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.

primary_white

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

Method

Domain

Dataset scale

Capability

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 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

Evaluating Olympiad-Grade Math Reasoning for Salesforce AI Research

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

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 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

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

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.

1,000+

automated checks validated actual workflow completion.

3,000+

ServiceNow partnered with Turing to create EnterpriseOps-Gym, a benchmark designed to evaluate enterprise agents operating across realistic, multi-system workflows. The work focused on designing structured prompts, execution trajectories, and evaluation pipelines that measure how well models plan, execute, and adhere to policies in production-like environments.

Explore how Turing contributed to EnterpriseOps-Gym by designing 1,000+ enterprise prompts and evaluation workflows to benchmark long-horizon planning, tool use, and policy adherence.

evaluated prompts spanning enterprise workflows with logged trajectories, tool interactions, and verifier scripts.

step ranges represented across prompts, enabling controlled tests of long-horizon planning difficulty.

7-30

enterprise domains covered, including HR, CSM, ITSM, Email, Calendar, Drive, Teams, and Hybrid workflows.

Designed and delivered a sandboxed RL environment for training and evaluating AI agents on real-world commercial sales workflows. The RL Gym packaged 100+ structured workflows across four enterprise platforms, with realistic UI replicas, natural-language prompts, step-level verifiers, and Docker-containerized delivery ready for agent experimentation at scale.

Explore how Turing delivered a fully sandboxed RL Gym with 100+ commercial workflows, Docker-packaged environments, and verifier-based scoring across LinkedIn Sales Navigator, HubSpot, Outreach, and Calendly.

workflows delivered with UI replicas, natural-language prompts, step-level verifiers, and Docker packaging.

enterprise platforms covered, including LinkedIn Sales Navigator, HubSpot, Outreach, and Calendly.

complexity framework applied across all workflows to calibrate difficulty and produce reliable RL training signals.

Pass@3

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

Industry

Company type

Capabilities used

The client

The problem

The solution

The result

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

Evaluated AI-generated videos across caption alignment, real-world fidelity, and visual quality using a structured, element-based methodology and objective scoring thresholds, enabling consistent differentiation between highly similar videos while minimizing annotator subjectivity.

Explore how Turing delivered structured video evaluation tasks using element-based caption scoring, real-world fidelity assessment, and objective visual quality metrics to benchmark AI-generated videos.

Evaluating 1,600+ Videos With Structured Caption Matching and Fidelity Scoring

video evaluation tasks, spanning caption matching, fidelity scoring, visual quality assessment, and holistic preference comparisons.

1,600+

inter-annotator alignment achieved, demonstrating strong consistency across independent evaluators.

first-pass client acceptance, with all deliverables meeting evaluation criteria without rework.

100%

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