Structure the next generation of model reasoning
Build, test, and refine model behavior in real-world environments. From reinforcement learning and code reasoning to scalable evaluation systems, and robust data packs, Turing structures what happens after training.
RL environments for agent evaluation
Turing RL Environments replicate consumer and enterprise systems in detail: browser-use, workflow automation, and backend function-calling.
Each environment is packaged as a Docker container with APIs for task retrieval, environment resets, and verifier-based scoring, enabling structured experimentation at scale
Turing’s UI RL environments simulate authentic enterprise and consumer systems where agents must plan, adapt, and recover through real UI interactions. Every element from click paths, state transitions, and verifier logic, is designed to turn browser behavior into a structured reasoning challenge.
What sets Turing apart is depth and fidelity: each environment mirrors live software workflows with deterministic verifiers and measurable reward signals, exposing not just what agents can do, but how they reason when confronted with uncertainty.
Turing’s MCP environments test reasoning in the invisible layer - where function calls, APIs, and decision logic define performance. These environments recreate enterprise workflows through structured tool calls and state-tracked verifiers that make reasoning measurable.
By combining deterministic evaluation, multi-agent reinforcement, and domain-specific logic packs, MCP environments reveal how agents learn to compose, critique, and refine decisions—the foundation of real-world reasoning maturity.
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Reliable systems for code reasoning
Turing’s coding ecosystem provides structured benchmarks, curated datasets, and reproducible evaluation systems that measure how well models reason, debug, and generate production-grade code.
Our data enables benchmarking, fine-tuning, and reinforcement across multi-language and multi-domain coding tasks.
Core Capabilities
Reproducible reasoning benchmark built on verified GitHub issues and containerized environments for auditable code reasoning.
Private dataset of 900+ multilingual coding challenges with deterministic scoring for bias-free evaluation.
Structured IaC datasets mirroring real-world cloud deployments for DevOps and automation reasoning.
Evaluate agentic logic across APIs, tools, and custom functions, ensuring alignment between intent and execution.
Structured hill-climb analysis converts unstructured outputs into actionable traces for reproducible fine-tuning.
All datasets and benchmarks map to Turing’s Five-Step Framework, reinforcing repeatability and QA consistency.
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The quick brown fox jumps over the lazy fox
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Why Turing?
