Data Engine
The operating system for custom AI data.
A managed workflow for designing, collecting, curating, annotating, validating, evaluating, and continuously improving production AI datasets.
The core loop
Scope → Design → Collect → Curate → Annotate → Validate → Evaluate → Iterate.
Why it compounds
Not a one-off dataset — a managed engine across the model lifecycle.
Every evaluation surfaces failure modes that become the next data spec. The loop tightens with each cycle, so model quality and data quality improve together.
Phase 01
Strategy & Design
Map model goals to a concrete, verifiable data specification before a single label is produced.
- Model goal mapping
- Data gap analysis
- Risk assessment
- Success metrics
- Acceptance criteria
Phase 02
Data Collection
Source expert, multimodal, and sensor data — or enrich data you already own.
- Expert data
- Multimodal data
- Sensor data
- Synthetic + human-validated data
- Global contributor sourcing
- Customer-owned data enrichment
Phase 03
Data Curation
Shape the distribution: dedupe, balance, mine edge cases, and filter for safety and quality.
- Deduplication
- Distribution balancing
- Edge-case mining
- Data quality filtering
- Metadata normalization
- Safety filtering
Phase 04
Annotation & Validation
Human-in-the-loop production with multi-layer QA and disagreement resolution.
- Taxonomy design
- Rubric design
- Guideline creation
- Human-in-the-loop workflows
- Multi-layer QA
- Disagreement resolution
Phase 05
Model Evaluation
Measure behavior, surface failure modes, and turn findings into the next data spec.
- Model behavior assessment
- Failure mode analysis
- Benchmarking
- Human preference evaluation
- Red teaming
- Feedback into next data cycle
Phase 06
Continuous Iteration
Secure, versioned delivery with documented QA and a plan for the next cycle.
- Secure delivery
- Versioned datasets
- QA reports
- Client review cycles
- Delivery documentation
- Iteration plan