Requirements before volume
We begin with the business decision the AI must support, the failure modes that matter and the evidence required to accept the result. This prevents expensive production of data that is plentiful but not useful.
The delivery model
A controlled path from AI need to trusted evidence.
Each engagement blends domain expertise, AI-assisted creation, human validation, quality metrics and governance controls.
- 01 / DISCOVERDefine use case, model weakness, risk, domain and acceptance criteria.
- 02 / AUTHORIZEVerify source, rights, consent, classification and permitted purpose.
- 03 / CREATEGenerate, curate or annotate representative training artifacts.
- 04 / VALIDATEApply rubrics, dual review, adjudication and SME approval.
- 05 / EVALUATETest model or agent behavior against quality and control thresholds.
- 06 / PROVEDeliver data cards, lineage, results and audit-ready evidence.
AI-assisted, expert-controlled
Generative models can accelerate scenario and task creation. Domain experts define rubrics, review representative and high-risk examples, adjudicate disagreements and approve the gold-standard dataset.
Risk-based quality assurance
- Qualification and calibration for contributors
- Explicit task- and domain-specific scoring rubrics
- Inter-annotator agreement and disagreement review
- Automated checks for format, duplication, leakage and inconsistency
- Enhanced review for sensitive or high-impact examples
- Versioned acceptance criteria and sign-off
Evidence travels with the data
AegisDatastra can connect the dataset to its sources, policies, contributors, validations, model versions and evaluations. That gives engineering teams usable artifacts while giving risk, compliance and audit teams a defensible record.
Frequently asked questions
Do we need to share sensitive data?
No. An engagement can use your controlled environment, de-identified artifacts, synthetic data, public standards or expert-created scenarios depending on the use case.
Can you work with our internal experts?
Yes. We can operationalize the workflow while your SMEs define rules, review edge cases and approve the final benchmark.
Do you train the model?
We can deliver training-ready data and collaborate with your model engineering team. The core offering is data creation, validation, evaluation and governance rather than foundation-model development.
How do we start?
Choose one model, agent, domain or dataset and define a measurable pilot. A focused engagement can validate quality, governance and business value before scaling.