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

primary_white

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%

Method

Domain

Dataset scale

Capability

The Challenge

The Approach

Key Results

The Outcome

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

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

Evaluating Olympiad-Grade Math Reasoning for Salesforce AI Research

Built a computer-use evaluation dataset consisting of 900+ structured tasks to benchmark how effectively AI systems execute long-horizon workflows and handle controlled error conditions.

Explore how Turing built a 900+ task paired computer-use dataset with structured mistake taxonomy, full interaction telemetry, and rubric-based QA to evaluate long-horizon agent performance.

Evaluating Computer-Use Agents With 900+ Paired Tasks and Structured Failure Modes

tasks delivered, with 450+ parent–child task pairs designed for controlled agent evaluation

900+

user actions captured per task to stress-test long-horizon agent execution

80–200+

workflow domains covered, including education, general computing, productivity, data science, development and creativity

Delivered a scientific coding STEM Q&A dataset with verifiable ground truth, spanning research-grade problems in physics, chemistry, mathematics, and biology domains. The dataset is designed to support hill climbing on sophisticated benchmarks such as SciCode, an industry-recognized benchmark that requires specialized domain knowledge and coding experience.

Explore how Turing produced computationally intensive STEM Q&A tasks that require Python, optimized for diversity, scientific validity, and stable grading through multi-stage human review and agent-assisted checks.

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

scientific coding STEM tasks delivered, built to be impractical to solve analytically or by hand calculations within at least 1 day by an expert, and intended for computational scientific workflows.

2,000+

quality process applied, including agentic review along with Level 1 (L1) prompt quality review and Level 2 (L2) scientific validation from coding and technical standpoint.

5-stage

task selection enforced: tasks were filtered to avoid both “always pass” and “always fail” behavior under repeated trials (pass@k).

Pass-band

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.

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.

About the client

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 high-quality prompts and RLHF preference pairs to improve the model’s ability to use built-in extensions across core customer journeys (CUJs). The dataset included complex tool chaining, fallback reasoning, and stepwise thought, code, explain formatting to support model alignment on grounded tool use.

Explore how Turing helped improve model performance on built-in extension tools with 1000+ prompts and RLHF data spanning tool chaining, fallback reasoning, and structured demonstration.

Training and Evaluating Extension Tool Use with 1000+ Prompts and RLHF Preference Pairs

diverse prompts across 8 CUJs, including positive and negative examples.

tool coverage across YouTube, Browse, Search, and Maps.

Multiple

pairs annotated with preference ratings and user-written demonstrations.

RLHF

Benchmarking Model Fidelity with 500 Expert-Verified Software Engineering Tasks

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

Improving LLM Performance with 4,000+ Apex and SOQL Notebook Tasks

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

Recreated failure scenarios in the client’s command line interface (CLI), an open-source harness for agent-style coding workflows. The project focused on replicating where the CLI failed to use external APIs correctly, creating reproducible environments, and validating agent fixes through shell tests or unit test suites.

Learn how Turing recreated and verified failure scenarios where the client’s CLI had issues interacting with external APIs, building testable environments and verification pipelines for agent evaluation.

Reconstructing and Validating 200+ CLI Failures in API Usage

CLI traces evaluated across diverse open-source codebases.

200+

environments recreated to match user setup with minimal assumptions.

Dockerized

of tasks verified through unit test or shell test-based success and failure gating.

50%+

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

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

Deploying a multilingual AI assistant for internal knowledge access

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

Evaluating Computer-Use Agents With 900+ Paired Tasks and Structured Failure Modes

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

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

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

Training and Evaluating Extension Tool Use with 1000+ Prompts and RLHF Preference Pairs

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

Reconstructing and Validating 200+ CLI Failures in API Usage

Want to accelerate your business with AI?