 23 more with specific field usage]
43 services indirectly affected:
- customer-portal (displays orders)
- analytics-pipeline (aggregates order data)
[... 41 more with dependency paths]
Breaking changes detected:
- Field 'order.discount' removed (used by 12 services)
- Type change: order.items[].quantity (string→number, affects 8 services)
Recommended migration strategy:
Real metrics from their first week:
Debug Resolution Time:
New Feature Implementation:
Code Reviews:
Indexing millions of files across hundreds of repositories isn't trivial. Zencoder uses:
Result: After initial indexing, updates happen in seconds, not hours. Your AI always has the latest context.
Enterprises don't use one language. Zencoder supports:
# Supported languages with deep understanding
languages:
- JavaScript/TypeScript (including frameworks: React, Vue, Angular, Node)
- Java (Spring, Micronaut, Quarkus)
- Python (Django, FastAPI, Flask)
- Go (including goroutines and channels)
- C# (.NET Core, ASP.NET)
- Ruby (Rails)
- PHP (Laravel, Symfony)
- Rust
- Kotlin
- Swift
- Scala
# ... and 50+ more
Each language parser understands not just syntax, but patterns, frameworks, and idioms specific to that ecosystem.
Code changes every minute. Zencoder maintains accuracy through:
Your AI knows about changes before your CI/CD does.
After analyzing hundreds of enterprise architectures, patterns emerged:
In 73% of enterprises, we discover an average of 340 undocumented service dependencies. These are real connections in code that don't appear in any architecture diagram.
Example from a fintech client:
Documented: payment-service → risk-assessment
Reality: payment-service → risk-assessment
→ fraud-detection (undocumented)
→ user-profile (undocumented)
→ notification-queue (undocumented)
→ audit-logger (undocumented)
AI now knows about these. Developers often don't.
One line change can ripple through your entire system:
change_in('user-service/models/user.py')
→ affects('auth-service') # Direct import
→ affects('api-gateway') # Uses auth
→ affects('mobile-app') # Calls gateway
→ affects('analytics') # Tracks mobile events
→ affects('ml-pipeline') # Trains on analytics
→ affects('recommendations') # Uses ML models
# 1 line change → 6 services affected
Without multi-repo intelligence, you discover this in production.
Here's how to enable it for your organization:
Microservices are not independent. They're a distributed monolith with network calls as spaghetti.
But now your AI can see the whole plate of spaghetti. It can trace every strand, understand every connection, predict every break.
The question isn't whether you need multi-repo intelligence.
The question is: How are you surviving without it?
Try it yourself: Connect your repos at zencoder.ai/multi-repo-trial. Subscribe to the core plan for multi-repo intelligence.
Because your AI should understand your architecture as well as you do.
Better, actually.