Supervised fine-tuning data
High-quality prompt-response demonstrations, rewrites, transformations and task-specific examples designed to teach the behavior you want.
Train & fine-tune models
Turn model failures into better training data.
Create expert-generated, preference, supervised fine-tuning and multimodal datasets around your model’s actual gaps, not a generic labeling queue.
Illustrative model loopmodel release
01Findfailure signals
02Createtargeted data
03Reviewexpert QA
04Measurerelease evidence
Model-ready
datatraceable by design
datatraceable by design
Failure-ledTarget weak behaviors
Expert-routedMatch the right judgment
QA-readyDeliver with evidence
Built around the real work
Capabilities that move a model from promising to useful.
Choose the parts of the data and quality workflow that match the behavior, market or modality you are building for.
BUILD
Designed for the next decision, not the last task.
Every capability is shaped around downstream use: what the model needs to learn, what reviewers need to judge and what your team needs to ship with confidence.
Talk through your workflow01
Preference & alignment data
Pairwise rankings, scalar ratings, critiques and chosen/rejected examples for preference optimization and alignment workflows.
Reasoning & expert demonstrations
Domain-grounded problem solving, structured explanations, tool-use examples and expert-authored reference answers for complex tasks.
Data curation & enrichment
Deduplication, taxonomy design, cleaning, filtering, metadata enrichment, difficult-example mining and synthetic-data validation.
Safety & policy data
Policy-aligned examples, edge cases, refusals, adversarial prompts, red-team findings and reviewer rationale.
Multimodal training data
Linked image-text, video-text, speech-text and cross-modal tasks such as captioning, VQA, grounding and response evaluation.
Data contractModel-ready structure
failure_taxonomylinked
expert_examplesreviewed
evaluation_holdouttraceable
release_notesattached
Every asset has a reason to exist.From failure discovery to the next model decision.
The continuous data engine
Build the feedback loop, not just the dataset.
A mature training-data program uses evaluation to discover where the model fails, then creates new examples targeted at those weak points.
- Failure taxonomy stays connected to examples.
- Expert work is routed by ambiguity, risk and domain.
- Train, validation, test and holdout assets stay traceable.
The operating model
A production workflow with calibration built in.
Designed for evolving rubrics, model versions and edge cases.
Find
Use evaluation and production evidence to identify meaningful model failures.
Classify
Group failures by behavior, domain, risk and the kind of data that could fix them.
Create
Curate existing examples or create new demonstrations, preferences and edge cases.
Review
Apply qualified review, automated checks, QA and adjudication before release.
Re-evaluate
Measure the next model version and feed important failures back into the loop.
Quality that holds up in production
Make model improvement inspectable.
Quality is not only the final score. It is the connection between the behavior you want, the example that teaches it, and the evidence that shows whether it improved.
- Representative prompts and realistic edge cases
- Clear rubrics for correctness, safety, style and task execution
- Known limitations, provenance and QA notes attached to delivery
Training assetsPrompt / response JSONL, preference pairs, critique + rewrite records
Evaluation assetsGold responses, error taxonomy labels, train / validation / test splits
Multimodal assetsLinked manifests, cross-modal labels and synchronized metadata
Operating recordQA notes, provenance fields and known limitations where relevant
What arrives at the end
Deliverables your team can use.
Clear outputs, stable structure and the context needed to move the work into training, evaluation or production.
01Prompt / response JSONL
02Chosen / rejected preference pairs
03Scalar ratings and rubric dimensions
04Critique + rewrite records
05Gold responses and reference solutions
06Error taxonomy labels
07Train / validation / test splits
08Multimodal asset manifests
09QA notes and known limitations
Where it fits
Instruction following
Teach format, completeness, policy and task execution.
Domain assistants
Create legal, finance, healthcare and technical examples with specialist context.
Safety alignment
Build safer response patterns from policy cases, red-team findings and reviewer rationale.
Multimodal reasoning
Connect image, video, speech and text evidence to tasks that can be evaluated.
Start with the outcome
Tell us what capability you need to improve.
We’ll translate your model goal into the data types, expert profile, QA design and delivery format required to move it.