Dataset preparation
Clean, structure and deduplicate source data. Define formats, selection criteria and coverage before production.
- Data selection and quality review
- Structured exports and dataset documentation
01 / AI model training
Training data built for your model’s task.
We turn raw material into structured, reviewed examples for fine-tuning and evaluation, with clear guidelines and traceable decisions.
Discuss your projectWhat we deliver
From source data
to reviewed examples.
Start with a sample of the task and your quality criteria. We design the workflow around the decisions your model needs to make.
Annotation and human feedback
Build task-specific labels or examples. Give reviewers clear instructions for difficult cases.
- Text classification and entity labeling
- Instruction examples and response comparisons
Fine-tuning and evaluation
Fine-tune a model on task-specific examples, then evaluate its outputs against a rubric agreed with your team.
- Quality rubrics and edge-case tests
- Error categories and a prioritized improvement list
Example project
Evaluate a support assistant.
Use representative questions to check whether the assistant answers accurately, follows policy and knows when to ask for help.
We define the rubric with you, review a sample and turn repeated errors into examples for the next training round.
EVALUATION RUBRICILLUSTRATIVE
- Grounding
- Does the answer match the policy?
- Completeness
- Does it answer the question?
- Escalation
- Does it flag cases that need a person?
Start a conversation
What should your model learn next?
Tell us where your model or search system falls short. We’ll help you define a focused first project.