01
Data preparation
Collect, clean, deduplicate, normalize, and organize information around client-defined schemas and acceptance criteria.
Collection and cleanup Taxonomy preparation Duplicate and exception review
Capabilities
The judgment layer
behind reliable AI.
ADCO organizes human attention around the parts of the AI lifecycle that require interpretation, consistency, verification, and accountable review.
Scope a workflow
Across the lifecycle
Human control at the
right decision points.
The work changes across a program. The operating principle does not: define the standard, train against it, review the work, and return what is learned.
AI lifecycleHuman control points
02
Data annotation
Train reviewers on taxonomies, examples, edge cases, and escalation rules before annotation moves into managed production.
Labeling and classification Tagging and categorization Edge-case escalation
03
Model evaluation
Compare, rank, and score model outputs for relevance, instruction following, accuracy, style, and other client-defined criteria.
Response comparison Rubric-based scoring Error categorization
04
Human verification
Verify outputs, review sources, resolve discrepancies, and document exceptions before work reaches its next decision point.
Fact and source review Discrepancy resolution Final quality checks
05
AI monitoring
Maintain a structured review layer after deployment to surface anomalies, drift, repeated errors, and new edge cases.
Output sampling Failure-pattern logging Escalation and reporting
06
Human-in-the-loop
Design review, approval, correction, and escalation steps around high-context or high-consequence decisions.
Exception handling Approval workflows Review and correction
Good workflow fit
Defined work.
Visible decisions.
ADCO is strongest when the work can be trained, reviewed, measured, and improved through a clear operating process.
01
Recurring volume
The work appears often enough for a trained team and a stable process to create value.
02
Judgment criteria
Success can be translated into examples, a rubric, acceptance rules, or an escalation path.
03
Reviewable output
Completed work can be sampled, checked, corrected, and reported without relying on trust alone.
04
Feedback loop
Corrections and new edge cases can improve training, instructions, and future production.
Start with one workflow
Have a workflow
that needs a human layer?
Share the non-confidential outline. We will help define what a controlled pilot should prove.
Discuss your workflow