Trusted AI data & evaluation

Build better AI with data you can trust, and prove.

AgileDone Tech creates expert-validated training and evaluation data for enterprise AI, then connects every artifact to its source, permitted purpose, reviewer, model version and compliance evidence.

Scope your AI data pilot →See the data-to-evidence model
Expert-created
Domain-specific, not generic crowdsourcing
Human-validated
SME review and measurable QA
Governed by design
Rights, purpose, access and lineage
Evaluation-ready
Model and agent benchmarks
What we deliver

Training signal for high-stakes AI.

We combine domain expertise, AI-assisted data creation, structured quality assurance and AegisDatastra governance, so your team receives usable data and the evidence needed to trust it.

01 / Train

Trusted training data

Expert-generated instruction-response pairs, policy scenarios, preference data and domain-specific datasets for SFT, DPO and RLHF.

Explore training data
02 / Evaluate

Model & agent evaluation

Benchmarks that test factuality, safety, compliance, tool use, delegated authority, human escalation and real-world action.

Explore AI evaluation
03 / Prove

Data and AI assurance

Trace source, rights, purpose, contributor, validation, dataset version, model consumption, evaluation result and evidence.

Explore governance
The trust chain

From source data to production evidence.

A continuous control chain, rather than a disconnected annotation handoff.

  1. 01Source, rights & consent
  2. 02Classification & permitted AI purpose
  3. 03Expert creation or annotation
  4. 04QA & SME validation
  5. 05Dataset-to-model lineage
  6. 06Evaluation & audit evidence
Why AgileDone Tech

More than labels. Enterprise-grade intelligence.

Commodity annotation answers “Was this item labeled?” We help you answer the harder questions enterprise leaders, risk teams and regulators ask.

  • Can this data legally and contractually be used for this AI purpose?
  • Did qualified experts create or validate the training signal?
  • Which dataset version trained or evaluated which model?
  • Did the model or agent stay within policy and delegated authority?
  • Can we reproduce the evidence for audit, risk or compliance?
See regulated-industry use cases

Start with one model, agent or high-value dataset.

Design a focused pilot