Hire us for a single stage or the whole chain — raw data in, working product out. Jump to a service:
This is the work most AI vendors outsource and stop thinking about. We don't. Every dataset gets a written labeling guideline, a trained annotator team, and a measured agreement score before it's considered done — because a model is only as good as the labels underneath it.
Bounding boxes, polygons, pixel-level segmentation, keypoints, and tracking across video frames.
Entity extraction, intent and sentiment labeling, document classification, RLHF response ranking.
Transcription, speaker diarization, intent tagging, and quality scoring for voice datasets.
Every batch sampled and cross-checked, with inter-annotator agreement tracked and reported.
Model work grounded in datasets we built ourselves — or your existing ones, audited first. From fine-tuning an existing foundation model to training computer vision systems from scratch, then deploying and monitoring them in production.
Custom computer vision, NLP, and predictive models, built and evaluated against holdout data.
Fine-tuning, prompt systems, and retrieval-augmented pipelines on your own knowledge base.
Training pipelines, versioning, and CI for models — reproducible runs, not one-off notebooks.
Drift detection and performance tracking once a model is live, with retraining triggers.
The interface layer that makes a model or dataset usable by an actual team — marketing sites for launch, internal dashboards for your annotators and analysts, and full product front ends backed by real APIs.
Fast, distinctive sites built to convert, not templated builder output.
The panels your ops and data teams actually live in — labeling review queues, model metrics, admin tools.
Front end, API, and database work as one build, not three separate contracts.
Ongoing support once you're live — performance, uptime, and new feature work.
Native and cross-platform apps for putting AI features — on-device inference, camera-based models, chat interfaces — directly in front of your users, wherever they are.
iOS and Android builds when performance and platform-specific features matter most.
React Native and Flutter builds for shipping to both platforms from one codebase.
Camera, voice, and sensor-driven features running inference directly on the device.
The services layer behind the app — auth, sync, and the endpoints your model lives behind.