Inference and Model Tuning Platform

A full-stack enterprise platform engineered for the complete lifecycle of large language model applications from initial experimentation through production deployment at scale. The system delivers the core functionality of an AI development studio, enabling cross-functional teams to experiment, validate, customize, and operationalize LLMs within a unified environment.

At its heart, the platform brings together two critical components

• an interactive Prompt Lab for real-time prompt engineering, evaluation, and A/B testing, and
• a powerful Tuning Studio that supports both prompt tuning and parameter-efficient fine-tuning techniques such as LoRA and QLoRA.
• These capabilities are delivered through a modern web application backed by a scalable API layer and modular backend services.

The architecture was designed from the ground up for extensibility, security, and performance at scale. It features a pluggable model registry supporting multiple LLM providers, a robust evaluation framework with custom metric definitions, versioned prompt management, and role-based access control. The backend leverages asynchronous job orchestration for training and inference workloads, with support for GPU cluster scheduling and auto-scaling. Observability is built in through structured logging, distributed tracing, and real-time dashboards tracking latency, throughput, token usage, and cost attribution across teams and projects.