Rivac LabsData Labeling & Annotation for ML Training
No machine learning model is better than the training data it's built on, and most teams either burn far too many hours labeling data by hand or trust fully automated labeling that quietly bakes errors into the model from day one. This service builds a hybrid labeling pipeline that uses AI to handle the volume — pre-labeling, flagging low-confidence cases — while your subject-matter experts or our trained annotators review and correct the cases that actually need a human judgment call, producing training data that's both fast to generate and genuinely high quality. We set up the labeling guidelines, inter-annotator agreement checks, and quality sampling so you can trust the resulting dataset rather than hoping it's good enough. It solves the real chokepoint in most ML projects: not the model architecture, but the unglamorous work of getting enough correctly labeled data to train it on.
How We’d Approach This
A clear, staged plan — not a black box
- 1
Define labeling guidelines and taxonomy with your team, including the edge cases that cause the most disagreement
- 2
Pilot the hybrid AI-plus-human labeling workflow on a sample batch, measuring inter-annotator agreement and AI pre-label accuracy
- 3
Review disagreement cases and guideline gaps with your team, refining instructions before scaling to full volume
- 4
Run the labeling pipeline at production volume with ongoing quality sampling and a feedback loop to catch guideline drift
What You Get
Deliverables from this engagement
- Labeled training dataset at agreed volume and quality bar
- Documented labeling guidelines and taxonomy
- Inter-annotator agreement and quality-sampling reports
- Repeatable labeling workflow for ongoing or future training runs
Six Ways We Could Architect This
Different engagement, different build — pick the shape that fits
There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.
Ready to get started?
Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start — scoped small enough to prove itself before you commit to anything bigger.
Talk to us about Data Labeling & Annotation for ML TrainingMost engagements like this start as a $500–$2,500 pilot — see full pricing.