What you provide.
Model outputs, evaluation questions and acceptance criteria.
AI Model Evaluation
Evaluate model behavior against a repeatable, project-specific rubric.
Request a SampleWhat the service does
From raw input to reviewable structure.
Evaluate model behavior against a repeatable, project-specific rubric. Labels and instructions are calibrated with your team on a small pilot before larger batches begin.
Typical tasks
- Test-set preparation
- Rubric-based scoring
- Error categorization
- Reviewer calibration
What you receive.
Scored records, error categories and a review summary.
Demo / Illustrative example
Make the output tangible.
This synthetic example shows the structure of a record. It is not a client dataset or a claim of project performance.
Labels with a clear purpose.
Test-set preparation is defined in the project guidelines. Ambiguous examples are flagged for review instead of silently forced into a category.
{ "output_id": "demo-1", "instruction_following": "pass", "factuality": "review" }Human-in-the-loop quality
Quality is a process. Not a percentage on a page.
Acceptance thresholds, review methods and sampling are agreed for each project. Calibration happens before volume.
Annotator review
Peer review
Quality reviewer
Sample audit
Client feedback loop
We document uncertainty and disagreement, revise guidelines with your team and keep an audit trail of corrections. Specialist medical, legal or financial review is available subject to project requirements and qualified reviewer availability.
Use cases
A fit for your data workflow.
AI product teamsResearch teams
Supported formats
Agree the schema first.
JSONLCSVReport
Shared responsibilities.
Clear delivery options.
You retain responsibility for source rights, lawful access, required approvals and intended use. We agree secure transfer, retention and deletion arrangements in the project scope.
- Client-approved guidelines and representative inputs
- A project owner for edge-case decisions
- An agreed acceptance rubric and sample audit
- Pilot, batch or milestone-based delivery
- Versioned exports and a documented handover
Project questions
Scope the work with confidence.
What do we need to provide?
Model outputs, evaluation questions and acceptance criteria. You also provide lawful access and usage rights, security requirements, acceptance criteria and a project owner who can resolve ambiguities.
How is annotation quality measured?
We agree a task-specific rubric, calibration pilot and sampling plan. Peer review, quality review and client feedback inform acceptance. No universal accuracy percentage is advertised.
Can you handle specialist subject matter?
Specialist medical, legal, financial or expert RLHF work is available subject to project requirements and qualified reviewer availability. Suitability is confirmed during scoping.
What delivery options are available?
Pilot batches, milestone-based deliveries or a scoped recurring workflow. Typical formats include JSONL, CSV, Report; exact schemas, tools, volumes and timelines are agreed before starting.
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