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.

primary_white

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

The Challenge

The Approach

Key Results

The Outcome

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 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.

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

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.

100%

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

Zero-inference

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

Built an end-to-end SWE-bench-style data generation pipeline that sources, validates, annotates, and delivers execution-grounded software engineering tasks across 10+ programming languages. Each task pairs a real GitHub issue with a PR, a validated patch and dockerized test harness, supporting both fail-to-pass and pass-to-pass evaluation signals for model training and benchmarking.

Explore how Turing built an end-to-end SWE-bench-style data pipeline across 10+ programming languages, delivering 12,000+ execution-grounded tasks spanning bug fixes, features, and performance improvements through hybrid annotation and dockerized validation.

Delivering 12,000+ SWE-Bench-Style Tasks With Execution-Grounded Validation

SWE-bench-style tasks delivered across 10+ programming languages, including Python, JavaScript/TypeScript, Java, Go, Rust, and more.

production pipeline spanning public PR sourcing, hybrid quality annotations, trajectory generation, and 3x validation for reproducibility.

6-stage

early rejection framework combined with dual-axis scoring and golden patch validation.

15-criteria

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

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, double-blind persuasion dialogue dataset annotated with structured Theory-of-Mind (ToM) reflections. Each task captured belief evolution, mid-conversation mental state inferences, and post-dialogue belief updates, providing human-grounded supervision signals to improve model performance on ToM-focused benchmarks such as PersuasiveToM, NegotiationToM, ToMATO, and more.

Explore how Turing delivered a large-scale, double-blind persuasion dataset with structured Theory-of-Mind annotations capturing belief evolution, mid-conversation mental state inferences, and post-dialogue belief updates to support frontier model training.

double-blind persuasion dialogues delivered, each with full pre-test, dialogue, mid-check, and post-test annotations.

2,000+

-dimension QA rubric enforced, covering dialogue coherence, persuasion strategy, ToM depth, belief evolution consistency, and language appropriateness.

human-authored content: zero LLM-generated dialogue or annotations permitted, verified through QA review.

Evaluating Olympiad-Grade Math Reasoning for Salesforce AI Research

Delivered a large-scale document understanding dataset spanning OCR, summarization, and translation tasks across 10+ languages. The dataset covers 10+ document subdomains, from handwritten notes and rotated scans to printed financial reports and web screenshots, sourced from diverse real-world origins to reflect the full complexity of documents an AI agent encounters in production.

Explore how Turing delivered 15,000+ single-page and multi-page document understanding tasks spanning OCR, summarization, and translation capabilities, covering 10+ real-world document subdomains for AI agent training and evaluation.

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

tasks delivered across single-page and multi-page documents, spanning OCR, summarization, and translation capabilities.

15,000+

summarization accuracy achieved across both single-page and multi-page tasks.

>95%

document subdomains covered, including printed documents, scanned records, handwriting, forms, slides, web screenshots, financial reports, and academic papers.

Evaluating 50,000+ Multimodal AI Responses Across Image-Grounded Reasoning Tasks

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

Delivered a large-scale preference evaluation dataset, covering head-to-head comparisons of AI-generated responses to image-grounded prompts. Annotators assessed factual accuracy, instruction-following, coherence, and honesty across diverse visual reasoning tasks, producing structured preference judgments with justified explanations to support model training and evaluation.

Explore how Turing delivered preference evaluations of AI-generated responses to image prompts, combining structured factuality review, grounded preference reasoning, and a multi-layer QA framework to support multimodal RLHF pipeline.

preference evaluations delivered across image-grounded prompts spanning scientific, descriptive, mathematical, and information-seeking tasks.

50,000+

evaluation framework assessing responses across factuality, coherence, instruction-following, signal-to-noise ratio, and honesty dimensions.

Multi-dimensional

QA pipeline with star-rated review, error trend tracking, and calibrated annotator training to maintain consistent preference signal quality at scale.

Structured

Delivered an execution-grounded evaluation benchmark to assess how agentic models perform across real-world HR workflows. The dataset uses verifier-based validation to measure whether models correctly complete multi-step processes involving tool calls, parameter usage, and workflow sequencing.

Explore how Turing benchmarked agent performance across HR workflows using 100+ execution-based tasks, verifier-driven evaluation, and cross-model failure analysis.

execution-grounded workflow tasks evaluated across core HR operations.

100+

runs per model, enabling stable pass@10 benchmarking.

automated checks validated actual workflow completion.

3,000+

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.

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