Case Study — AI Application

Real-time voice + vision assistant for wardrobe styling and outfit recommendations.

Built by Alexander Dudnik

AI models orchestrated — STT, vision detection, LLM reasoning

C# / .NET

backend with segmented image processing

Real-time

voice + vision interaction loop

Modular

multi-agent architecture for extensibility

The challenge.

Fashion and wardrobe applications have a fundamental interaction problem: users need to describe what they're looking for while showing what they have. Typing "find me a blue blazer that goes with these grey trousers" while holding the trousers is clumsy. Existing apps force users into either voice OR image — never both in the same interaction.

The goal was to build an assistant that could hear a user's request, see their clothing, reason about compatibility, and respond with recommendations — all in a single real-time session. No app-switching. No uploading photos separately and then typing a query.

The specific problems

No unified voice + vision interaction model existed for wardrobe applications
Real-time inference required orchestrating STT, object detection, and LLM reasoning in sequence
Garment detection and segmentation needed to run locally (ONNX) for latency, with cloud fallback
Multi-agent coordination required to separate concerns: detection, recommendation, session memory

What was built.

A real-time voice + vision wardrobe assistant with multi-agent architecture, built for interactive styling sessions.

Voice + Vision Pipeline

Built the assistant backend in C#/.NET using OpenAI STT/TTS for voice interaction, YOLO object detection for garment recognition, and ONNX-based inference for local model execution. The pipeline accepts voice input, processes the audio through STT, captures the visual frame through YOLO, and feeds both modalities into the reasoning layer.

Multi-Agent Coordination

Designed a modular multi-agent architecture with dedicated agents for detection, recommendation, session memory, and fallback handling. Each agent operates independently with a shared context bus — enabling the system to be extended with new capabilities without rewriting existing logic.

Session-Based Learning

Added session-based voice interaction logging to capture user behavior for future ML fine-tuning. Every interaction — what was asked, what was shown, what was recommended, what the user chose — is stored as structured training data.

Segmented Image Processing

Implemented segmented image processing in the C#/.NET backend, enabling the system to identify and isolate individual garments within a single photo. This powers the virtual try-on pipeline and semantic outfit suggestion engine.

What shipped.

Real-time

voice + vision interaction loop — speak, show, get recommendations

AI models orchestrated: OpenAI STT/TTS, YOLO object detection, LLM reasoning

C# / .NET

backend with ONNX-based local inference for low-latency garment detection

Multi-agent

architecture with shared context bus — detection, recommendation, memory agents

Extensible

modular design — add new capabilities without rewriting existing agent logic

Session

logging for ML fine-tuning — every interaction captured as structured training data

C# / .NET OpenAI STT/TTS YOLO ONNX Runtime LangChain Multi-Agent SSE Docker

The developer.

Alexander Dudnik

AI & Full-Stack Engineer

7+ years designing, implementing, and maintaining distributed backend systems and AI-integrated applications. Technical team lead experienced in Node.js, TypeScript, C#/.NET, LangChain/LangGraph, and cloud-native environments (Azure/AWS). Strong focus on system architecture, production readiness, CI/CD, and domain-driven design.

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