[ X: 1045, Y: 890, Z: 200 ]
AI Infrastructure & LLM Tooling

Gemini GYM:
LLM Evaluation Platform Development

A backend evaluation and orchestration platform that simulates and analyzes large language model workflows at scale, turning raw model outputs into analytics-ready data.

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[ 01 ]

The
Challenge

Evaluating large language models at scale isn't a single test — it's thousands of concurrent runs producing structured scores, unstructured transcripts, and edge-case failures that all need to land in one analyzable place. This client needed a system to 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.

The existing setup couldn't keep pace with the volume or variety of evaluation data. Structured scoring outputs and unstructured conversational transcripts arrived through different channels with no consistent validation layer, so analysts spent more time cleaning data than analyzing it.

What stood in the way

  • Evaluation At Scale

    Thousands of concurrent runs landing in one analyzable place

  • Mixed Output Formats

    Structured scores and raw transcripts arriving separately

  • No Validation Layer

    Mismatched schemas reconciled by engineers every week

  • Research Throughput

    Data queryable by analysts without engineering help

Unified platform
  • SQL
  • BigQuery
  • Redis
  • Celery
  • Docker
  • Python
[ 02 ]

The
Solution

Toadster designed and implemented ETL and ELT workflows modeled on Informatica BDM ingestion and transformation patterns, giving the platform a predictable, auditable path from raw evaluation output to analytics-ready tables. The transformation layer runs on SQL-based batch processing with Celery-driven orchestration, so evaluation jobs run in parallel without blocking downstream reporting.

Measurable Impact

The Project
Overview

SYSTEM_DIAGNOSTICS: OPTIMAL
MODULES: ONLINE
3.2M+

Evaluation Records Processed Monthly

99.9%

Pipeline Uptime

58%

Data Processing Time Reduced

SEQ.01_IMPACT

Automated Scale

The platform now supports high-volume LLM evaluation runs with a validated, schema-consistent data layer feeding directly into analytics. Data validation and reconciliation that previously required manual review now run automatically as part of ingestion, and the orchestration layer scales horizontally as evaluation volume grows.

  • Reports
  • Answers
  • Insights
AI Engine
  1. 01

    Connect

    Bring your data sources together

  2. 02

    Analyze

    AI finds patterns and answers

  3. 03

    Deliver

    Grounded insights that drive action

CLIENT SIGNAL
“Toadster understood the difference between building a data pipeline and building one AI research teams could actually trust. The reconciliation logic alone saved us weeks of manual QA.”
— Engineering Lead, AI Evaluation Team
  • ReliableConsistent data you can depend on
  • AuditableFull traceability and transparency
  • ActionableInsights that drive real outcomes
Architecture

Enterprise
Architecture

Built with modern, scalable technologies designed for high-throughput data pipelines and robust orchestration.

Core & Application

PythonFastAPISQLBigQuery

Infrastructure & Delivery

CeleryRedisDockerCI/CD

Frequently asked Questions

Common questions about the LLM Evaluation Platform

It runs large language model workflows at scale, captures both structured scores and unstructured outputs like transcripts, validates that data against a defined schema, and loads it into a data warehouse so teams can analyze model performance without manual cleanup.

LLM outputs are inconsistent by nature — the same evaluation can produce clean numeric scores and messy free-text transcripts in the same run. A proper ETL/ELT layer normalizes both into a consistent format before they hit the analytics warehouse, enabling cross-model comparison.

Through schema mapping, automated validation rules, and reconciliation checks at the point of ingestion, combined with fault-tolerant batch orchestration so a failed job can be retried without corrupting existing data.

A typical stack pairs Python and FastAPI for orchestration APIs, SQL-based transformation logic, a cloud warehouse such as BigQuery for analytics, Celery and Redis for distributed job processing, and Docker with CI/CD for reliable deployment.

Yes — because the ingestion and transformation layers are schema-driven rather than model-specific, new evaluation sources can be onboarded by mapping their output format to the existing schema, without redesigning the pipeline.

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