Synovion is the team that owns the whole chain — from messy raw data to a labeled dataset, a trained model, and the web or app product it ships inside. No handoffs between vendors, no lost context.
Images, video, text, audio, logs — collected, cleaned, and structured for labeling.
Bounding boxes, segmentation, transcription, NLP tagging — labeled by trained annotators, checked for agreement.
Models trained, fine-tuned, evaluated, and deployed on the dataset your team just watched get built.
The model shipped inside a real product — a web dashboard, a mobile app, an API your users touch.
Each service works alone if you need it to — but they were built to hand off to each other without losing context.
Image, video, text, and audio labeling with QA workflows built around inter-annotator agreement, not guesswork.
Model architecture, fine-tuning, evaluation, and MLOps — built on data pipelines we understand end to end.
Product sites, dashboards, and internal tools that make your models and datasets usable by real teams.
Native and cross-platform apps that put your AI features in front of users on iOS and Android.
Most projects die in the gap between the annotation vendor, the ML team, and the dev shop. We run all three under one roof, so context never gets lost in a handover doc.
Auto-labeling tools speed us up, but every dataset gets reviewed by trained annotators with measured agreement scores before it touches a model.
We design for production from day one — versioned datasets, monitored models, and products with real error states, not prototypes that stall at the demo stage.
You see labeling guidelines, model metrics, and sprint boards as we go — not a black box that resurfaces at the deadline.
The pipeline stays the same — what changes is the guideline, the edge cases, and what "correct" means for your domain.
Shelf imagery, product catalogs, and demand data — labeled for detection and recommendation models.
Document verification, transaction data, and fraud signals, handled under strict data-handling requirements.
De-identified clinical text and imaging, annotated by workflows built around review and auditability.
Fleet, routing, and warehouse data feeding dashboards and predictive maintenance models.
Field and crop imagery labeled for detection models built to run offline, on-device.
Text and response data for RLHF, content moderation, and language model evaluation.
We map your data, goals, and constraints into a concrete labeling and build plan.
Annotators work your dataset against a written guideline, with QA sampling throughout.
Models are built, evaluated against holdout data, and tuned against real failure cases.
The model goes live inside a web or mobile product, with monitoring from day one.
We keep tuning as new data and edge cases come in — not a one-time delivery.
We'd been through two annotation vendors before Synovion. The difference was the agreement scores — we could finally see the label quality instead of guessing at it.
They shipped the model and the app it lives in. That alone cut about six weeks off our timeline versus running two separate contracts.
The labeling guideline they wrote for us is still the reference doc our internal team uses today. That's the part most vendors never leave behind.
No — each service stands on its own. Plenty of clients bring us in for annotation only, or for the app build on top of a model they already trained. The full pipeline is an option, not a requirement.
It's a measure of how consistently different annotators label the same data according to the guideline. We sample and cross-check batches and report the score, so you can see label quality directly instead of taking it on faith.
Annotation work is typically priced per labeled unit or as a monthly capacity block; model and product work is scoped per project. Tell us what you're working with and we'll send a real estimate, not a range pulled from a rate card.
Yes — we sign NDAs as standard, and for regulated data (healthcare, financial) we scope handling and access controls into the project plan up front rather than after the fact.
A labeling-only engagement can start within a week. A full pipeline project — annotation through a shipped product — typically runs 8 to 16 weeks depending on data volume and product scope.
We'll tell you honestly whether it's ready for a model, and what it'll take to get there.