Our Approach
We follow a thoughtful approach to designing a solution that fits your needs
Strategic Groundwork for Successful Annotation
Designing the Process
We believe that successful data annotation and processing starts with a deep understanding of the problem space and a carefully designed workflow.
Our approach ensures that the final outcome is not just "labelled data", but a strategic data asset that powers long-term machine learning and operational success.
By thinking ahead and planning for integration, future updates, and long-term use, we build data solutions that continue to deliver value well beyond the initial project.
Designing the Process
We follow a thoughtful approach to designing a solution that fits your needs
NEEDS
ASSESSMENT
We start by understanding your data, models, and business goals. Our in-house data professionals work with your team to identify:
- • The real purpose of the dataset
- • The best annotation methods for model performance
- • Gaps in data quality or structure that may impact outcomes
CUSTOM
ANNOTATION
DESIGN
We don't apply a one-size-fits-all approach. We design:
- • Annotation workflows tailored to your use case
- • Quality control protocols aligned with your accuracy targets
- • Scalable pipelines that support growth and evolving needs
FUTURE-PROOFED
DATA
ARCHITECTURE
We think ahead. Our approach ensures your labelled data:
- • Integrates seamlessly into ML/AI pipelines
- • Supports model retraining and versioning
- • Reduces the need for costly rework later
HUMAN IN THE
LOOP,
ALWAYS
Our workforce is trained beyond basic labelling – they are:
- • Skilled in domain-specific annotation
- • Supported by data experts to ensure precision
- • Continuously improving through feedback loops
THE RESULTS
Not just "labelled data", but a strategic data asset that powers long-term machine learning and operational success
Needs Assessment
We don't just accept data — we interrogate it.
Every engagement starts with a deep-dive assessment led by our cross-functional team of data scientists and annotation architects.
WHAT WE DO:
- Use Case Discovery: Map out model objectives and business value to guide data strategy
- Data Audit & Profiling: Identify gaps, anomalies, and schema inconsistencies
- Label Strategy Definition: Co-create a label set, ontology, and annotation policy tailored to your domain
HOW WE DO IT:
Our Swiss team leads structured assessments and defines annotation KPIs and data readiness benchmarks
Our Namibian team conducts dataset exploration, metadata mapping, and prepares datasets for pipeline ingestion
Custom Annotation Design
Once the goals are clear, we design an annotation workflow that reflects both the complexity of the task and the structure of your data.
WHAT WE DO:
- Workflow Design: Design end-to-end annotation pipelines (task creation, labelling, validation, QA, and approval) using flexible platforms
- Quality Controls: Implement multi-layered quality checks including consensus scoring, gold standard tasks and blind reviews
- Scalability: Configure the pipeline to integrate with your infrastructure for task dispatching and cloud storage sync
HOW WE DO IT:
- Our Swiss team sets up control and measurement systems using annotation benchmarks
- Our Namibian team operationalizes the workflow, ensuring flexibility for daily QA feedback loops
Future-Proofed Data Architecture
Data labelling is an investment. We ensure it continues to yield returns by embedding reusability, traceability, and extensibility into the output.
WHAT WE DO:
- Standardized Output Formats: Deliver in ML-ready formats like COCO, JSONL, TFRecord, or Pascal VOC
- Version Control: Track dataset iterations and changes using tools like DVC or Git-based systems
- Retraining Readiness: Design feedback loops for future annotation cycles and model fine-tuning
HOW WE DO IT:
- Our Swiss team defines output architecture, schema governance, and compliance-ready data documentation
- Our Namibian team ensures versioning, traceability, and consistent data delivery through managed pipelines
Human in the Loop
We believe annotation is not a mechanical task — it's a cognitive skill.
Our Human-in-the-Loop (HITL) model enables contextual, high-quality annotations.
WHAT WE DO:
- Domain-Specific Training: Annotators are trained in the nuances of the industry (legal, medical, retail, etc.)
- Human-AI Collaboration: Combine pre-annotations, model suggestions, and manual corrections
- Feedback-Driven Improvement: Use performance reviews and disagreement analysis to continuously raise annotation quality
HOW WE DO IT:
- Our Swiss team sets review policies and manages continuous learning from model outcomes
- Our Namibian team executes annotation with quality-first discipline, escalating ambiguities and refining work
The Results
Not just "labelled data", but a strategic data asset that powers long-term machine learning and operational success.
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