AI delivery
AI training data, labelled to a standard you can train on
Annotation, RLHF and model evaluation from Canada, with quality managed before the data reaches your model rather than reported after it.
The decision a training-data buyer is actually making
If you are buying AI training data you are not buying hours, you are buying a label you can trust enough to train on. The hard part is never starting a labelling project, it is keeping quality stable as the guidelines shift, the edge cases pile up and the volume climbs. A vendor that quotes a low per-unit rate and reports quality after the fact is selling you a number you cannot use, because by the time the quality report lands the model has already learned the errors. Corpshore Canada treats quality as a gate the data passes through before you ever see it, not a summary produced once the batch ships.
The market around this work is growing fast and changing shape. The AI data labelling market was estimated at about USD 2.32 billion in 2026 and is projected to reach roughly USD 6.53 billion by 2031, a compound annual growth rate near 23 percent (Mordor Intelligence, AI Data Labeling Market, 2026). What is moving underneath that number matters more than the number itself: foundation models now handle routine pre-labelling, which pushes human effort toward edge cases, subjective judgment and regulated domains where a wrong label is expensive. The work that still needs people is the work that needs qualified people, and that is the work we staff for.
What we label, and in what modality
Corpshore Canada annotates across every modality a modern model consumes, and the common thread is that each one is run with measured inter-annotator agreement rather than a single pass. The programme covers the full pipeline, from raw collection through labelling to the human preference and evaluation work that aligns a finished model.
Text and natural language
Classification, named-entity recognition, intent and sentiment, semantic parsing, summarisation review and instruction data, in English and Canadian French to a native standard.
Image and video
Bounding boxes, polygons, segmentation masks, keypoints and frame-by-frame video tracking for computer vision, with class schemas agreed before the first frame is touched.
Audio and speech
Transcription, speaker labelling, diarisation and phonetic tagging, including Canadian French speech where accent and idiom change the transcript.
RLHF and preference data
Reinforcement learning from human feedback, ranked comparisons and red-team prompts, with layered adjudication and credentialed evaluators in STEM, medicine and law.
How quality is actually controlled
The difference between usable training data and expensive noise is the quality system around it. We start every engagement by building the guideline with you and labelling a calibration set against it, so disagreement surfaces before production rather than in your model. From there, multiple annotators see overlapping items, inter-annotator agreement is tracked as a live metric, and items below threshold route to an adjudication layer staffed by reviewers qualified in the domain. You get the agreement scores, the gold-set performance and the error taxonomy as the work runs, not a tidied summary afterward.
Because leading labs now spend on the order of a billion dollars a year on human data (herohunt.ai, The Ultimate AI Data Labeling Industry Overview, 2026), the economics have moved decisively toward expertise and defensibility over raw throughput. A cheap label that has to be redone, or worse that quietly poisons a fine-tune, costs far more than it saved. Our model is built for the class of work where that is true: regulated domains, subjective judgment and the long tail of edge cases that pre-labelling cannot close.
Why Canada is the right place for sensitive training data
Training data is often some of the most sensitive data an organisation holds, because it frequently contains the real customer records, documents and conversations the model is meant to learn from. Handled by Canadian delivery, that data stays in North America under the federal Personal Information Protection and Electronic Documents Act, PIPEDA, and Quebec engagements apply the stricter Quebec Law 25. The workforce is Canadian and on Canadian contracts, the governance sits in Toronto and the cross-border position is documented before go-live wherever any part of the work is supported from the global network.
Canada also holds a genuine edge on one axis the rest of the market cannot fake: native Canadian French. Models consistently underperform in Canadian French because the training data does not exist in volume, and a Canadian bilingual workforce is one of the few places that gap can be closed at a native standard rather than translated into. The AI practice behind this work is ranked fifth of fifty AI outsourcing companies worldwide by Outsource Accelerator, and it is delivered in partnership with Corpshore AI, the group's dedicated AI division.
Backed by Corpshore AI
This work is delivered with Corpshore AI, the group's dedicated artificial-intelligence division, which is where the model-facing expertise and the evaluation methodology come from. Canadian delivery gives you the workforce, the data residency and the French capability, and the AI division gives you the people who understand what a model does with the labels once it has them.
Visit Corpshore AIFrequently asked questions
Is Corpshore a data annotation company in Canada?
Yes. Corpshore Canada delivers data annotation and AI training data from a distributed Canadian workforce on Canadian contracts, covering text, image, video, audio and multimodal data plus RLHF. The AI practice is ranked fifth of fifty AI outsourcing companies worldwide by Outsource Accelerator, and data is handled under PIPEDA with Quebec Law 25 on Quebec work.
What types of data can you annotate?
We annotate across every common modality: text and natural language, image and video for computer vision, audio and speech, and multimodal data. We also run RLHF, ranked preference data and model evaluation. Each modality is managed with measured inter-annotator agreement and an adjudication layer, rather than a single unchecked pass.
How do you measure annotation quality?
Quality is a gate, not a report. We build the guideline with you, label a calibration set, then run production with overlapping annotators so inter-annotator agreement is tracked live. Items below threshold route to domain-qualified adjudicators. You see agreement scores, gold-set performance and the error taxonomy as the work runs, not a summary after delivery.
Can you provide RLHF and model alignment data?
Yes. We run reinforcement learning from human feedback, ranked comparisons and red-team prompts with layered adjudication. Reviewers include credentialed experts in STEM, medicine and law, so model reasoning is judged by people qualified to tell a correct answer from a plausible wrong one, which is where generic annotation pools fall short.
Do you offer Canadian French training data?
Yes, and it is a real differentiator. Models underperform in Canadian French because the data does not exist in volume. Our bilingual Canadian workforce builds it at a native standard, matched by language variety rather than translated from English, across text, speech and preference data. This is hard for offshore providers to match honestly.
Where does our training data physically sit?
Data handled by Canadian delivery stays in North America under PIPEDA, with Quebec Law 25 applied to Quebec engagements. The workforce is on Canadian contracts and governance sits in Toronto. Where any part of a programme is supported from the global network, the cross-border transfer position is documented transparently before go-live.
How quickly can an annotation team be running?
Teams of one to ten are typically live within 5 to 15 business days of signature, and larger programmes of 20 to 100 within 3 to 6 weeks. The timeline depends on guideline complexity, systems access and the language mix, and we commit to a dated plan during scoping rather than a general promise.
Talk to us about your training data
Tell us the modality, the guideline and the volume, and we will show you how quality is gated before anything reaches your model. Bring a sample and we will calibrate against it.