Case Studies

Deep Dive Projects

Comprehensive case studies of our most impactful projects. Architecture decisions, implementation details, code samples, and lessons learned.

NEXUS AI OS

Local-First AI Operating System

NEXUS AI OS is our most ambitious project — a local-first AI operating system that orchestrates 13+ specialized agents to handle personal, financial, health, home automation, and productivity tasks entirely on-device. No cloud dependency, no data leaving the machine, zero subscription fees.

35K+

Total Lines of Code

13+

Agent Count

API Endpoints

Docker Services

1.2s

Avg Query Latency

8GB

Memory Usage

72%

Test Coverage

99.2%

Uptime

Challenge

The traditional AI landscape relies on cloud APIs which compromise privacy and require ongoing costs. We needed to build a system that matches cloud AI quality while running 100% locally, handling diverse domains (finance, health, home, code), and maintaining sub-2-second response times.

Approach

We designed a multi-agent architecture where each domain has a specialized agent with its own prompt templates and tool access. An orchestrator agent performs intent classification and routes queries. All inference runs through Ollama with dynamic model selection based on system resources. Agent context is shared via ChromaDB vector store and Redis pub/sub IPC bus.

Architecture

Frontend Layer: React (web), Electron (desktop), React Native (mobile) sharing a common API client

API Gateway: FastAPI with async WebSocket connections for streaming responses

Orchestrator: Intent classification using Llama 3.1 8B for fast routing decisions

Specialist Agents: 13 agents each with domain-specific prompts, tools, and context

Memory Layer: ChromaDB for long-term memory, Redis for short-term session context

Model Serving: Ollama managing Llama 3.1 70B (complex), 8B (fast), CodeLlama 34B (code)

IPC Bus: Redis Pub/Sub for inter-agent communication with correlation IDs

Deployment: Docker Compose with 6 services (API, Ollama, ChromaDB, Redis, Web, Worker)

Implementation Timeline

Foundation

4 weeks

Agent Development

6 weeks

Frontend Platforms

4 weeks

Integration & Polish

3 weeks

Code Samples

Agent Orchestrator

python

The orchestrator classifies user intent and routes to the appropriate specialist agent.

class OrchestratorAgent(BaseAgent):
    def __init__(self):
        super().__init__(
            name="orchestrator",
            model="llama3.1:8b",
            system_prompt=ORCHESTRATOR_SYSTEM_PROMPT
        )
        self.agent_registry = AgentRegistry()
        self.ipc_bus = AgentIPCBus()
    
    async def process(self, query: str, context: dict) -> AgentResponse:
        # Step 1: Classify intent
        classification = await self.classify_intent(query)
        
        # Step 2: Route to specialist
        target_agent = self.agent_registry.get(classification.agent_id)
        
        # Step 3: Execute with context
        response = await target_agent.process(
            query=query,
            context={**context, **classification.extracted_params}
        )
        
        # Step 4: Post-process and return
        return self.format_response(response, classification)

IPC Message Bus

python

Redis-based inter-process communication for agent collaboration.

class AgentIPCBus:
    def __init__(self):
        self.redis = Redis(host='localhost', port=6379)
        self.context_db = ChromaDB(path='./agent_contexts')
    
    async def send_message(
        self, 
        from_agent: str, 
        to_agent: str, 
        payload: dict
    ):
        message = AgentMessage(
            sender=from_agent,
            receiver=to_agent,
            payload=payload,
            timestamp=datetime.utcnow(),
            correlation_id=uuid4()
        )
        await self.redis.publish(
            f"agent:{to_agent}", 
            message.model_dump_json()
        )
    
    async def request_collaboration(
        self, 
        initiator: str, 
        agents: list[str], 
        task: dict
    ) -> list[AgentResponse]:
        responses = []
        for agent_id in agents:
            await self.send_message(initiator, agent_id, task)
            response = await self.wait_for_response(
                agent_id, timeout=30
            )
            responses.append(response)
        return responses

Lessons Learned

•Dynamic model sizing is critical — Llama 70B is unnecessary for simple queries
•Redis Pub/Sub is excellent for agent IPC but needs correlation IDs for tracking
•ChromaDB chunk size significantly affects RAG retrieval quality
•Electron + React shares 95% of the web codebase with minimal platform-specific code
•Docker memory limits prevent OOM kills when running multiple LLM instances
•Prompt engineering is 80% of agent quality — good prompts beat bigger models

Future Roadmap

→NPU acceleration for Intel Core Ultra processors via OpenVINO
→Voice interaction with local Whisper speech-to-text model
→ESP32 IoT integration for true smart home AI
→Offline-first mobile experience with on-device inference
→Plugin marketplace for community-contributed agents
→Multi-language support (Hindi, Telugu, Spanish)

Results

1.2s

Average Response Time

Simple queries

4.5s

Complex Query Time

Multi-agent tasks

8GB RAM

Memory Baseline

16GB recommended

12GB

Model Storage

For all models

Concurrent Agents

Simultaneous processing

100%

Zero Cloud Dependency

All local

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class OrchestratorAgent(BaseAgent): def __init__(self): super().__init__( name="orchestrator", model="llama3.1:8b", system_prompt=ORCHESTRATOR_SYSTEM_PROMPT ) self.agent_registry = AgentRegistry() self.ipc_bus = AgentIPCBus() async def process(self, query: str, context: dict) -> AgentResponse: # Step 1: Classify intent classification = await self.classify_intent(query) # Step 2: Route to specialist target_agent = self.agent_registry.get(classification.agent_id) # Step 3: Execute with context response = await target_agent.process( query=query, context={**context, **classification.extracted_params} ) # Step 4: Post-process and return return self.format_response(response, classification)

class AgentIPCBus: def __init__(self): self.redis = Redis(host='localhost', port=6379) self.context_db = ChromaDB(path='./agent_contexts') async def send_message( self, from_agent: str, to_agent: str, payload: dict ): message = AgentMessage( sender=from_agent, receiver=to_agent, payload=payload, timestamp=datetime.utcnow(), correlation_id=uuid4() ) await self.redis.publish( f"agent:{to_agent}", message.model_dump_json() ) async def request_collaboration( self, initiator: str, agents: list[str], task: dict ) -> list[AgentResponse]: responses = [] for agent_id in agents: await self.send_message(initiator, agent_id, task) response = await self.wait_for_response( agent_id, timeout=30 ) responses.append(response) return responses