Designed and delivered a hard-tier reinforcement learning task suite for codebase exploration. Each task package combined a repository snapshot in a standardized, read-only Docker workspace with a weighted multi-stage verifier, deterministic grounding scoring, and a human-authored gold trajectory.

Explore how Turing designed and validated a reproducible hard-tier reinforcement learning task suite for codebase exploration, combining a read-only Docker workspace, weighted multi-stage verifiers, and human-authored gold trajectories.

Building a Hard-Tier RL Task Suite for Real-World Codebase Exploration

codebase exploration RL tasks delivered using a standardized, reproducible Docker-based exploration workspace.

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1,000+

of tasks included in the final delivery passed the validation gate, with human-authored gold trajectories scoring above 99.9% through the full verifier pipeline.

100%

model-generated rollouts produced through pass@10 sampling for difficulty profiling and calibration.

5,000+

Method

Domain

Dataset scale

Capability

The challenge

The approach

Key results

The outcome

Building Production-Grade Web Environments for Computer-Use Agent RL Training

Evaluating Agent Workflows With Verifier-Grounded Execution Benchmarks

Building Production-Ready RL Gyms for Commercial Agent Workflows Across 4 Platforms

Building a hard-tier RL task suite for real-world codebase exploration

Built human-driven agent trajectories to support Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) for a frontier AI research lab's general-purpose AI agent. Turing produced structured trajectory data grounded entirely in real APIs and live services, with no simulated environments or mocked tool responses.

Explore how Turing produced 35,000+ SFT trajectories and 120,000+ RLHF preference pairs from human-driven agent sessions, spanning general task execution and privacy-compliant behavior.

Delivering 35,000+ Agent Trajectories to Train and Align a Frontier AI Assistant

SFT trajectories delivered spanning general task execution and privacy-compliant agent behavior.

35,000+

RLHF preference pairs produced, pairing optimal agent actions against suboptimal or policy-violating alternatives.

120,000+

tools and skills exercised, from local file operations to third-party API calls.

150+

Building 70,000+ Table Reasoning Q&A Pairs Across Real-World Documents for AI Training

Benchmarking Frontier Models With 5,000+ HLE-Grade STEM Problems

Delivering 3,500+ Locale-Aware Agentic Conversations Across 15+ Languages

Delivering 35,000+ agent trajectories to train and align a frontier AI assistant

Delivered a large-scale multilingual agentic dataset spanning 15+ locales, designed to train and evaluate an AI agent's ability to interpret user requests, select appropriate tools, and execute multi-step workflows across diverse languages and cultural contexts.

Explore how Turing delivered multi-turn agentic conversations spanning 15+ locales, designed to train and evaluate tool-calling accuracy, instruction following, and locale-aware reasoning in real-world agent workflows.

multi-turn agentic conversations delivered across 15+ locales, spanning tool calls, sequential reasoning chains, and locale-specific instruction following.

3,500+

locales covered, including English, Korean, Swedish, Dutch, German, French, Italian, Portuguese, Spanish, and Chinese.

quality dimensions evaluated per task, including tool use accuracy, hallucination, system context adherence, datetime reasoning, and naturalness of dialogue.

Building a Document Understanding Dataset Across 15,000+ OCR, Summarization, and Translation Tasks

Building 2,000+ Human-Grounded Theory-of-Mind Dialogues for Persuasion Research

Evaluating Olympiad-Grade Math Reasoning for Salesforce AI Research

Delivering 3,500+ locale-aware agentic conversations across 15+ languages

Delivered a large-scale multilingual transcription dataset capturing word-for-word speech, non-verbal vocalizations, and speaker attributes. Each task combined precise textual transcription with a 20-tag annotation taxonomy covering filled pauses, background speech, media speech, and garbled audio, supporting evaluation and improvement of automatic speech recognition and dialog models.

Explore how Turing delivered 20,000+ transcription tasks across 500+ hours of multilingual audio, applying a 20-tag annotation taxonomy and severity-tiered quality framework to support ASR and dialog model training.

Delivering 20,000+ Multilingual Transcription Tasks for ASR and Dialog Model Training

transcription tasks delivered across 500+ hours of multilingual audio.

20,000+

standardized tags applied per task to capture verbal, non-verbal, and contextual audio elements beyond plain transcription.

severity framework enforced across 10 error categories, with critical errors triggering automatic task failure.

3-tier

The Challenge

The Approach

Key Results

The Outcome

Delivering 450+ Hours of Studio-Grade Speech Data for Automated Dubbing

Driving Frontier-Level Reasoning in Apriel-1.5 with 390K+ High-Signal Prompts

Delivered high-difficulty math prompts aligned with the rigor of the Humanity’s Last Exam (HLE) dataset. Each prompt was designed to break state-of-the-art (SOTA) LLMs while maintaining novelty, correctness, and reviewer traceability.

Explore how Turing delivered 1000+ research-grade, model-breaking math prompts, aligned with the HLE benchmark to evaluate frontier LLM performance at scale.

Delivering 1000+ HLE-Grade Math Prompts to Benchmark SOTA Models

research-level math prompts spanning 10+ subdomains, each reviewed for novelty, clarity, and solution validity.

1000+

review coverage: Dual-layer expert QA to ensure correctness, difficulty calibration, and formatting compliance.

model break criteria: Every question required to break two internal benchmark models, with at least 50% also breaking a third external SOTA model during evaluation.

Industry

Company type

Country

Capabilites used

Internal Evaluation & Dataset Impact

Lean & Symbolic Reasoning for LLM-Based Mathematical Problem Solving

Audio SFT: Teaching AI to Understand Human Voice in Noisy, Real-World Scenarios

LLM functionality enhanced beyond basic text generation through integrated diverse tools significantly expands capabilities for complex tasks...

See how multimodal integration with 1,000+ RL test cases expanded LLM functionality, enabling complex reasoning and advanced task execution.

Multimodal LLM Case Study: 1,000+ RL Tests Enhance Accuracy

RL test cases: Validated LLM advancements in API handling & processing.

adoption growth: Robust action-oriented model boosted user adoption in two quarters.

team scaling: Expanded in three months to meet growing project demands.

Capabilities used

About the client

The problem

The solution

The result

Why Vision-Language Models Still Struggle With Real Business And STEM Workflows

Fine-Tuning LLMs : Overview, Methods, and Best Practices

Evaluating VLMs On Real Business And STEM Tasks

1000+ RL Test Cases: Multimodal Integration in LLMs for Advanced Task Execution

Created expert-verified multimodal QA prompts from real-world slide decks, targeting reasoning failures in large multimodal models (LMMs) across business, STEM, finance, and general knowledge.

See how Turing designed a structured SlideVQA pipeline across 20+ domains to generate 7K+ expert-verified prompts that require multi-step visual, numerical, and document reasoning.

Building 7K+ High-Complexity SlideVQA Tasks Across 20 Knowledge Domains

SOTA model-breaking QA samples: Created across 20+ knowledge domains with slide-grounded visual reasoning.

7000+

difficulty structure: Prompts were labeled Easy, Medium, or Hard based on reasoning complexity, number of steps, and the number of slides and visuals referenced.

visual grounding required: All prompts require visual references, such as charts, infographics, maps, or diagrams.

Revealing Systemic Chart Reasoning Gaps with 20K+ Expert CoTs

Chain of Experts: What Is It and How It Solves MoE’s Limitations

Building 7K+ High-Complexity SlideVQA Tasks Across 20+ Knowledge Domains

Delivered a non-verifiable coding evaluation dataset to assess natural-language reasoning, explanation depth, and technical judgment in language models. Each task was built around a structured, reference-free rubric and validated against a frontier LLM to confirm model-breaking difficulty before acceptance.

Explore how Turing delivered 1,000+ non-verifiable coding evaluation tasks across multiple languages and domains, combining structured rubrics, reference-free scoring, and model-breaking validation to assess natural-language reasoning and explanation quality in frontier models.

Building 1,000+ Model-Breaking Coding Evaluation Tasks With Reference-Free Rubrics

model-breaking coding evaluation tasks delivered across multiple programming languages and technical domains.

binary quality dimensions enforced per task through a combined automated and human review pipeline.

task categories covered: code understanding and explanation, code debugging and fixing with explanation, and code recommendation and comparison.

Delivering 2,000+ Scientific Coding Tasks With Verified Answers for Frontier Model Training

Creating 10,000+ RLEF-Ready Python Tasks for Model Evaluation and Training

Delivered a video description dataset spanning factual narrative summaries and structured accessibility-grade audio descriptions. Both workstreams required evaluators to produce descriptions grounded strictly in visible and audible video content, supported by a three-agent processing pipeline and validated through a human-in-the-loop QA system.

Explore how Turing delivered structured video description tasks spanning long-form narrative summaries and accessibility-grade audio descriptions, combining a three-agent processing pipeline, zero-speculation standards, and three-layer human-in-the-loop QA to support multimodal AI training and evaluation.

Delivering 500+ Factually Grounded Video and Audio Descriptions for Multimodal AI Training

video description tasks delivered, each grounded strictly in visible and audible content with zero speculation or outside knowledge permitted.

500+

processing pipeline deployed, combining frame extraction, audio processing, and image understanding to support evaluator accuracy and content grounding.

3-agent

quality score achieved across delivered tasks, reflecting consistent evaluator calibration and narrative discipline at scale.

>90%

Powering the UI-Vision Benchmark with 10,000+ Desktop GUI Tasks

Benchmarking RTL Agents with 1,500+ Real-World Verilog Tasks for NVIDIA’s CVDP

Building 12,000+ Chart Q&A Pairs to Train Multimodal Reasoning Across Real-World Documents

Delivering 500+ factually grounded video and audio descriptions for multimodal AI training

Discover how AI labs use Turing’s talent, data, and tools to improve model performance — and how enterprises deploy production-ready AI with Turing’s solutions

Case Studies | AI-Driven Transformation, Proven Through Real-World Impact

Maximizing Your LLM ROI: The True Costs of Poor Strategy, Evaluation, and Training

Future-proofing with generative AI: Scalable enterprise solutions

All case studies

Talk to one of our solutions architects and start innovating with AI-powered talent.

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Case studies

45% Improvement in Loan Processing Time via Lending Automation Transformation

1000+ RL Test Cases: Multimodal Integration in LLMs for Advanced Task Execution

35% Boost in User Confidence: Transforming Enterprise Search With Vespa and Google Vertex AI

6 Unique Evaluation Frameworks: Improving LLM Coding Precision

All case studies

Building a hard-tier RL task suite for real-world codebase exploration

Delivering 35,000+ agent trajectories to train and align a frontier AI assistant

Delivering 3,500+ locale-aware agentic conversations across 15+ languages

Delivering 20,000+ Multilingual Transcription Tasks for ASR and Dialog Model Training

Delivering 1000+ HLE-Grade Math Prompts to Benchmark SOTA Models

1000+ RL Test Cases: Multimodal Integration in LLMs for Advanced Task Execution

Building 7K+ High-Complexity SlideVQA Tasks Across 20+ Knowledge Domains

Building 1,000+ Model-Breaking Coding Evaluation Tasks With Reference-Free Rubrics

Delivering 500+ factually grounded video and audio descriptions for multimodal AI training

Want to accelerate your business with AI?