Ryan Le
Gen AI Manager
Coding, STEM & Engineering, Physical AI & Robotics
Data Solutions for STEM AI Built on Expert-level Reasoning
LTS GDS delivers expert-led STEM datasets with strong training alignment, enabling accurate reasoning for advanced AI models
Data Solutions for STEM
LTS GDS delivers expert-led STEM datasets for advanced AI models.
Trusted by Industry Leaders Worldwide
Our Capabilities
E2E AI training data solutions for life sciences, combining domain experts with scalable data pipelines to elevate science-specialized models.
Data Collection
LTS GDS creates high-quality human-authored datasets across core STEM domains, designed to power accurate STEM reasoning.
Our expert-led STEM datasets across maths, physics, chemistry, biology, health & life sciences, computer science, and engineering include:
- Multimodal image- and text-based STEM queries (diagrams, lab setups, equations, engineering schematics, research papers, documentation, report, etc)
- Problem-solution pair generation with stepwise reasoning
- Synthetic + real-world dataset collection for diverse STEM scenarios
STEM Annotation
LTS GDS delivers annotated STEM datasets that improve reasoning, interpretability, and training alignment for advanced VLM/AI models.
Our offerings include:
- Chain-of-Thought (CoT) and stepwise reasoning annotation
- Symbolic reasoning and equation-based labeling
- Annotation for proofs and derivations (maths & physics-heavy tasks)
- Multi-level difficulty tagging for curriculum-style learning
Expert Validation & STEM QA
LTS GDS ensures the quality of STEM datasets through rigorous QA workflows and multi-layer validation by domain experts.
Our offerings include:
- Lean-based proof QA for mathematical and logical correctness
- Cross-validation by subject-matter experts (SMEs)
- Consistency checks for multi-step reasoning outputs
- Error analysis and dataset refinement loops
Data for Model Alignment (SFT & RLHF)
LTS GDS optimizes AI training data for SFT and RLHF to elevate training alignment and overall performance of STEM-specific models.
Our offerings include:
- Dataset preparation for SFT
- Human feedback collection for RLHF pipelines
- Preference ranking and response scoring
- Alignment tuning for STEM reasoning accuracy
Benchmarking & Evaluation Data
LTS GDS designs rubric-aligned benchmarks and evaluation datasets to measure real-world performance across STEM domains.
Our offerings include:
- Rubric-aligned benchmarks for STEM tasks
- Test sets for reasoning depth and correctness
- Custom evaluation frameworks for maths and science QA
- Adversarial and edge-case dataset creation accuracy
Our Experts in STEM
Our experts from elite academic backgrounds integrate domain knowledge, intensive expertise, and framework-level understanding to deliver validated datasets for STEM-specific models.
Elly Tran
Project Manager
Physical AI & Robotics, Healthcare & Life Sciences
Andy Nguyen
Advisor
Coding, STEM & Engineering, BFSI
Bach Le
Expert
Physical AI & Robotics, Computer Science
Christina Vu
Expert
STEM & Engineering, Physical AI & Robotics, BFSI
Chloe Tran
Expert
Legal & Social Sciences, Education & Languages
Lucas Pham
Expert
Coding, STEM & Engineering
Daniel Nguyen
Expert
Coding, BFSI, Physical AI & Robotics
Felix Vu
Expert
Arts & Creative, Physical AI & Robotics
Christina Vu
Expert
Healthcare & Life Sciences, STEM & Engineering
Why LTS GDS for Your Data Solutions for STEM Projects?
Built for teams that need expert data, consistent quality, and scalable STEM data pipelines.
Expert STEM Talent
Work with a network of researchers, engineers, and technical specialists with strong academic and industry backgrounds across math, physics, chemistry, biology, and engineering to build high-quality datasets.
Superior Data Quality
Multi-layer QA and expert validation ensure consistent STEM datasets, especially for complex reasoning tasks like Chain-of-Thought and symbolic problem solving.
Scalable Delivery & Integration
Quickly ramp up teams and scale from small research datasets to large enterprise volumes, with flexible infrastructure, and seamless API integration with existing ML workflows.
Cost-Effective
Leverage Vietnam’s strong STEM talent pool and flexible engagement models to optimize costs while maintaining high-quality AI training data for large-scale projects.
Wall of Achievement
99%
Accuracy
100M+
Data Units
11
Countries
500+
Projects
Benchmark-ready Training Data
We deliver data labeling aligned with benchmark standards to ensure your datasets are built for accurate evaluation and high-performing AI.
Benchmark-centric Pipelines
We design custom data labeling workflows tailored to the strict demands of leading industry benchmarks, including OSWorld, GAIA, SWE-bench, COCO, and MMMU.
Zero Data Contamination
Our stringent filtering protocols prevent benchmark test data from leaking into your training pipeline, protecting model integrity and evaluation validity.
Expert-in-the-loop (HITL)
We bridge the gap between training and benchmark success by leveraging subject matter experts to ensure nuanced reasoning and domain-specific accuracy for AI models.
Set a New Standard for Your Training & Evaluation Data
Run Free Pilot Core QA Metrics for Dataset Evaluation and Benchmark Readiness
A structured QA framework to evaluate dataset quality across accuracy, knowledge, security, and safety before model training and benchmarking.
Quality
We assess dataset quality through evaluation of accuracy, completeness, and timeliness, so the dataset is reliable and ready for model training.
Knowledge
We examine data relevance, diversity, and depth, supported by experienced AI trainers with strong domain expertise and language proficiency.
Security
We enforce strict data security standards by evaluating privacy protection measures and ensuring full compliance with regulations and governance frameworks.
Safety
We identify and mitigate risks such as bias, toxicity, and hallucinations, ensuring datasets are safe, responsible, and aligned with real AI deployment standards.
Quality
We assess dataset quality through evaluation of accuracy, completeness, and timeliness, so the dataset is reliable and ready for model training.
Knowledge
We examine data relevance, diversity, and depth, supported by experienced AI trainers with strong domain expertise and language proficiency.
Security
We enforce strict data security standards by evaluating privacy protection measures and ensuring full compliance with regulations and governance frameworks.
Safety
We identify and mitigate risks such as bias, toxicity, and hallucinations, ensuring datasets are safe, responsible, and aligned with real AI deployment standards.
Our Case Studies
Explore real-world success stories where our data annotation services powered innovative AI solutions across industries.
23 - 02 - 2026
Client overview Our client is a South Korea–based AI company providing intelligent solutions across multiple industries. For this project, they were building a computer vision system focused on construction site...
23 - 02 - 2026
Client overview Our client is developing a computer vision system designed to monitor operational environments such as warehouses and manufacturing facilities. Their system focuses on detecting forklifts during active operations,...
23 - 02 - 2026
Client overview Our client is developing a computer vision solution designed to recognize and classify drill bit markers from visual data. These markers are critical for identifying drill bit types,...
23 - 02 - 2026
Client overview Our client is developing a computer vision system that requires precise identification of multiple object types within structured images. The system depends on accurate annotation to detect and...
23 - 02 - 2026
Client overview Our client is a Singapore-based company developing an AI system to support building inspection and structural assessment. The goal of the project was to train a computer vision...
12 - 01 - 2026
Client overview Our client is a university in Italy conducting a government-funded research project focused on insects, larvae, and disease transmission. The research aims to improve early detection and analysis...
12 - 01 - 2026
Client overview Our client is a Korean company specializing in digital twin and LiDAR solutions for various domains. The client already had raw image data collected from agricultural environments but...
12 - 01 - 2026
Client overview Our client is a Singapore-based company that provides data solutions for intelligent AI models. Their work supports a wide range of computer vision applications, including retail analytics and...
12 - 01 - 2026
Client overview Our client is a leading perception software company headquartered in Korea. They are focused on advancing autonomous vehicle (AV) technology and already work with large amounts of transportation...
11 - 12 - 2025
Client overview The construction industry is adopting digital transformation at an increasing pace. One of the most significant advancements is the use of 4D digital twin platforms, which combine design...
11 - 12 - 2025
Client overview The client is a Japanese company specializing in industrial waste sorting, processing, and recycling. They handle large volumes of mixed waste collected from factories, construction sites, and urban...
11 - 12 - 2025
Client overview The client is a Singapore-based manufacturer specializing in the sorting, processing, and recycling of electronic waste. Their operations focus on handling everything from microchips to power sources, with...
Our Tools and Technologies
Powered by advanced tools and secure platforms to deliver speed, accuracy, and full transparency.
FAQs about Data Solutions for STEM
What are STEM datasets, and why are they critical for AI models?
STEM datasets are structured AI training data covering disciplines like math, physics, chemistry, and biology, designed to teach models how to reason through complex problems rather than just predict outputs. Unlike general datasets, they require precise logic, domain knowledge, and structured explanations to support accurate STEM reasoning in actual applications.
What are Chain-of-Thought datasets used for in STEM AI?
Chain-of-Thought (CoT) datasets enable models to break down problems into stepwise reasoning steps, improving both accuracy and explainability. This is especially important in STEM tasks such as solving equations, deriving formulas, or analyzing scientific scenarios, where intermediate reasoning matters as much as the final answer.
How are STEM datasets used across training and evaluation?
STEM datasets are used not only for model training, such as Supervised Fine-Tuning (SFT) and RLHF, but also for evaluation through rubric-aligned benchmarks that measure reasoning accuracy, consistency, and the ability to handle problem complexity.
What makes high-quality STEM datasets different from general datasets?
High-quality STEM datasets emphasize logical consistency, symbolic reasoning, and domain accuracy, often incorporating proofs, derivations, and structured explanations. They also require validation by domain experts and rigorous QA processes to ensure correctness across multi-step reasoning tasks.
What domains are typically included in STEM data solutions?
STEM data solutions cover diverse domains, including mathematics, physics, chemistry, biology, health & life sciences, computer science, and engineering disciplines such as mechanical, electrical, and industrial engineering, each requiring specialized datasets to support accurate reasoning and model performance.
Awards & Certifications
Train STEM-specific models with data built for reasoning and alignment
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