HUMAN INTELLIGENCE. BETTER AI.

The right data.
The next breakthrough.

Training data with context. Labels with meaning. Testing that asks the difficult questions. Build better AI with human judgment in the loop.

Access your workspace
From raw inputs to models you can evaluate.
ANNOTATION / VISUAL EXAMPLE
OBJECT LABELHUMAN REVIEW
EVALUATION / ILLUSTRATIVE
AResponse quality
BInstruction following
CEdge-case review
THE HUMAN LAYERDATA → JUDGMENT → LEARNING
BUILT AROUND YOUR DATA
Computer visionLanguage modelsMultimodal datasetsModel evaluation
01 / Our expertise

Give your models
more to work with.

Clear instructions, thoughtful annotation and purposeful testing. Workflows shaped around the behavior you want your models to learn.

01 / STRUCTURE

Data labelling

Turn unstructured inputs into consistent training examples with contextual labels and review criteria.

  • Image & video annotation
  • Text classification
  • Entity & intent labelling
02 / ALIGNMENT

Human feedback

Capture human preferences and expert judgment to make model outputs more useful, relevant and aligned with your goals.

  • Response comparisons
  • Preference ranking
  • Rubric-based review
03 / CONFIDENCE

Testing & evaluation

Look beyond a single benchmark. Explore how your model behaves across real tasks, difficult prompts and unexpected inputs.

  • Task-based evaluation
  • Edge-case exploration
  • Output quality review
02 / The process

Human insight.
Deliberate by design.

Good training data starts with a shared understanding of the task. Every stage is an opportunity to sharpen the signal.

[ 01 ]

Define what good looks like

Translate your objectives into annotation instructions, examples and measurable evaluation criteria.

[ 02 ]

Build the right signal

Label, compare and review your inputs with a workflow matched to the data and domain.

[ 03 ]

Review. Learn. Refine.

Use disagreements, edge cases and evaluation results to improve the next iteration.

03 / Applications

Different challenges.
The same attention to detail.

Language & reasoning

Instructions, response quality and the nuances that make an answer useful.

Vision & perception

Objects, scenes and contextual labels for visual learning tasks.

Conversational systems

Intent, relevance and the human context behind an interaction.

Evaluation datasets

Purposeful test cases that make model behavior easier to understand.

A clearer signal starts here.

Your data. Your goals. A workspace for the next step.

Enter your workspace
A few things to know

Questions,
with context.

What kinds of data can be labelled?

Annotation workflows can address image, video, text and multimodal inputs. The right labelling approach depends on the task, domain and intended use of the dataset.

How does human feedback help model evaluation?

Human review adds context to output comparisons, instruction following and task-specific quality. Clear rubrics help turn subjective judgments into usable evaluation signals.

Where can I access my workspace?

Use the Client login at the top of this page to reach the workspace sign-in screen.