What Makes Hansem Global Different
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High-Complexity SFT/RLHF, Built by Expert LLM Data Trainers
- • We do not use basic taggers. Our LLM data trainers understand model behavior—patterns, responses, and reasoning.
- • We build SFT (instruction) and RLHF (preference/policy) datasets and calibrate data standards through output analysis to drive measurable gains.
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End-to-End Training Data Operations
- • LLM quality is not improved in a single step.
- • We run one integrated pipeline: SFT → RLHF → Evaluation.
- • SFT builds high-quality generation data, RLHF aligns behavior, and evaluation (benchmarks, human review, scenario tests) validates business readiness.
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Policy and Safety by Design
- • Safety, policy compliance, and regulatory readiness are built into the data—by default.
- • AI safety–trained specialists mitigate harmful content, bias, and policy violations, with strict privacy and de-identification controls.
- • Policy-led SFT, safety-focused RLHF, and high-risk scenario testing support trustworthy LLM deployment.
LLM Training Data Service Offerings
SFT Dataset Development
We build custom prompt–response datasets aligned to your objectives and domain requirements. Coverage includes text tasks (Open QA, summarization, reasoning) and high-precision prompt engineering for image/video generation models, enabling the model to learn task-appropriate response patterns and logical structure.
Supported by multilingual domain experts and ISO-aligned quality controls, we also allow multilingual model tuning to optimize LLM performance for your business.
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. Using core criteria such as relevance, accuracy, and usefulness, we consider model outputs and identify areas for improvement.
Stage-by-stage A/B tests and competitor benchmarking provide data-driven insight to guide model selection and an LLM improvement roadmap.
Relevance, accuracy, usefulness—measured
LLM Safety & Trust Validation (Benchmarking)
We verify risk factors that undermine trust—accuracy, factuality, safety, and bias—under conditions close to real production use. Key tests include hallucination detection and response-consistency evaluation.
The result is practical insight for performance tuning and risk management, enabling safer and more effective enterprise deployment.
Hallucination and bias risk, validated
Preference Data Development (RLHF/DPO)
We improve response quality to match user preferences using RLHF/DPO. The model learns preferred response styles by ranking outputs based on human preference—improving naturalness and consistency.
We also build scenario-based datasets for single- and multi-turn conversations, helping your model continuously improve user satisfaction.
Human preference ranking for better answers
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Technology Infrastructure and Security
We maintain a structured infrastructure that prioritizes enterprise requirements for data security and quality management.
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Enterprise-Grade Security: We apply ISO 27001–aligned controls required in regulated industries, including healthcare, legal, and finance. Upon request, we provide a closed working environment and execute an NDA as standard.
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LLM-Specific Quality Management: We run the full lifecycle—data collection → SFT generation → RLHF evaluation → LLM evaluation—through an LLM-dedicated workflow to reduce quality variance and maintain a feedback loop that improves model performance.
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Flexible Infrastructure: We can integrate with customer platforms and tailor the environment to maximize efficiency and end-to-end traceability.
Explore More
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