Fuel Smarter Coding Agents with High-fidelity Training Data!

We offer data services for coding models powering AI coding agents, AI coding assistant tools and AI IDEs. Our team creates high-quality supervised fine-tuning datasets by analyzing, labeling, refining code snippets, dialogues, and programming tasks. This ensures accuracy, and optimal performance in coding-focused LLMs.

Fuel Coding Agents with Training Data!

We offer data services for coding models powering AI coding agents, AI coding assistant tools and AI IDEs.

Trusted by Industry Leaders Worldwide

Our Capabilities

Deliver curated datasets tailored for training and fine-tuning coding agents.

Supervised Fine-Tuning (SFT)

Human Preference Ranking (RLHF)

LLM Evaluation & A/B Testing

LLM Red Teaming

LTS GDS provides fine-tuned datasets, including custom prompts, response generation, and dialogue evaluation, to enhance Coding LLMs’ capabilities in code generation, source code analysis, and algorithm explanation. Our support includes:

Prompt generation.
Prompt verification.
Answer generation.
Answer verification.
Dialogue generation.
Dialogue evaluation.
Bug detection and fix suggestions.

Our experts evaluate and rank model-generated responses in programming contexts using Reinforcement Learning with Human Feedback (RLHF), based on quality criteria such as accuracy, algorithmic efficiency, executability, and language compliance. Key features:

Real-time human interactions.
Evaluation of single- or multi-turn conversations.
Customizable evaluation criteria: semantic accuracy, syntax compliance, performance optimization, and more.

LTS GDS offers data labeling services to evaluate model performance on programming tasks through A/B comparisons—between different model versions or against existing benchmarks. Key capabilities include:

Detailed comparisons between code generation models.
Evaluation based on correctness, performance, and coherence.
Support for both qualitative and quantitative analysis of model responses in specific programming scenarios.

LTS GDS identifies potential weaknesses in programming models, including bias, hallucinations, and unsafe content. Use cases include:

Insecure code generation.
Malicious or inappropriate suggestions (e.g., bypassing authentication, SQL injection).
Multi-turn testing using real-world scenarios.

Pre-Training

LTS GDS supplies large-scale, vetted datasets to build a strong foundation for coding models, enabling them to learn programming syntax, patterns, and general reasoning across multiple languages and domains.

Our offerings include:

Trajectory data collection
Data cleaning and deduplication
Data augmentation and diversification

Supervised Fine-tuning (SFT)

LTS GDS provides fine-tuned datasets to enhance coding LLMs’ capabilities in code generation, source code analysis, and algorithm explanation.

Our support includes:

End-to-end Prompt & Answer engineering (Generation & Verification)
Dialogue generation & evaluation
Code bug detection & fix suggestions

Human Preference Ranking (RLHF/DPO)

Our experts evaluate and rank model-generated responses in programming contexts using reinforcement learning with human feedback (RLHF), based on quality criteria such as accuracy, algorithmic efficiency, executability, and language compliance.

Key features:

Real-time human interactions
Evaluation of single- or multi-turn conversations
Customizable evaluation criteria: semantic accuracy, syntax compliance, performance optimization, and more

LLM Evaluation & A/B Testing

LTS GDS offers data labeling services to evaluate model performance on programming tasks through A/B comparisons, between different model versions or against existing benchmarks.

Key capabilities include:

Detailed comparisons between code generation models
Evaluation based on correctness, performance, and coherence
Support for both qualitative and quantitative analysis of model responses in specific programming scenarios

LLM Red Teaming

LTS GDS identifies potential weaknesses in programming models, including bias, hallucinations, and unsafe content.

Use cases include:

Insecure code generation
Malicious or inappropriate suggestions (e.g., bypassing authentication, SQL injection)
Multi-turn testing using real-world scenarios

Our Data Labeling for Coding Agents Workflow

Follow our expert-driven process to solve coding tasks at scale.

Requirements

Team Setup

Trial Tasks

Execution

Improvement

From the beginning, vetted engineers of GDS define the project requirements. We meet with the client for initial training and conduct Q&A sessions to clarify the project guideline documentation.

We begin by setting up the project team, including both internal and vendor teams, and then assign tasks based on the required programming languages. We conduct training sessions for both our delivery team and vendors to clarify guidelines and answer questions. Finally, we hold meetings with both teams to align on the execution methodology.

We carry out trial tasks and deliver them to the client. After receiving feedback, we organize follow-up meetings with internal and external delivery teams. Based on the results and feedback, we update the guidelines to address new scenarios or edge cases identified during this phase.

We assign tasks to vendors and enforce LTS GDS deadlines. LTS GDS conducts random reviews of vendor-completed tasks. We then deliver the output to the client, who reviews it in batches, typically consisting of around 100 tasks. The client's acceptance criteria are as follows:

- If a batch achieves a ≥90% acceptance rate, the entire batch is approved.

- If a batch has a ≥90% rejection rate, the entire batch must be reworked and resubmitted.

We report externally caused rejections (unclear descriptions, hidden requirements) to the client for clarification. Additionally, we meet every other day to address and resolve internal errors discovered during the execution process.

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Our Experts

Our experts integrate domain knowledge, advanced programming expertise, and framework-level understanding to deliver validated datasets for coding agents.

Ryan Le

Gen AI Manager

Coding, STEM & Engineering, Physical AI & Robotics

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

Adrian Tran

Expert

Healthcare & Life Sciences, STEM & Engineering

Why LTS GDS?

Trust our expert-verified traing data to accelerate coding LLM development..

Superior Quality

Rigorous QA processes are implemented to build precise datasets with up to 99% accuracy, specifically designed for training high-performing coding agents.

Leading Experts

100+ seasoned developers mastering in SQL, Python, C#, JavaScript, TypeScript, Bash, .NET, Scala work tirelessly to ensure coding agents to generate, analyze, and refine code, and execute multi-step workflows.

Quick Team Ramp-up

LTS GDS guarantees to build up a dedicated team consisting of a battle-hardened PM and up to 200 man-months from in-house team and our partner network for large-scale projects within 2 weeks.

Cost-effectiveness

Global businesses can get IT experts to adapt pre-trained models to coding agents with optimal budgets in light of the expense gaps of Vietnam outsourcing market and favorable tax policies.

Wall of Achievement

99%

Accuracy

50M+

Lines of Code

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 and Evaluation Data

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 examples of how our AI training data services have turbocharged more advanced coding agents.

22 - 06 - 2026

Prepare and Evaluate Long-horizon trajectory dataset for AI coding agents

Client overview The client is a leading multinational technology company based in China, developing an AI coding agent designed to solve long-horizon software engineering tasks across multiple programming languages and...

AI Training Data

19 - 06 - 2026

Dataset Provision and Evaluation for AI Agent CUA Training

Client overview Our client is a research group at a leading U.S. technology university. They are developing an AI Agent in the form of a Computer Use Agent (CUA), capable...

AI Training Data

19 - 06 - 2026

Simulated App Usage Recording for Smarter AI Training

Client overview Our client is a U.S.-based research lab working on human-AI interaction. They want to build AI systems that can use digital platforms in ways that look and feel...

AI Training Data

19 - 06 - 2026

Code Generation

Client overview The client is a trusted data solutions partner headquartered in the Netherlands. The company specializes in supporting all stages of AI development, from training to evaluation, by delivering...

AI Training Data

18 - 06 - 2026

AR/VR Motion & Environment Interaction Dataset Collection

Client overview The US client is implementing a project to collect user behavior data to train an AI model in a robotic pick-and-place environment. Participants use an AR/VR headset (PICO...

AI Training Data

18 - 06 - 2026

Website Data Collection & AI Agent Output Evaluation

Client overview A US-based AI client collecting realistic web-browsing interaction data to evaluate AI agent performance. The project focuses on validating step-by-step reasoning, action logic, screenshot fidelity, and final answer...

AI Training Data

18 - 06 - 2026

Generate Datasets for AI Coding Agent & Evaluate Responses

Client overview Our client is a U.S.-based pioneer in Data-Centric AI, specializing in delivering high-quality data services for advanced frontier AI and agentic systems. The company plays a critical role...

AI Training Data

23 - 02 - 2026

Large-Scale Gaze Data Collection for Hands-Free AI Systems

Client overview Our client is an Israel-based technology company focused on advancing hands-free interaction systems. Their goal is to improve how people communicate with digital devices using only eye movement,...

AI Training Data

11 - 12 - 2025

Simulated App Usage Recording for Smarter AI Training

Client overview Our client is a U.S.-based research lab working on human-AI interaction. They want to build AI systems that can use digital platforms in ways that look and feel...

AI Training Data

Explore other case studies

Our Tools and Technologies

Use advanced tools and custom-built systems to optimize training data workflows for coding agents.

FAQs about Data for Coding Agents

What are coding agents?

Coding agents are AI-powered systems designed to assist with software development tasks such as writing, analyzing, debugging, and optimizing code. Unlike traditional LLMs, coding agents can interact with tools, execute code, and handle multi-step workflows.

How do coding agents differ from coding LLMs?

Coding LLMs focus on generating and understanding code based on input prompts. Coding agents go further by integrating with external tools, maintaining context across tasks, and autonomously executing complex development workflows.

What types of data are required to train coding agents?

Training coding agents requires diverse datasets, including source code, code-text pairs, debugging scenarios, multi-turn dialogues, and tool interaction data. High-quality, structured data is essential for enabling reasoning and task execution.

What is fine-tuning for LLMs in coding?

Fine-tuning is the process of taking a pre-trained large language model and training it further on a curated dataset of source code or code-related tasks. This allows the model to specialize in programming-specific functions such as code generation, debugging, or documentation, etc.

What is RLHF?

Reinforcement Learning from Human Feedback (RLHF) is a method used to improve LLMs by incorporating human preferences. Following initial training, human feedback is integrated into this process to further train LLMs for better response performance.

What is the difference between SFT and RLHF?

Supervised Fine-tuning (SFT) involves training LLMs using labeled data to teach task-specific behavior. RLHF then follows, utilizing human feedback and reinforcement learning to refine outputs and align them with human values. SFT teaches what to say, while RLHF refines how to say it.

How does fine-tuning differ from prompt engineering?

Fine-tuning uses specific datasets to adjust an LLM’s parameters for specialized coding tasks. In contrast, prompt engineering focuses on crafting better input prompts to guide the model’s responses, without changing the model itself.

What types of coding tasks can fine-tuned coding LLMs perform?

Fine-tuned LLMs can generate code, provide answers, create dialogues, and evaluate logic. They can also translate between languages, generate documentation, and assist with DevOps scripts. When trained on specific codebases, they master domain-specific development tasks.

What are the benefits of fine-tuning coding agents with end-to-end training data services?

Fine-tuning coding agents with end-to-end training data services ensures higher accuracy, consistency, and task reliability. By covering the full data lifecycle from collection and labeling to evaluation, these services help coding agents better understand context, execute multi-step workflows, and integrate with tools more effectively.

What benchmarks are used to evaluate coding agents?

Coding agents are commonly evaluated using industry-standard benchmarks such as SWE-bench, HumanEval, MBPP (Mostly Basic Python Problems), BigCodeBench, and OSWorld. These benchmarks assess capabilities across code generation, bug fixing, reasoning, and real-world task execution.