Expert-Annotated Training for Advanced Models
Complex chain-of-thought explanations, multi-step reasoning, and cultural context
Discuss Your Data RequirementsHigh Quality Reasoning
LLMs need more than labels. They need structured reasoning, cultural context, and clear explanations
Reasoning annotation requires validating logical steps and identifying flawed inferences—analytical work that demands rigorous training.
Chain-of-Thought
Step-by-step explanations of how to reach conclusions—not just correct answers.
Multi-Step Reasoning
Complex problems decomposed into sequential, logical steps.
Cultural Context
Genuine understanding of idioms, references, and contextual meaning.
Explanatory Depth
Why answers are correct, why alternatives fail, and how to articulate reasoning.
Reasoning Data. Engineered, Not Assembled.
Structured Chain-of-Thought Annotation
Our annotators follow rigorous frameworks to document reasoning in consistent, machine-readable formats. Every step is explicit. Every logical leap is explained.
Domain-Matched Annotators
Multi-step reasoning varies by domain—mathematical proofs demand different approaches than legal analysis. Annotators are matched to fields where they hold verified expertise.
Expert reasoning data
Local Cultural Expertise
Our Ghana-based team brings authentic African cultural context, while our broader network covers additional regions. No outsourced translation. No surface-level localization.
Genuine cultural nuance
Iterative Guideline Development
Reasoning data is hard to specify upfront. We work collaboratively with your team—annotating samples, refining guidelines, calibrating quality—until the output matches what your model needs.
Where This Fits
Instruction-Tuned Models
Train models that follow complex, multi-step instructions accurately.
Conversational AI
Build assistants that understand context, nuance, and user intent.
Educational Applications
Develop models that can explain their reasoning to learners.
Multilingual & Multicultural Products
Train models that work across languages and cultural contexts without losing meaning.
Ready to Move Towards Smart Data?
Your LLM needs training data as sophisticated as the problems it is solving. Let us discuss how our reasoning-first approach can improve your model depth of understanding.