Response validation and scoring
Review responses for factual accuracy, completeness, tone, relevance, safety, and alignment with project requirements.
Supervised fine-tuning
High-quality demonstrations for the behavior you want.
Create and validate instructions, responses and multilingual examples that teach models to be accurate, helpful, safe and context-aware.
What we create
Training examples aligned with your target behavior.
Data programs are shaped around your use case, domains, languages, policies and definition of an ideal response.
Response drafting
Write accurate, helpful, and context-appropriate responses that demonstrate the ideal behavior your model should learn.
Multilingual fine-tuning data
Create and review fine-tuning examples across languages and locales to support more natural, culturally aware model behavior.
Instruction-response pairing
Match prompts with high-quality responses to create structured training examples for supervised fine-tuning workflows.
Instruction generation
Create clear, relevant, and task-specific instructions that reflect your use case, from open-ended prompts to complex workflow commands.
Bias and safety checks
Evaluate training examples for potential bias, unsafe content, sensitive wording, or policy risks before they are used for model training.
Built for adaptation
Demonstrations shaped around your product behavior.
We translate model goals into writer guidance, examples and review criteria that stay consistent across domains and languages.
01Assistant behavior
02Domain adaptation
03Multilingual quality
04Safety & policy alignment
How we deliver
From behavior brief to validated demonstrations.
A calibrated workflow keeps instructions realistic, responses high quality and safety risks controlled.
Define ideal behavior
Align tasks, domains, policies, languages, formats and quality criteria.
Create examples
Draft instructions and responses through trained native contributors.
Review & score
Validate factuality, helpfulness, tone, safety and consistency.
Structure & deliver
Provide clean training pairs with quality findings and documentation.
Build fine-tuning data for your target model behavior.
Share your use case, policies and example tasks. We’ll propose a focused data creation pilot.
Discuss an SFT data pilot ↗