Built a large-scale artifact generation benchmark, generating and validating document and infographic artifacts across different output formats, AI providers, and query complexity levels. The project delivered a model- and format-aware benchmark dataset with a 99.9% artifact acceptance rate, providing clean signal for evaluating enterprise-grade artifact generation capabilities.

Explore how Turing executed a large-scale artifact generation benchmark across document and infographic formats, collecting and validating outputs from leading AI providers with a 99.9% artifact acceptance rate.

Building a 1,500+ Artifact Benchmark to Evaluate SOTA Model Performance Across Enterprise Document Formats

artifacts generated and validated across PPTX, HTML, DOCX, PDF, JSON, Excel, CSV, TXT, and infographics.

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1,500+

artifact acceptance rate achieved, reflecting strong execution discipline and format compliance enforcement at scale.

99.9%

query complexity levels covered, testing model performance from basic generation through high-complexity enterprise instruction-following.

Method

Domain

Dataset scale

Capability

The challenge

The approach

Key findings

Key results

The outcome

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

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

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

Building a 1,500+ artifact benchmark to evaluate SOTA model performance across enterprise document formats

Delivered a full-stack instruction-following benchmark suite and training environment integration to help a leading AI organization improve instruction inference and instruction following across realistic, training-relevant surfaces.

Explore how Turing delivered a large-scale multilingual instruction-following benchmark suite spanning dense constraints, multi-turn state, system-priority behavior, and training environment integration.

Building a Full-Stack Instruction-Following Suite

of instruction-following tasks delivered across four benchmark categories, plus training environment integration for evaluation and model improvement workflows.

Thousands

coverage across major global languages, enabling instruction-following improvement beyond English-only behavior.

Multilingual

coverage across dense constraints, multi-turn state, uncommon prompt patterns, and system-prompt priority behavior.

Full-stack

Building a full-stack instruction-following suite to stress-test and improve model behavior

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.

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

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

Evaluating Olympiad-Grade Math Reasoning for Salesforce AI Research

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

Built production-grade simulated web environments across food delivery and retail platforms, paired with 500+ verifier-backed tasks designed to stress-test browser-use agents during reinforcement learning. Each environment shipped with realistic seed data, dynamic verification, and difficulty calibration tied to current SOTA agent performance.

Explore how Turing delivered simulated web environments across food delivery and retail platforms, paired with 500+ verifier-backed tasks calibrated to break SOTA computer-use agents during RL training.

Building Production-Grade Web Environments and 500+ Verifier-Backed Tasks for Computer-Use Agent RL Training

verifier-backed tasks delivered across simulated food delivery and retail environments, spanning 100+ task templates.

500+

tasks calibrated as model-breaking against SOTA computer-use agents, with hard tasks defined as Pass@10 < 2 against SOTA computer-use agents.

>50%

environment delivery, including simulated UI, realistic seed data (products, menus, images, reviews, inventory), dynamic verifiers, and performance-optimized infrastructure.

End-to-end

The Challenge

The Approach

Key Results

The Outcome

Powering ServiceNow’s EnterpriseOps-Gym: Benchmarking Enterprise Agents With Execution-Grounded Workflows

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

Evaluating Agent Workflows With Verifier-Grounded Execution Benchmarks

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

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.

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

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.

100%

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.

1,000+

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

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 1,500+ artifact benchmark to evaluate SOTA model performance across enterprise document formats

Building a full-stack instruction-following suite to stress-test and improve model behavior

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

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

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

Want to accelerate your business with AI?