Before there's a model, there's a dataset. Before there's a dataset, someone has to sit with the raw material and label it correctly, consistently, thousands of times over. That work is where Synovion began.
Synovion started as a small annotation team frustrated by how disconnected data labeling was from the models and products it fed into. Annotation guidelines were written by one company, models trained by another, and the product shipped by a third — and every handoff lost something: context, edge cases, the reasons a label was drawn a certain way.
So we built the whole chain ourselves. Today Synovion runs data annotation, AI/ML development, and web and app development as one team, so the person labeling your data and the person shipping your app are one call away from each other — sometimes the same person.
We'd rather relabel a batch than let a bad guideline propagate through a model.
Data, model, and product stay with one accountable team — not three contracts.
Automation speeds up labeling and code. It doesn't replace review.
Guidelines, metrics, and progress are visible to you throughout, not just at delivery.
A working group across annotation ops, ML engineering, and product development — not a layer of account managers between you and the work.
Owns labeling guidelines, QA, and annotator training across every dataset.
Takes labeled data through training, evaluation, and deployment.
Builds the dashboards, sites, and APIs models and datasets live behind.
Ships the iOS, Android, and cross-platform builds your users hold.