DATA GENERATION
Human-generated training data.
Built to perform.
Every large language model is trained on human-generated data. The quality of your model reflects the quality of the people who built its training sets — and the rigor of the systems managing them.
500k+
Verified contributors across 155+ locales
>90%
Quality measures across scaled programs
>10%
Task-specific accuracy improvement per iteration
THE SERVICE
SFT and RLHF data, built from scratch
Data generation is the production of net-new training data built specifically for your model’s requirements. The two primary workflows are supervised fine-tuning and RLHF — with additional capability across the full scope below.
Prompt Engineering
Crafting and refining inputs that generate accurate, domain-appropriate model outputs.
Adversarial Data
Edge cases, stress scenarios, and adversarial prompts that expose and close model failure modes.
Edge Case Generation
Rare, underrepresented, or synthetic scenarios that real-world data cannot supply at volume.
Pre-Training Corpus
Human-authored and curated text for base model training before fine-tuning begins.
SUPERVISED FINE-TUNING
SFT
SFT trains a model on labeled examples of the exact behavior you want. It requires high-quality, domain-accurate instruction-response data — and that is where most programs run into problems at scale.
- Expert-created instruction and response datasets tailored to your use case
- Multilingual SFT data across 150+ languages with verified cultural accuracy
- Domain specialists for technical, legal, medical, and scientific content
LLMs
Computer vision
Multimodal
Code generation
Audio and speech
Document processing
REINFORCEMENT LEARNING (RLHF)
RLHF
RLHF improves model alignment by using human judgements — comparisons, rankings, corrections — to train a reward model that guides further learning.
- Pairwise comparisons, ranking tasks, and scalar ratings from qualified human raters
- Rater pools matched to the domain, language, and expertise level your model requires
- Inter-annotator agreement monitoring and full audit trails for compliance review
Pairwise comparison
Preference ranking
Scalar rating
Constitutional AI
Safety alignment
Direct preference optimisation
HOW IT WORKS
Designed for the full training lifecycle.
SFT and RLHF are not isolated tasks. They are sequential stages that build on each other — and Welo Data manages both ends of the pipeline with consistent quality standards throughout.
01
PROGRAM DESIGN
Scoped to your model and use case
We start by understanding your model architecture, target behaviors, and current gaps. Task design, contributor selection, and quality criteria are defined before any data is created.
Use case scoping
Task design
Contributor matching
Quality framework
02
DATA CREATION
Built by verified domain experts
NIMO-verified contributors create instruction-response pairs, preference rankings, adversarial prompts, and domain-specific demonstrations — with calibration tasks and gold-standard validation running throughout production.
SFT datasets
RLHF feedback
Adversarial prompts
Domain-specific content
Multilingual coverage
03
QUALITY AND ITERATION
Measured outcomes, every cycle
Each delivery includes quality metrics, inter-annotator agreement scores, and iteration recommendations. Programs improve with every cycle — and every quality event feeds back into future task design.
Multi-stage review
IAA tracking
Accuracy metrics
Iteration feedback
GET STARTED
Ready to scope your training data program?
Tell us about your model, your use case, and where you are in your pipeline.
WHY WELO DATA
The workforce behind the data matters.
Generated data is only as good as the people creating it. Welo Data’s contributor network is verified, credentialed, and monitored throughout every program.
500,000+ verified contributors
Every contributor passes through NIMO — verified identity, confirmed location, validated credentials — before any production access.
155+ locales, genuine expertise
Multilingual programs use native speakers and regional specialists — not translators approximating cultural context.
Domain specialists on demand
Medical, legal, financial, and technical datasets use credentialed subject matter experts, not generalists approximating domain knowledge.
Quality built into the pipeline
Calibration tasks, gold-standard validation, and inter-annotator agreement tracking run throughout — quality problems are caught before delivery, not after.
MEASURABLE OUTCOMES
Improvements you can track.
We report against defined metrics, not directional claims. Every program includes quality scores, accuracy benchmarks, and iteration recommendations at each delivery.
>10%
ACCURACY IMPROVEMENT
Task-specific accuracy improvement per iteration across SFT programs.
>65%
F1 SCORE AVERAGE
Average F1 scores on complex, emerging projects with nuanced domain requirements.
>90%
QUALITY MEASURES
Quality measures maintained across scaled programs with multi-stage validation.
CASE STUDIES
Results from real programs.
See how Welo Data has delivered SFT and LLM training data for leading technology companies.
LLM TRAINING / DATA GENERATION
Improving LLM Reasoning Through Expert-Level Research Prompts
A top AI company used expert-authored prompts and structured evaluation rubrics to improve LLM reasoning — delivering faster, higher-quality results with fewer tasks.
RLHF / ALIGNMENT
Improving Helpfulness in LLMs
Welo Data improved the relevance and alignment of LLM responses with user intent — a direct application of human feedback workflows to production model behavior.
FAQ
Common questions. Straight answers.
SFT uses labeled examples to teach a model specific behaviors directly. RLHF uses human preference signals — comparisons and rankings — to train a reward model that guides further learning. Most production pipelines use SFT first, then apply RLHF to refine alignment and output quality.
Data collection sources existing real-world inputs. Data annotation labels what has been collected. Data generation creates training data from scratch — instruction-response pairs, preference rankings, adversarial prompts — that serves specific training objectives and cannot be gathered from existing sources.
Yes. We run integrated programs where SFT data creation and RLHF feedback collection are managed as a single workflow, with consistent quality standards across both stages. This reduces handoff friction and keeps quality criteria aligned throughout.
Contributors are calibrated before production, gold-standard tasks are seeded throughout, and outputs go through multi-stage review with inter-annotator agreement tracked at every stage.
Yes. We manage programs across 150+ languages in parallel, with native speaker contributors and per-locale quality monitoring under unified quality standards. Our localization heritage means multilingual is a core capability, not an add-on.
NIMO is Welo Data’s workforce integrity platform. It verifies contributor identity, confirms location, validates credentials, and monitors behavior throughout production. NIMO runs across all Welo Data programs by default — it is the infrastructure our contributor network operates on, not an optional add-on.
Standard programs move from scoping to first delivery within a few weeks — contact us to discuss your specific requirements and timeline.
GET STARTED
Ready to build training data that performs?
Tell us about your model, your use case, and where you are in your training pipeline. We’ll scope a program that fits.