Introduction

Presence emerged as a digital twin platform that creates an AI voice agent with your voice and knowledge, enabling natural conversations through text or voice. Our team constructed this solution in just 6 hours during the Microsoft Reactor Open Source AI Agents Hackathon, securing first place through systematic execution and technical innovation.

The core value proposition of Presence lies in its ability to create a digital twin that represents you continuously. This digital representation handles repetitive inquiries and maintains your presence when you're unavailable for direct communication—a critical capability for creators, professionals, and businesses seeking continuous representation without constant availability.

Our technical architecture integrates ElevenLabs for voice cloning, Claude for contextual understanding, Whisper for speech recognition, FastAPI for backend services, and Flask for frontend delivery. The decisive advantage came from our AI-powered development methodology that compressed weeks of work into hours.

This breakdown dissects our architecture, implementation decisions, and development methodology that produced a production-ready application in a compressed timeframe. The techniques apply broadly beyond voice agents to any complex software development challenge requiring rapid execution without sacrificing quality.

The Technical Challenge

The Microsoft Reactor Hackathon imposed a 6-hour constraint for creating open-source AI agents with practical applications. This time compression created the central tension in our development process.

Voice-enabled AI agents with knowledge integration typically demand weeks of development time. The scope encompassed architecture design, voice cloning implementation, natural language understanding, document processing, real-time streaming, interface development, and system integration—each representing substantial technical challenges in isolation, let alone as an integrated whole.

We approached this constraint through ruthless prioritization and technical selection based on implementation speed versus quality impact. This forced a systems-thinking approach where we decomposed the problem into components with clear interfaces, enabling parallel development and incremental integration.

System Architecture

The architecture emerged from a need for component isolation and parallel development. We designed a modular system with distinct boundaries between the Flask frontend, FastAPI backend, ElevenLabs voice services, Claude language processing, Whisper speech recognition, and our custom document processing pipeline.

Data flows through the system in a clear sequence: users provide documents and voice samples through an interactive interview process. The system processes these inputs to create a knowledge base and voice profile. When conversations begin, speech inputs transform to text via Whisper, Claude generates contextual responses drawing from the knowledge base, ElevenLabs synthesizes these responses in the user's voice, and the system delivers this audio back to the visitor.

This architectural approach wasn't merely technical—it reflected our team dynamics and development constraints. Component isolation enabled independent work streams, reduced integration complexity, and created natural checkpoints for incremental testing. The clean separation of concerns proved essential for maintaining development velocity under extreme time pressure.

AI-Powered Development Methodology

The transformative element in our approach came from AI-powered development acceleration. Traditional development cycles for systems of this complexity typically span days or weeks—we compressed this to 6 hours through systematic application of AI assistance.

Aider served as our AI coding assistant throughout the process. We established a shared repository with explicit component boundaries, created comprehensive system design documentation for context, and developed consistent prompt patterns that yielded predictable, high-quality code generation.

Our workflow evolved into a repeatable pattern: we defined component requirements and interfaces first, generated initial implementations with AI assistance, reviewed and refined the generated code, integrated with adjacent components, then tested and iterated as needed. This methodical approach maintained quality while dramatically reducing implementation time.

The efficiency gains proved substantial. The WebSocket handler for real-time TTS streaming required approximately 3 minutes to generate with AI assistance—a task that would typically consume 2-3 hours of traditional development time. The generated code functioned at near-production quality, requiring only minor adjustments for permanent data storage and deployment configurations.

Effective prompt engineering emerged as a critical skill. We discovered that starting with interface definitions before implementation details, providing concrete input/output examples, explicitly stating error handling requirements, and referencing stylistically similar components consistently produced superior results.

Task decomposition became the foundation of our approach. By breaking complex features into smaller, well-defined components with clear interfaces, we enabled independent generation and parallel development across the team. This decomposition strategy allowed us to maintain multiple simultaneous AI coding sessions, each focused on discrete system elements.

Technical Implementation Deep Dive

Voice Cloning with ElevenLabs

The voice cloning implementation demanded precise API integration with ElevenLabs:

def text_to_speech(text, voice_id):
    """Convert text to speech using ElevenLabs API."""
    client = ElevenLabs(api_key=ELEVENLABS_API_KEY)
    
    audio = client.text_to_speech.convert(
        text=text,
        voice_id=voice_id,
        model_id="eleven_monolingual_v1",
        output_format="mp3_44100_128"
    )
    
    # Process audio data and return bytes
    return audio

Performance optimization focused on three key areas: selecting the most efficient audio format (mp3_44100_128) to balance quality and size, implementing streaming for longer responses to reduce perceived latency, and caching frequently used phrases to minimize redundant API calls.

Claude Integration for Natural Language Understanding

Claude provided the contextual intelligence for our AI agents:

def generate_text_with_anthropic(prompt, max_tokens=300):
    """Generate text using Anthropic's Claude API."""
    client = anthropic.Anthropic(api_key=ANTHROPIC_API_KEY)
    
    message = client.messages.create(
        model="claude-3-opus-20240229",
        max_tokens=max_tokens,
        system="You are a helpful assistant that provides concise, informative responses.",
        messages=[{"role": "user", "content": prompt}]
    )
    return message.content[0].text

We enhanced Claude's performance through context window management that dynamically included relevant document knowledge, response streaming for faster initial response perception, and prompt engineering techniques that maintained consistent agent personality across interactions.

Speech Recognition with Whisper

Speech-to-text conversion relied on OpenAI's Whisper model:

def speech_to_text(self, audio_file: Union[str, BinaryIO]) -> str:
    """Convert speech in an audio file to text using OpenAI's Whisper model."""
    # Handle file path vs file object
    if isinstance(audio_file, str):
        with open(audio_file, "rb") as f:
            transcript = self.openai_client.audio.transcriptions.create(
                model="whisper-1", file=f)
    else:
        transcript = self.openai_client.audio.transcriptions.create(
            model="whisper-1", file=audio_file)
    
    return transcript.text

Latency reduction strategies included preprocessing audio to remove silence, selecting the smallest appropriate Whisper model for the task, and implementing chunked processing for longer audio inputs.

API-Based Communication Implementation

Real-time communication formed the backbone of natural conversation experiences. Our API-based approach handled the voice conversation flow:

@app.post("/api/conversation")
async def process_conversation(request: ConversationRequest):
    """Process conversation text and return audio response."""
    try:
        # Extract request data
        text = request.text
        voice_id = request.voice_id
        
        # Generate response using Claude
        response_text = generate_text_with_anthropic(
            prompt=text, 
            context=get_relevant_context(text, request.agent_id)
        )
        
        # Convert to speech
        audio_data = text_to_speech(
            text=response_text,
            voice_id=voice_id
        )
        
        # Return response
        return {
            "text": response_text,
            "audio": base64.b64encode(audio_data).decode("utf-8"),
            "status": "success"
        }
    except Exception as e:
        return {"error": str(e), "status": "error"}

The implementation incorporated comprehensive error handling, context management for relevant knowledge retrieval, and efficient audio encoding for client delivery.

Document Processing Pipeline

Knowledge integration relied on our document processing pipeline:

def parse_document(self, file_path: Union[str, BinaryIO], file_extension: Optional[str] = None) -> str:
    """Parse a document and extract its text content."""
    # Get file extension
    if isinstance(file_path, str):
        _, ext = os.path.splitext(file_path)
        ext = ext.lower()
    elif file_extension:
        ext = file_extension.lower()
    else:
        raise ValueError("File extension must be provided for file-like objects")
    
    # Get the appropriate parser function
    if ext not in self.supported_extensions:
        raise ValueError(f"Unsupported file type: {ext}")
    
    parser_func = self.supported_extensions[ext]
    return parser_func(file_path)

Knowledge retrieval mechanisms included generating embeddings for document chunks, implementing semantic search for contextually relevant information, and employing a sliding window approach for dynamic context management during conversations.

Implementation Challenges

The compressed development timeline surfaced significant technical challenges requiring pragmatic solutions. The central tension emerged between quality and speed—particularly acute in voice synthesis where perceptual quality directly impacts user experience.

Performance analysis revealed several system bottlenecks. ElevenLabs API calls introduced the most significant latency, document processing exhibited linear scaling with document size creating potential performance cliffs with large knowledge bases, and maintaining consistent response quality across varied inputs demanded sophisticated prompt engineering.

We faced a critical tradeoff between voice quality and processing time. After analyzing the judging criteria and user experience implications, we deliberately chose the highest quality voice model despite its increased latency cost. This decision recognized that voice naturalness represented a core evaluation metric and differentiating feature, justifying the performance tradeoff.

Technical Lessons & Best Practices

The development process yielded substantive insights about AI-powered development methodologies. AI-generated code demonstrated surprisingly high quality but exhibited predictable weaknesses in edge case handling. Approximately 80% of generated code functioned correctly without modification, while 20% required targeted refinement—a ratio that dramatically accelerates development compared to traditional approaches.

Integration points emerged as the primary source of complexity and potential failure. The interfaces between independently generated components demanded particular attention, reinforcing the value of clearly defined contracts before implementation begins. This interface-first approach proved essential for maintaining system coherence despite the parallel development streams.

AI assistance demonstrated domain-specific strengths and weaknesses. It excelled at generating boilerplate code, implementing standard patterns, and creating API integrations—tasks characterized by well-established conventions. Manual coding remained superior for novel algorithms, complex business logic, and performance-critical sections where nuanced understanding of tradeoffs becomes essential.

Our testing strategy adapted to the compressed timeline through ruthless prioritization. We focused on critical path testing with minimal coverage, concentrating verification efforts on integration boundaries and user-facing features. This approach accepted calculated technical debt in exchange for development velocity.

Error handling demanded a systematic approach across the codebase. We established consistent patterns for all components, implemented detailed logging for diagnostic capability, and designed graceful degradation mechanisms that maintained core functionality during partial failures.

Prompt engineering emerged as a crucial skill that directly impacted output quality. The most effective prompts specified clear component boundaries, defined expected interfaces explicitly, included concrete usage examples, and articulated specific error handling requirements. The quality differential between naive and well-crafted prompts proved substantial.

Component isolation through strict separation of concerns enabled both parallel development and simplified integration. This architectural discipline created natural boundaries for AI-assisted generation tasks and reduced the cognitive load during system assembly.

Replicable Technical Framework

Our experience crystallized into a replicable framework for AI-powered development applicable across diverse project domains. The environment setup phase requires deliberate preparation: configuring version control with explicit component directories that mirror architectural boundaries, deploying an AI coding assistant like Aider with appropriate context, and establishing shared prompt templates that encode best practices.

Architecture definition becomes the critical foundation. The process must begin with clearly articulated component boundaries and interfaces, comprehensive documentation of data flow patterns and integration points, and explicit API contracts established before implementation begins. This upfront investment pays dividends through reduced integration friction and parallel development capacity.

The development workflow follows a consistent pattern: interface definition establishes the contract, AI assistance generates implementation conforming to that contract, human review identifies edge cases and performance concerns, integration connects the component to adjacent systems, and targeted testing verifies critical paths. This sequence balances velocity with quality control.

Prompt engineering patterns evolved significantly throughout our development process. Structured templates emerged as particularly effective: "Create a [component] that [function] with [specific requirements]" and "Implement an interface for [functionality] that accepts [inputs] and returns [outputs]". These formulations consistently outperformed vague or open-ended requests by constraining the generation space appropriately.

Code review processes adapted to focus on AI-specific concerns. Reviews prioritized edge case handling, verified interface compliance, evaluated security implications, and enforced consistent error handling patterns. This targeted approach recognized that AI-generated code exhibits different failure modes than human-written code.

The framework transcends the specific domain of AI voice agents. It applies equally well to web applications, data processing systems, automation tools, and other software domains where development velocity creates competitive advantage without sacrificing maintainability or quality.

Future Technical Roadmap

Our hackathon implementation, while functional and impressive, revealed clear opportunities for technical enhancement. Architecture improvements will focus on three key areas: adopting a microservice approach for independent scaling of components, implementing containerization for deployment flexibility across environments, and introducing a comprehensive caching layer to address the identified performance bottlenecks.

The Microsoft Reactor Hackathon victory with Presence validates AI-powered development as a transformative methodology. The ability to create a functional, sophisticated product in 6 hours rather than weeks represents a step-change in development economics and capabilities.

Feature enhancements will extend the core capabilities: multi-language support to expand addressable markets, emotion detection with response adaptation for more natural interactions, calendar integration for practical scheduling applications, and multi-agent interaction capabilities for complex use cases. Performance optimization efforts will target client-side audio processing to reduce latency, document indexing improvements for faster knowledge retrieval, and strategic response caching for frequently requested information.

Security considerations demand attention for production deployment. The roadmap includes end-to-end encryption for voice data to protect sensitive communications, fine-grained access controls for enterprise deployment scenarios, and compliance features addressing the specific requirements of regulated industries.

Tomorrow's demonstration will showcase building a full-stack web application in 60 minutes using these same AI-powered development techniques. This practical session aims to provide an accessible entry point for developers interested in adopting these methods in their own workflows.

For developers seeking deeper understanding of these AI-powered development techniques, my course on Agentic Engineering with Aider teaches the specific methods that enabled our 6-hour development cycle for Presence.

What's Coming This Week

This week marks the beginning of a daily build series focused on AI-powered development. Each day will feature construction of a new AI-powered project from scratch, demonstrating the practical application of these techniques across different domains. These demonstrations will reveal how AI transforms traditional development processes, compressing development cycles and enabling rapid creation of functional applications. The content serves both seasoned developers looking to enhance their workflow and newcomers exploring AI-assisted coding for the first time. The Microsoft Hackathon victory represents just the initial validation of an approach that fundamentally changes development economics.

Resources & References

• GitHub Repository: https://github.com/technolifts/presence - Complete codebase for examining implementation details
• Aider Documentation: https://aider.chat/docs/ - Resources for AI-assisted development
• ElevenLabs API: https://docs.elevenlabs.io/ - Documentation for voice synthesis
• Anthropic Claude API: https://docs.anthropic.com/ - Reference for language understanding
• OpenAI Whisper: https://platform.openai.com/docs/guides/speech-to-text - Documentation for speech recognition
• FastAPI Documentation: https://fastapi.tiangolo.com/ - Resources for backend implementation

Example Aider Commands

# Create a WebSocket handler for real-time TTS streaming
aider "Create a WebSocket handler for text-to-speech streaming with voice_id configuration, 
audio chunk streaming, and error handling."
 
# Implement document processing pipeline
aider "Create a DocumentParser class that handles PDF, DOCX, TXT, and MD files with a unified interface."
 
# Generate FastAPI backend and Flask frontend
aider "Create a FastAPI backend with endpoints for voice cloning, text-to-speech, document upload, 
agent listing, and chat functionality, plus a Flask frontend with templates."

Special thanks to my teammates Ryan Carroll, Michael Zhang, and Victor Nova for their collaborative work, and to Microsoft Reactor for creating this catalyst event.