// hybrid_capability_centers
Your model changes. Your data strategy should too.
Datafy Lab provides an ongoing data improvement loop for AI teams deploying into real-world environments — driven by your model’s actual failures, not a fixed dataset.
// closed_loop
A pipeline that compounds.
01
Diagnose
Model & dataset failure review
02
Design
Spec the data needed next
03
Capture
Field, egocentric, synthetic
04
Annotate
Human-in-the-loop + QA
05
Certify
Quality, rights, balance
06
Improve
Loop on new failures
06 → 01 the loop never closes — it compounds
// monthly_workflow
What happens every cycle.
[01]
Review model failures
We analyze deployment performance and where the model is breaking now.
[02]
Identify new edge cases
We translate failures into a prioritized edge-case list.
[03]
Create or source targeted data
Field capture, egocentric video, controlled scenarios, or synthetic.
[04]
Annotate & QA
Human-in-the-loop labeling with consistency checks and expert review.
[05]
Filter & certify
Clean, validate, and issue the Model-Ready Data Certificate.
[06]
Deliver monthly data report
What changed, what improved, and what to collect next.
[07]
Recommend next training priorities
A clear roadmap feeding directly into your next training run.
// faq
Common questions
A foundry that continuously creates, collects, annotates, filters, and certifies model-ready datasets — improving as your model and deployment evolve.
It's a recurring loop driven by your model's real failures: review, identify edge cases, capture, annotate, certify, report, and prioritize the next training run.
Yes. The loop starts from your current model and data, then continuously fills the gaps it surfaces.
Each cycle's output ships with a Model-Ready Data Certificate plus a monthly data report.
Yes — the foundry is often paired with a capability center pod that runs the loop as an extension of your team.
Begin with a Data Failure Audit, then move into a monthly Continuous Data Foundry cadence.