Google Gemini GYM:
Building an Enterprise LLM Evaluation & Orchestration Platform
A backend evaluation and orchestration platform that simulates and analyzes LLM workflows at scale, turning raw model outputs into analytics-ready data.
The Project Overview
Complex Output Formats
Evaluating large language models at scale is not a single test. It is thousands of concurrent runs producing structured scores, unstructured transcripts, and edge-case failures that all need to land in one analyzable place.
Manual Reconciliation Overhead
Our client needed a system that could simulate real LLM workflows, capture every output format the models produced, and make that data queryable for research teams without engineers manually reconciling mismatched schemas every week.
Scaling & Validation Bottlenecks
The existing setup could not keep pace with the volume or variety of evaluation data. Structured scoring outputs and unstructured conversational transcripts were arriving through different channels, with no consistent validation layer, which meant analysts were spending more time cleaning data than analyzing it.
Scalable ETL & Orchestration
Toadsters designed ETL and ELT workflows inspired by Informatica BDM, creating a reliable pipeline from raw LLM evaluation outputs to analytics-ready datasets. SQL-based transformations and Celery orchestration enabled parallel evaluation jobs without blocking downstream reporting.
Automated Reconciliation
Structured and unstructured data, including scoring metrics and model transcripts, was consolidated into BigQuery using schema mapping and automated validation. Invalid or incomplete records were detected during ingestion, ensuring only trusted data reached the analytics layer.
Fault-Tolerant Infrastructure
The platform was containerized with Docker and integrated into a CI/CD pipeline for reliable deployments. Redis managed distributed job state and optimized repeated evaluation runs, while fault-tolerant retry and rollback mechanisms ensured pipeline reliability.
Enterprise Architecture
Built with modern, scalable technologies designed for high-throughput data pipelines and robust orchestration.
Measurable Success
The platform now supports high-volume LLM evaluation runs with a validated, schema-consistent data layer feeding directly into analytics.
Data validation and reconciliation checks that previously required manual review now execute automatically as part of the ingestion pipeline.
The orchestration layer also scales horizontally as evaluation volume continues to grow.
"Toadsters understood the difference between building a data pipeline and building one that AI research teams could actually trust. The reconciliation logic alone saved us weeks of manual QA."
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