01
Corrected responses
Produce reviewed responses for tasks where the model missed a requirement, made an error, or failed to complete the work. Capture the correction and the reason it matters.
Training data / Model failure analysis
Find the failure.
Build the data.
Turn recurring model failures into a focused data production brief. We create corrected responses, additional conversations, and difficult examples tied to the behaviors your team needs to address.
What we produce
Data for a specific problem.
A recurring error needs a clear production target. We connect each batch of training data to an observed failure pattern, the intended behavior, and concrete acceptance criteria.
02
Additional conversations
Collect or author conversations that exercise the missing behavior, including clarification, recovery, language switching, and multi-turn instruction following as the project requires.
03
Difficult examples
Build variations around recurring failure cases. Change the context, constraints, or ambiguity to cover the conditions your team wants the training data to address.
Execution & quality assurance
A defined failure.
A managed production workflow.
We connect evaluation findings to contributor instructions, quality review, and structured delivery. Each production brief stays tied to the behavior it is intended to teach.
- 01
Categorize failures
Review supplied examples and group recurring errors by task, behavior, and failure type.
- 02
Write the brief
Define the desired response, example coverage, contributor qualifications, and review criteria.
- 03
Produce & review
Create examples, check corrections and labels, and resolve ambiguous cases against the brief.
- 04
Support re-evaluation
Deliver training data separately from held-out evaluation examples so your team can assess the next model version.
What you receive
Traceable examples.
Ready for your pipeline.
Receive reviewed data organized around the failure categories and formats your team uses, with training and evaluation material kept separate.
- Failure categories and collection brief
- Agreed error definitions, target behaviors, coverage requirements, and acceptance criteria for the data production work.
- Reviewed training examples
- Corrected responses, conversations, or difficult cases with source references, category labels, and review status in your required schema.
- Separate evaluation material
- Held-out examples when included in scope, with explicit split labels and overlap checks against the training deliverable.
Start with a recurring failure
Bring the examples
your model gets wrong.
Share the failure cases, desired behavior, and training format. We will scope a focused data collection and review workflow around the problem.
Scope your training-data project