Data Foundation & Human-in-the-Loop AI
Build the data foundation required for reliable AI systems.
AI systems are only as good as the data, examples and feedback loops that support them. We help organizations structure, annotate, validate and improve datasets used for training, evaluation, fine-tuning and quality control, and design the human-in-the-loop workflows that keep AI accurate, auditable and trustworthy over time.
- Profiles
- Annotation specialists · Data quality engineers · Domain experts · ML engineers · Annotation tooling specialists
- Engagement models
- Annotation programs · Golden dataset design · HITL workflow integration · Continuous evaluation
Organizations building AI assistants, computer-vision systems, document AI, RAG platforms or domain-specific datasets requiring expert review.
- ENTRY 001
Our model accuracy has plateaued, is it a data problem or a model problem?
- ENTRY 002
How do we build evaluation datasets that reflect real production conditions?
- ENTRY 003
Where should human review sit in our AI workflow without slowing it down?
- ENTRY 004
How do we make annotation a long-term capability, not a one-off project?
- 01
Annotation strategy, guidelines and tooling setup
- 02
Golden dataset design for evaluation and benchmarking
- 03
Quality assurance and inter-annotator agreement programs
- 04
Human-in-the-loop workflow design for high-stakes outputs
- 05
Continuous evaluation harnesses tied into release pipelines
- 06
Domain-expert review programs for sensitive or regulated content
Clean, structured and usable annotated datasets
Clear labeling rules and quality standards
Evaluation datasets to test model performance
Human-review process for sensitive or high-impact outputs
Better visibility on model errors and improvement priorities
Stronger foundation for reliable AI delivery
High-quality data and feedback loops that improve AI accuracy, reliability and trustworthiness over time.
30 minutes is enough to know whether we're the right fit.
We'll come prepared, ask hard questions, and tell you honestly if you should be talking to someone else instead.