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.

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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

The Challenge

The Approach

Internal Evaluation & Dataset Impact

Key Results

The Outcome

Evaluating Olympiad-Grade Math Reasoning for Salesforce AI Research

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.

3-tier

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.

Method

Domain

Dataset scale

Capability

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

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%

The challenge

The approach

Key results

The outcome

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

Delivered an ad storyboard dataset for multimodal AI training, where evaluators produced original 15-shot product advertisement storyboards grounded in provided images and descriptions. Each shot included a scene description, camera motion, and voice-over line, structured for a 8-second video format.

Explore how Turing delivered 500+ structured, image-grounded ad storyboard tasks, with 7,500+ original shots with scene descriptions, camera direction, and voice-over lines, designed for multimodal AI training and evaluation.

Generating 7,500+ Image-Grounded Ad Storyboard Shots for Multimodal AI Training

original shot descriptions delivered across 500+ storyboard tasks, each grounded in real product images and descriptions.

7,500+

voice-over lines produced, each matched to the specific product feature or benefit shown in the corresponding shot.

6,000+

quality score maintained across delivered tasks, reflecting consistent evaluator calibration and creative discipline at scale.

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

Generating 7,500+ image-grounded ad storyboard shots for multimodal AI training

Delivered a large-scale table understanding dataset for AI training and evaluation, where experts extracted numerical tables from real-world PDFs and generated structured Q&A pairs spanning descriptive, comparative, and analytical reasoning.

Explore how Turing delivered structured table-understanding tasks across financial, government, academic, and product domains, combining rigorous table extraction, multi-type Q&A generation, and human-in-the-loop QA to train AI systems on precise numerical reasoning.

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

structured table reasoning Q&A pairs delivered across 7,000+ tasks, spanning seven real-world document domains.

70,000+

overall pass rate achieved across all delivered tasks, reflecting strong annotator calibration and quality discipline at scale.

95%+

external inference enforced: all answers sourced exclusively from table data, with no assumptions, calculations steps, or outside knowledge permitted.

Zero

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

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

Building 70,000+ table reasoning Q&A pairs across real-world documents for AI training

Turing helped a large government agency deploy a secure Arabic/English AI assistant that unified internal knowledge and reduced query resolution from hours to minutes.

Secure English/Arabic conversational AI platform that centralizes enterprise knowledge, accelerates employee support, and delivers compliant, scalable access to internal systems for large government organizations.

Deploying a multilingual AI assistant for internal knowledge access | Turing

information retrieval, from hours to minutes

Faster

alternative to public AI tools

Secure

enterprise AI foundation, expanding beyond the 500 employee pilot

Scalable

The client

A new foundation for enterprise AI

Navigating the Cloud Migration Process: A Step-by-Step Guide to Successful Cloud Adoption

Choosing the Right Application Development Service Provider

Maximizing Business Value through Application Portfolio Rationalization

Deploying a multilingual AI assistant for internal knowledge access

Delivered a large-scale chart understanding dataset for multimodal AI training, sourced through a licensed, real-world data pipeline spanning seven document domains. Each task includes three grounded Q&A pairs covering descriptive, comparative, and analytical reasoning, produced under strict zero-inference and zero-approximation standards.

Explore how Turing delivered 12,000+ structured chart Q&A pairs spanning descriptive, comparative, and analytical reasoning across seven document domains, with zero-inference annotation standards and a rigorous multi-layer quality pipeline.

structured chart Q&A pairs delivered across seven real-world document domains, including financial reports, government files, and business reports.

12,000+

client acceptance rate across all delivered tasks, with a scalable sourcing pipeline ensuring licensed, domain-diverse documents with full compliance validation.

standard enforced: no unlabeled numbers, no visual approximation, and no causal claims permitted at any stage.

Zero-inference

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.

Want to accelerate your business with AI?

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

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

Generating 7,500+ image-grounded ad storyboard shots for multimodal AI training

Building 70,000+ table reasoning Q&A pairs across real-world documents for AI training

Deploying a multilingual AI assistant for internal knowledge access

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

Want to accelerate your business with AI?