Multimodal AI data production needs one traceable record linking each licensed asset to its task, instructions, model prelabel, reviewer decision and accepted output. BergFlow is the reference platform in this design; specialist review resolves ambiguity and sensitive content before an accepted label package leaves the workflow.
Composite reference architecture. A BergFlow reference design; it does not identify a client or assert a measured programme outcome.
What is multimodal AI data production?
Multimodal AI data production creates governed training or evaluation records across text, image, audio and video. The accepted unit is not just a label. It is the source asset, its permitted use, the instruction and model versions, reviewer decisions, corrections and final export state.
The operating problem
Multimodal programmes fail at the seams: assets lose provenance, instructions drift by modality, reviewers sample inconsistently and accepted outputs cannot be traced back to the source version.
How BergLabs runs the operating loop
In our in-house reference workflow, BergFlow keeps the asset, task, guideline, prelabel and accepted decision in one versioned record. BergAuto moves repeatable work through validation, review and export gates.
The workflow connects specialist quality to execution: Quinn supports specialist vetting, Rosterr manages the review path and produces project-level QA outputs from a shared control-room view, while Charterr provides governed delivery capacity. These connections describe the BergLabs operating pattern, not a claim that every client uses the same configuration.
How the workflow works
Text, images, audio and video need modality-specific instructions within a common versioning scheme. Track the source asset and transformations, such as audio segments or video frames, so a label can be reconstructed against the correct material. Prelabels move through BergAuto with consensus scores to route work, while protected gold examples and independent samples test quality.
Where review and controls matter
Missing usage rights, corrupted media and personal information outside the approved scope should stop the affected item. Low agreement returns to a specialist with the competing labels and guideline version. Corrections update the labelled record and any affected evaluation sample. Production throughput must be reported alongside accepted yield and rework, because a large export can still contain unusable labels.
Risk reference: NIST AI RMF.
What to measure
Use a fixed cohort and measurement window. Count numerator events only within the denominator's eligible cohort; sampled measures use the same audit sample for both terms. Record the source, exclusions and owner, and keep missing data visible. These definitions are proposed measurement contracts, not observed results.
| Measure | Definition and unit |
|---|---|
| Gold accuracy | Correct labels on protected gold items divided by scored gold items; percent, by class and modality. |
| Inter-annotator agreement | Agreement measured with a declared statistic suitable for the task, such as Cohen's kappa or Krippendorff's alpha; report statistic, scale and sample. |
| Accepted yield | Items accepted under final quality checks divided by eligible input items; percent. |
| Rework | Items returned for correction divided by reviewed items; percent, with reasons. |
| Turnaround time | Time from eligible task release to accepted output; median and p90 hours. |
| Cost per accepted unit | Attributable production and quality cost divided by accepted units of the same task type; currency per unit. |
Multimodal AI data production FAQ
Why is provenance important for multimodal data?
Images, clips and transcripts are often transformed before labelling. Provenance lets a reviewer reconstruct which licensed source, segment, instruction and model version produced an accepted label.
When should a human review a model prelabel?
Review is required when the task is sensitive, confidence is not calibrated for the class, instructions conflict, agreement is low, or the cost of a missed error exceeds the benefit of automatic acceptance.
What is the right unit of quality?
Use accepted units that pass the declared checks, not raw labels produced. Report accuracy, agreement, rework, turnaround and cost against the same eligible cohort.
A useful first pilot
Use a small mixed-modality batch with known ambiguous items and one guideline change. Reconstruct accepted outputs from their source versions and check that corrected labels reach the final export.
Bring the workflow, its source records and the decision that needs an owner to a BergLabs pilot discussion.
