LLM Training Data Services
Hansem Global develops and evaluates LLM training data, covering SFT, preference (RLHF/DPO), performance, and safety, with expert human input in Asian languages.
Why Hansem Global?
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Built by trainers who understand model behavior.
Our LLM data specialists are more than basic taggers. They work from how models actually behave, studying patterns, responses, and reasoning, to build SFT (instruction) and preference (RLHF/DPO) datasets, calibrating data standards through output analysis rather than fixed rules.
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One integrated pipeline, not isolated steps.
LLM quality isn't improved in a single pass, so we run SFT, RLHF, and evaluation as one connected workflow. SFT builds generation data, RLHF aligns behavior, and evaluation (benchmarks, human review, scenario tests) checks whether the model is ready for its intended use.
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Safety and policy woven into the data.
Our AI-safety-trained specialists work to reduce harmful content, bias, and policy violations, applying policy-guided SFT, safety-focused RLHF, and high-risk scenario testing under strict privacy and de-identification controls to support trustworthy deployment.
LLM Training Data Service Offerings
SFT Dataset Development
We build custom prompt–response datasets aligned to your objectives and domain. Coverage includes text tasks such as Open QA, summarization, and reasoning, and prompt engineering for image and video generation models, so the model learns task-appropriate response patterns and logical structure. Backed by multilingual domain experts and ISO-aligned quality controls, we also support multilingual tuning across languages.
Custom prompt–response data for domain tuning
LLM Performance Evaluation & A/B Testing
We assess reliability and accuracy using a structured set of quantitative and qualitative metrics. Applying core criteria such as relevance, accuracy, and usefulness, we score model outputs and identify where they fall short. Stage-by-stage A/B tests and competitor benchmarking give data-driven insight to guide model selection and an improvement roadmap.
Model evaluation and A/B testing against clear metrics.
LLM Safety & Trust Validation (Benchmarking)
We test the risk factors that undermine trust, including accuracy, factuality, safety, and bias, under conditions close to real production use. Key checks include hallucination detection and response-consistency evaluation. You get practical insight for performance tuning and risk management ahead of enterprise deployment.
Hansem Global?
Built by trainers who understand model behavior.
Our LLM data specialists are more than basic taggers. They work from how models actually behave, studying patterns, responses, and reasoning, to build SFT (instruction) and preference (RLHF/DPO) datasets, calibrating data standards through output analysis rather than fixed rules.
One integrated pipeline.
LLM quality isn't improved in a single pass, so we run SFT, RLHF, and evaluation as one connected workflow. SFT builds generation data, RLHF aligns behavior, and evaluation (benchmarks, human review, scenario tests) checks whether the model is ready for its intended use.
Safety and policy woven into the data.
Our AI-safety-trained specialists work to reduce harmful content, bias, and policy violations, applying policy-guided SFT, safety-focused RLHF, and high-risk scenario testing under strict privacy and de-identification controls to support trustworthy deployment.
Preference Data Development (RLHF/DPO)
We build preference data for RLHF and DPO. By ranking model outputs on human preference, the model learns which response styles are preferred, supporting more natural and consistent answers. We also build scenario-based datasets for single- and multi-turn conversations to train against realistic user interactions.
Human preference ranking for RLHF and DPO.
AI Data Experts and Global Delivery
LLM data work depends less on volume than on judgment: what makes a good response, and what crosses a policy line. Our project managers translate these standards into clear, workable guidance for specialists, and keep judgment consistent across long-running projects where criteria evolve. Delivery runs across teams in Korea, Vietnam, China, Spain, Argentina, and the US, moving continuously across time zones, with depth concentrated in Asian languages (Korean, Japanese, Chinese, Vietnamese, Indonesian, and Thai) and other languages supported on request.
Technology Infrastructure and Security
Our infrastructure is built for enterprise data security and quality at scale. Sensitive data is handled under ISO 27001–aligned controls used in regulated fields like healthcare, legal, and finance, with a closed working environment and NDA available on request. We run the full LLM data lifecycle, from data collection and SFT generation to RLHF and evaluation, through a dedicated workflow that reduces quality variance and feeds a review loop, with full traceability. When clients require it, our teams work directly within the client's own platform.
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