Taming the Chaos: The Great Dialgen.AI Backend Rebuild 🔧

The system was working. But barely. 😅

It was messy, kinda hardcoded, and glued together with trial-and-error. Switching STT or TTS felt like defusing a bomb. And don't get me started on cross-talk issues! All these things were noticed when we started to test what we had built extensively.

Picture this: the agent suddenly started talking random things, and that's when I noticed one of my team was talking to another instance of the agent! The agent was giving responses for MY questions to MY TEAM and responses meant for him to ME. 🤦‍♂️ Classic cross-talk disaster!

Soon I started to debug the code and quickly realized—it's about time for maturing the system, not just making it work. I spent long hours understanding the code (occasionally asking Aniz questions on how stuff worked, and of course bugging Claude non-stop 😂).

After drowning in code for days, I had this lightbulb moment: we needed to be model-agnostic for STT and TTS. It was a necessity! We needed a more robust, scalable architecture pattern for the backend to easily swap between different models without rewriting entire chunks of code.

Design Patterns to the Rescue!

So here's how I overcame this mess. Hold on tight—it's gonna get technically detailed (but I promise to keep it fun)!

Building Model-Agnostic Voice AI Systems: My Technical Adventure 🧠

In the crazy-fast world of AI tech, building flexible systems isn't just nice—it's do-or-die essential. I recently implemented a voice AI agent architecture that can seamlessly plug into multiple speech-to-text (STT), text-to-speech (TTS), and language model providers. This model-agnostic design lets our system adapt to shiny new tech without requiring us to rewrite everything. Future-proofing FTW! 🙌

Problem Domain: The Technical Mess I Faced 😰

Building a voice AI system is WAY harder than it sounds:

• API Chaos: Different AI providers have completely different APIs, parameters, auth methods... you name it!
• Vendor Quirks: Each provider has unique behavior, error handling, and stream processing weirdness.
• Provider Roulette: The system needs to switch between providers on the fly.
• Component Communication: Getting STT, TTS, and LLM to talk to each other without creating spaghetti code.
• Technical Debt Risk: Hard-coding dependencies = future nightmare.

To fix all this, I went pattern-crazy with an architecture focused on separation of concerns, polymorphic interfaces, and runtime composition. Fancy words for "making stuff plug-and-play!" 🔌

The Tech Breakdown: Design Patterns and Why They Matter

1. Factory Pattern: Provider Magic ✨

The Factory pattern lets us create objects without exposing all the messy instantiation logic.

┌───────────────┐     creates      ┌───────────────┐
│ TTSFactory    │─────────────────▶│ ITTSProvider  │
└───────────────┘                  └───────┬───────┘
        │                                  │
        │                                  │
        │      ┌────────────────┐          │
        └─────▶│ ElevenLabsTTS  │◀─────────┘
        │      └────────────────┘          │
        │                                  │
        │      ┌────────────────┐          │
        └─────▶│ OpenAITTS      │◀─────────┘
        │      └────────────────┘          │
        │                                  │
        │      ┌────────────────┐          │
        └─────▶│ KokoroTTS      │◀─────────┘
               └────────────────┘

What I did:

• Created TTSFactory and STTFactory classes with static createProvider() methods
• Factory methods check the type parameter to decide which concrete implementation to create
• Adding new providers? Just add new case statements to the factory methods (so easy!) 🙌
• Factories handle all the provider-specific init details, keeping client code squeaky clean

Why it's awesome:

• Provider selection logic all in one place
• Super easy to add new providers
• Runtime provider switching? No problem!

2. Adapter Pattern: Making Everyone Speak the Same Language 🗣️

The Adapter pattern gives a unified interface to different implementations, converting provider-specific APIs into a standard contract. AKA making everyone play nice together!

                    ┌──────────────┐
                    │ ITTSProvider │
                    └──────┬───────┘
                           │
           ┌───────────────┼───────────────┐
           │               │               │
┌──────────▼─────┐ ┌───────▼─────┐ ┌───────▼──────┐
│ OpenAIAdapter  │ │ ElevenLabs  │ │ KokoroAdapter│
└──────────┬─────┘ └───────┬─────┘ └───────┬──────┘
           │               │               │
           │               │               │
┌──────────▼─────┐ ┌───────▼─────┐ ┌───────▼──────┐
│ OpenAI API     │ │ ElevenLabs  │ │ Kokoro API   │
└────────────────┘ └─────────────┘ └──────────────┘

What I did:

• Each adapter extends a base adapter class with common functionality
• Adapters handle provider-specific stuff like:
  • Authentication (API keys, tokens, etc.)
  • Communication (WebSockets, REST)
  • Stream processing and buffer management
  • Error handling and reconnection strategies
• Adapters translate our unified interface methods into provider-specific API calls

Why it's awesome:

• API complexity? Hidden away! 🙈
• Different protocols all work the same way
• Stream processing unified (this was a HUGE pain before)
• Provider-specific optimizations without breaking everything else

3. Interface Pattern: Making Everyone Follow the Rules 📏

The Interface pattern sets clear rules that all provider implementations must follow.

┌─────────────────────────────────────┐
│ <<interface>>                       │
│ ITTSProvider                        │
├─────────────────────────────────────┤
│ + initialize(): Promise<void>       │
│ + generate(text: string): void      │
│ + forceFlush(): Promise<void>       │
│ + setVoiceId(voiceId: string): void │
│ + emitCachedBuffer(key): boolean    │
└─────────────┬───────────────────────┘
              │
              │ implements
              │
┌─────────────▼───────────────────────┐
│ BaseTTSAdapter                      │
├─────────────────────────────────────┤
│ # config: TTSProviderConfig         │
│ # textAudioCache: Map<string,Buffer>│
│ + setVoiceId(voiceId: string): void │
│ + getCachedBuffer(key): Buffer      │
└─────────────┬───────────────────────┘
              │
              │ extends
              │
┌─────────────▼───────────────────────┐
│ OpenAITTSAdapter                    │
├─────────────────────────────────────┤
│ - openai: OpenAI                    │
│ + initialize(): Promise<void>       │
│ + generate(text: string): void      │
│ + forceFlush(): Promise<void>       │
└─────────────────────────────────────┘

What I did:

• Created ITTSProvider and ISTTProvider interfaces with method signatures that all providers MUST implement
• Made interfaces extend EventEmitter for event-based communication
• Used detailed TypeScript type definitions for parameters and return values
• Created abstract base classes with partial implementations

Why it's awesome:

• TypeScript catches implementation errors before runtime (saved my butt many times!) 🙏
• Forces all implementations to provide required methods
• Acts as documentation AND contract
• Can swap implementations without breaking client code
• Stable API while implementation details evolve

More Design Pattern Goodness

4. Template Method Pattern: Common Framework 🏗️

The Template Method pattern defines a skeleton algorithm in a base class, with specific steps handled by subclasses.

┌────────────────────────────────┐
│ BaseTTSAdapter                 │
├────────────────────────────────┤
│ + setVoiceId(id: string)       │
│ + emitCachedBuffer(key: string)│
│ + getCachedBuffer(key: string) │
│ # handleVoiceChange(id: string)│
└──────────────┬─────────────────┘
               │
    ┌──────────┴───────────┐
    │                      │
┌───▼────────────┐  ┌──────▼─────────┐
│ OpenAIAdapter  │  │ ElevenLabs     │
├────────────────┤  ├─────────────────┤
│ + initialize() │  │ + initialize()  │
│ + generate()   │  │ + generate()    │
│ + forceFlush() │  │ + forceFlush()  │
└────────────────┘  └─────────────────┘

What I did:

• Built abstract base classes with common functionality
• Implemented cache management, event handling, and config management in base classes
• Defined abstract methods for provider-specific behavior
• Created template methods that define algorithm structure

Why it's awesome:

• Code reuse! No more copy-paste madness 🚫
• Consistent behavior across providers
• Way less code needed for new provider adapters
• Clear extension points for customization

5. Observer Pattern: Event-Driven Communication 📡

The Observer pattern sets up a publish-subscribe system for communication, reducing tight coupling.

┌────────────────┐    transcription    ┌──────────────┐
│ STTProvider    │───event─────────────▶ LLM Service  │
└────────────────┘                     └──────────────┘
                                               │
                                               │ LLM reply event
                                               ▼
┌────────────────┐      speech         ┌──────────────┐
│ StreamService  │◀────event─────────── TTSProvider   │
└────────────────┘                     └──────────────┘

What I did:

• Made components extend EventEmitter
• Used events for cross-component communication (no more direct method calls!)
• Standardized event names and data structures
• Registered event handlers during system initialization

Why it's awesome:

• Loose coupling between components
• Natural support for async operations (crucial for AI services)
• Multiple components can listen to the same event
• Dynamic registration at runtime
• Flexible processing pipelines

6. Strategy Pattern: Mix and Match Algorithms 🔄

The Strategy pattern lets us select different algorithms (providers) at runtime.

                    ┌─────────────────┐
                    │ CallContext     │
                    └────────┬────────┘
                             │
     ┌───────────────────────┼───────────────────────┐
     │                       │                       │
┌────▼─────┐            ┌────▼─────┐           ┌─────▼────┐
│Strategy 1│            │Strategy 2│           │Strategy 3│
│ TTS 1    │            │ TTS 2    │           │ TTS 3    │
└──────────┘            └──────────┘           └──────────┘

What I did:

• Made the CallContext class accept different provider implementations at construction
• Enabled provider selection based on config, user preferences, or runtime conditions
• Had CallContext interact with providers through interfaces only
• Used configuration-driven provider selection

Why it's awesome:

• Runtime provider switching (crucial for testing!)
• Client code doesn't need to know implementation details
• Easy A/B testing of different providers
• Graceful fallback if a provider fails
• Feature differentiation without complex client code

7. Dependency Injection: Component Composition 🧩

Dependency Injection provides objects with their dependencies rather than having them create dependencies themselves.

┌──────────────────┐
│   CallContext    │
└────────┬─────────┘
         │ creates and injects
         ▼
┌──────────────────┐
│  Configuration   │
└────────┬─────────┘
         │
         ├───────────────┐
         │               │
         │               ▼
         │       ┌─────────────────┐
         │       │   TTSProvider   │
         │       └─────────────────┘
         │
         ├───────────────┐
         │               ▼
         │       ┌─────────────────┐
         │       │   STTProvider   │
         │       └─────────────────┘
         │
         └───────────────┐
                         ▼
                 ┌──────────────────┐
                 │   LLMProvider    │
                 └──────────────────┘
 

What I did:

• Had CallContext receive or create dependencies and inject them where needed
• Instantiated services based on configuration
• Accessed dependencies through interfaces, not concrete implementations
• Managed service lifetimes through the container

Why it's awesome:

• Components focus on their job without dependency creation headaches
• Super testable with mock dependencies
• System composition controlled through external config
• Centralized dependency management
• Proper lifecycle management

The Big Picture: How It All Works Together 🧠

When combined, these patterns create a flexible architecture that looks like this:

┌────────────────────────────────────────────────────────────────┐
│ CallContext                                                    │
├────────────────────────────────────────────────────────────────┤
│                         │                                      │
│  ┌─────────────┐   uses │   ┌────────────┐        ┌──────────┐ │
│  │ STTFactory  │────────────▶ STTAdapter │───────▶│  STT    │ │
│  └─────────────┘        │   └────────────┘        │ Provider │ │
│                         │      implements         └──────────┘ │
│                         │   ┌────────────┐                     │
│                         │   │ISTTProvider│                     │
│                         │   └────────────┘                     │
│                         │          ▲                           │
│                         │ events   │   events                  │
│                         │          │                           │
│ ┌──────────────┐  uses  │   ┌──────▼─────┐         ┌─────────┐ │
│ │ LLMFactory   │◀──────────┤  LLM       │────────▶│LLM      │ │
│ └──────────────┘        │   │  Adapter   │         │Provider │ │
│        │                │   └────────────┘         └─────────┘ │
│        │ events         │      implements                      │
│        ▼                │   ┌────────────┐                     │
│ ┌──────────────┐  uses  │   │ILLMProvider│                     │
│ │ TTSFactory   │────────────▶ TTSAdapter │───────▶┌─────────┐ │
│ └──────────────┘        │   └────────────┘         │TTS      │ │
│                         │      implements          │Provider │ │
│                         │   ┌────────────┐         └─────────┘ │
│                         │   │ITTSProvider│                     │
│                         │   └────────────┘                     │
│                         │                                      │
└────────────────────────────────────────────────────────────────┘

This architecture is like a LEGO set for voice AI—mix and match the pieces you need! 🧱

• Decoupled components operate independently
• Different providers swap in and out transparently
• Event-driven communication instead of direct dependencies
• System composition controlled through config
• Provider-specific complexity contained in adapters
• Common operations standardized
• Clear extension points for new providers

Why This Matters & My Technical Battle Scars 💪

This model-agnostic architecture gave us some serious advantages:

• Tech Flexibility: New AI models? Just implement adapters, no core logic changes needed! ✅
• Independent Scaling: Scale components separately based on load
• Testability: Test each component in isolation with mocked dependencies (saved us so much debugging time!)
• Performance Tweaking: Optimize providers without affecting other components
• Protocol Flexibility: Support different communication protocols through adapters
• Load Balancing: Use multiple providers simultaneously
• Backup Plans: Fall back to alternative providers if one fails
• Feature Exploration: Try new provider capabilities through interface extensions

The Challenges (AKA Things That Made Me Pull My Hair Out) 😱

Not gonna lie, this approach had some pain points:

• Interface Design Complexity: Finding the right balance of provider features without bloated interfaces
• Common Denominator Headaches: Standardizing functionality across very different providers
• Event Debugging Nightmares: Tracking down event-based bugs is HARD! 🔍
• Error Whack-a-Mole: Getting consistent error handling across providers
• Performance Overhead: Yes, adapters and events add some overhead
• Integration Testing Chaos: Testing all possible provider combos

But honestly, the benefits were totally worth the struggle! 💯

The Next Steps: From Design Patterns to a Polished Product! 🚀

So what's next after all this architectural refactoring and pattern implementation? Well, this model-agnostic foundation is just the beginning for Dialgen.AI!

With our architecture now being as pluggable as LEGO blocks (Factory Pattern FTW! 🏆), we're ready to take this product from "technically solid" to "market ready."

Our vision? To create a complete AI Calling Suite powered by AI Agents that's right at your fingertips:

ANY SCENARIO. ZERO DOWNTIME. HIGHLY CUSTOMIZABLE. 24/7 SERVICE.

The design patterns we implemented aren't just academic exercises—they're the backbone that will support everything we build next:

• More Provider Options: Thanks to our Adapter Pattern, adding new STT/TTS providers is now trivial! 🙌
• TypeScript Migration: With our interfaces clearly defined, moving to TypeScript is a natural next step
• Local Model Optimization: Now that our Template Method Pattern handles the common logic, we can focus on optimizing local TTS/STT without breaking everything
• Advanced LLM Integration: Our Strategy Pattern makes switching between different LLMs as easy as changing a config file
• Self-Healing System: With Observer Pattern in place, we can implement smart error recovery that automatically switches providers when one fails

If you're as tired of those soul-crushing IVR calls as Nisham was when we started this journey—and want to be among the first to experience AI-powered calls built on a rock-solid architecture—join our waitlist at Dialgen.AI.

We've built a flexible, future-proof system that can adapt to whatever the AI landscape throws at us next. And trust me, that's something 🔥. You'll want a front-row seat for this revolution!