Unified LLM Platform

Status: Under active development — architecture stable, features evolving.

This page documents the engineering intent, system architecture, and design decisions behind the Unified LLM Platform. It is presented transparently for technical review and professional evaluation.

Project Overview

The Unified LLM Platform is an experimental web‑based system that consolidates multiple large language models into a single interface. Its primary goal is to enable model comparison, routing, and evaluation without locking users into a single provider.

Designed and prototyped a unified LLM orchestration platform focused on provider abstraction, request normalization, and model comparison. The system explores multi‑provider routing, failover strategies, and interface‑level decoupling to enable scalable AI integration across applications

Rather than replacing individual LLMs, the system acts as an orchestration layer, standardizing inputs and outputs while preserving each model’s strengths and limitations.

Core Engineering Objectives

Abstract differences between LLM APIs behind a unified interface
Enable side‑by‑side and routed model responses
Maintain clear separation between UI, orchestration, and model adapters
Design for extensibility as new models become available
Ensure transparent behavior (no hidden prompt manipulation)

System Architecture

The platform follows a modular, service‑oriented design to avoid tight coupling between the user interface and individual LLM providers.

1. Client Layer (Web UI)

A lightweight web interface responsible for:

User input capture
Conversation history rendering
Model selection and comparison controls

2. Orchestration Layer

The orchestration layer acts as the system’s control plane:

Normalizes prompts into a common internal format
Routes requests to selected models
Handles fallback and error isolation per provider

3. Model Adapter Layer

Each LLM provider is wrapped by an adapter responsible for:

API‑specific request formatting
Authentication handling
Response normalization

4. Response Aggregation

Outputs from different models are normalized and returned in a consistent structure for display or comparison.

Technology Stack

Frontend:

HTML · CSS · JavaScript

Backend:

Node.js · Express

APIs:

Multiple LLM provider APIs (abstracted)

Architecture:

Adapter Pattern · Service Separation

Current Implementation State

Multi‑model request routing (basic)
Unified prompt and response structure
Web interface prototype
Local development deployment

Planned Enhancements

Conversation memory management
Advanced model comparison views
Cost and latency visibility per model
Access control and usage limits

Professional Context

This project is presented as an engineering portfolio artifact. It demonstrates architectural reasoning, abstraction strategies, and real‑world system design considerations rather than a finished commercial product.