Define what good looks like.
Share your data, model failure modes, taxonomy, and delivery requirements. Together, we turn them into a practical annotation rubric and acceptance criteria.
Our Approach
Your data. Your requirements. A team built for both.
We design the workflow, assemble and train the team, and manage delivery. Human judgment stays at the center, from the first example to the final review.
Designed for Your Reality
Start with the problem. Build the right process.
A taxonomy is only useful when people apply it consistently. A tool is only useful when it fits the task. And a dataset is only ready when it meets your requirements.
We work backward from those requirements to define the team, tooling, rubric, and review process your project needs.
Domain-matched talentTask-specific trainingManaged delivery
Calibrate on real examples.
We assemble the team, train on representative samples, and resolve disagreements early. Difficult cases become examples the whole team can learn from.
Annotate with the right tools.
Use manual, model-assisted, or hybrid workflows according to the task. Human reviewers correct the labels and protect the context automation can miss.
Review, refine, and deliver.
Adjudicate disagreements, identify recurring errors, and update guidance. We deliver the dataset in the agreed format with the review process made clear.
The Human Layer
Intelligence begins with human judgment.
The right people, trained for your task. The right tools, shaped around your data. A feedback loop that makes the work stronger with every cycle.
DESIGN
A workflow that fits.
Your taxonomy, risk, and delivery requirements determine the process. Manual, model-assisted, or a combination of both.
CALIBRATE
Expertise, calibrated.
Annotators are selected for the domain, trained on real examples, and coached through the edge cases that matter.
REFINE
A stronger next cycle.
Disagreements inform the rubric. Review informs the training. Each cycle strengthens consistency across the dataset.
Working Together
A few practical questions.
Yes. We can work with your existing data for annotation, cleaning, enrichment, quality review, or a combination of these services. The workflow starts with what you already have and what needs to improve.
The choice follows the taxonomy, risk, data characteristics, and delivery requirements. Model-assisted tools can support the process where they are useful, while human review checks the result against the task rubric.
Task-specific training, calibration on shared examples, adjudication, and recurring feedback. Disagreements help us identify unclear guidance and where the team needs more support.
A description of the task, a representative sample when appropriate, the annotation schema if one exists, quality expectations, the expected volume, and any delivery constraints.
Build What Comes Next
The next breakthrough starts with better data.
Show us the data, the edge cases, or the gap. We’ll help shape the workflow that moves you forward.
Talk about your project