# BenchGen

> BenchGen is a simulation and benchmarking platform that builds digital-twin environments where AI agents practice, fail, and learn — turning evaluation into continuous training data for production-ready agents.

BenchGen is a simulation and benchmarking platform for AI agents, built by a team with backgrounds in AI systems research, cloud infrastructure, and enterprise software. It creates digital twins of real business environments — connecting to CRM, ERP, databases, and APIs — so agents can be evaluated and trained in realistic conditions before deployment. The platform is designed for mission-critical industries including defense, fintech, energy, and education, and supports sovereign, on-premise, and air-gapped deployments.

## What It Is

BenchGen describes itself as a "Synthetic Data Factory" and "flight simulator" for AI agents. Rather than testing agents against static prompt-response benchmarks, it runs them through complete multi-step workflows inside sandboxed simulations that mirror real operational systems. Every agent run produces structured trajectory data — the full decision sequence, tool calls, retrieval steps, intermediate outcomes, and final success or failure signals — which can be fed directly into reinforcement learning pipelines using methods like PPO, GRPO, and PRM-style training. The result is a closed loop: simulate, evaluate, train, and repeat.

## Core Workflow

The platform organizes agent development into four stages:

- **Create Environment**: Spin up an isolated runtime connected to enterprise data sources (CRM, ERP, databases, APIs), deployable on cloud or on-premise GPU/CPU infrastructure.
- **Evaluate**: Run agents through curated benchmarks and multi-step task scenarios, capturing full decision trajectories and scoring every step.
- **Train**: Use trajectory data and LoRA fine-tuning to improve agent behavior; export adapters ready for inference.
- **Deploy**: Ship agents with verifiable benchmark reports showing task completion rates, per-step accuracy, identified failure modes, and audit-ready logs.

## Benchmark Discovery and Model Leaderboards

BenchGen hosts a public benchmark directory where frontier models are run through standardized environments. The platform currently lists benchmarks including MMLU-Pro, GPQA Diamond, Humanity's Last Exam, GSM8K, MATH, and LiveCodeBench, with live rankings showing model scores. According to the site, the platform has captured over 2 million trajectories, improved more than 1,400 agents, and hosts over 750 RL environments.

## Industries and Deployment Model

BenchGen targets organizations where AI failure carries high operational or regulatory risk:

- **Defense & Intelligence**: Air-gapped, classified environments with full audit trails; the site states the platform has been deployed in NATO-member defense organizations.
- **Fintech**: A flagship use case is a full bank simulation — a "paper trading" environment for AI agents covering fraud detection, loan approval, KYC/AML, trade execution, and risk management.
- **Energy & Utilities**: Critical infrastructure protection with verifiable agent behavior.
- **Education**: Workflow assistants evaluated against measurable completion metrics.

The platform supports SOC 2, ISO 27001, and HIPAA compliance, and can run in sovereign or air-gapped environments where model weights and evaluation data never leave the controlled infrastructure.

## Team and Background

BenchGen was co-founded by three practitioners: Andrii Bidochko (PhD in AI Systems, published research on long-horizon LLM agents in Elsevier's Journal of Computational Science), Tolga Dincer (specialist in AI-native cloud architecture with experience in fintech, defense, and public-sector infrastructure), and Ruslan Synytsky (serial entrepreneur, Java Champion, and founder of Jelastic PaaS, which was acquired by Virtuozzo in 2021). The company is incorporated as Benchgen, Inc.

## Why It Matters

The platform addresses what it calls the "demo-to-production gap" — the observation that agents performing well in controlled demos frequently fail in real operational environments. BenchGen's position is that trajectory-based evaluation, grounded in realistic simulations, is necessary infrastructure for any team deploying autonomous agents at scale. The site claims backing from 500+ teams and lists case studies with organizations including a national defense organization, Enerjisa (energy), BAU Colleges (education), DT Cloud, and Ravatar.

## Features
- Digital twin simulation environments for AI agents
- Trajectory-based agent evaluation capturing full decision sequences
- Reinforcement learning training data generation (PPO, GRPO, PRM-compatible)
- Public benchmark leaderboard with live model rankings
- LoRA fine-tuning with exportable inference adapters
- Air-gapped and sovereign on-premise deployment
- Enterprise data source connectors (CRM, ERP, databases, APIs)
- Audit-ready benchmark reports with per-step accuracy scores
- Agentspace visual agent builder
- REST API for pipeline integration
- SOC 2, ISO 27001, and HIPAA compliance
- Skill Checker tool for quick agent capability audits

## Integrations
CRM systems, ERP systems, Databases, REST APIs, PPO training frameworks, GRPO training frameworks, PRM-style training methods, LoRA fine-tuning pipelines, Cloud GPU infrastructure (H100, A100), On-premise CPU/GPU servers

## Platforms
WINDOWS, WEB, API, CLI

## Pricing
Freemium — Free tier available with paid upgrades

## Links
- Website: https://benchgen.com/
- Documentation: https://benchgen.com/docs
- EveryDev.ai: https://www.everydev.ai/tools/benchgen