Services
Data annotation and labelling
The quality of a model is bounded by the quality of the data it learns from, and one wrong guess repeated across a dataset corrupts the training signal for everything downstream. Corpshore Canada delivers data annotation and labelling across text, image, video, audio and multimodal data, including Canadian French, from a distributed Canadian workforce on Canadian contracts. The practice sits inside an AI capability ranked fifth of fifty AI outsourcing companies worldwide by Outsource Accelerator. What separates it is that quality is managed as a gate with measured inter-annotator agreement, not a report produced after delivery, and that annotators are engaged under Canadian labour standards rather than the informal arrangements that make so much of this market fragile. Where the work needs domain judgment, credentialed reviewers in STEM, medicine and law do it rather than general annotators.
How the service works
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Guideline design and calibration
We build or refine the annotation guideline with you and calibrate a pilot batch before volume begins, because most annotation error is a guideline problem before it is an annotator problem. Where the guideline produces inconsistent outcomes we flag it and propose a revision rather than pushing volume through an unstable specification.
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Workforce matching
Annotators are matched to the work by skill and, for language data, by variety, so Canadian French is produced by Quebec, Acadian, Franco-Ontarian and western francophone speakers rather than treated as regional colour. Specialist tasks are routed to credentialed reviewers with genuine field qualifications.
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Production with layered review
Work is produced in batches with layered review and measured inter-annotator agreement against the programme threshold. A batch that misses the threshold is not shipped, it is reworked, and the pattern behind the miss is fed back into the guideline so the same error does not recur.
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Delivery, audit and iteration
Delivered data carries its provenance, its consent chain and its quality metrics, so you can audit it rather than trust it. As the model trains and reveals where the data is weak, we iterate the guideline and the sampling rather than restarting, because a labelling programme is a loop and not a one-time hand-off.
How we deliver it from Canada
Delivery is from a distributed Canadian workforce nationwide, coordinated by a quality and programme team in Ontario, Quebec and Alberta. Canadian French is a first-class capability matched across varieties, and more than thirty-five further languages are available through the group network. Data is processed and stored to your residency requirement, including in-province where a public sector health buyer requires it, and every dataset carries a documented consent and governance chain under Canadian privacy law.
Pilot, defined batch
A small annotation team with a quality lead calibrating the guideline and proving inter-annotator agreement on a defined batch before volume is committed.
Programme, sustained volume
Multiple annotation pods with a dedicated quality layer, a guideline owner and per-batch gating, sized to your throughput with output pricing and measured agreement.
Practice, multi-modality
A standing annotation practice across text, image, video, audio and multimodal work, with specialist reviewers, domain calibration and global data operations capacity behind it where volume justifies the blend.
Compliance and data handling
Annotation work is governed under PIPEDA and, where Quebec residents' data is in scope, Law 25, including its provisions on the collection and use of personal information and, where labelled data feeds an automated decision, the transparency that regime requires. Annotators are engaged under Canadian labour standards and contracts, which is both an ethical and a continuity position, and every dataset carries a documented consent chain. Where data includes sensitive categories, access is restricted and handling follows the relevant group security framework with scope confirmed on request.
Technology
We work in your annotation platform where you have one, and bring proven text, image, video, audio and multimodal tooling where you do not, integrated under your governance rather than locked behind ours. Quality tooling captures inter-annotator agreement, adjudication and provenance as first-class outputs. Specific platform choices are confirmed against the modality and the residency requirement during scoping rather than prescribed here.
How performance is measured
- Inter-annotator agreement against the programme threshold
- Batch acceptance rate and rework rate
- Throughput against the committed schedule
- Guideline stability, measured by revision frequency and its cause
- Provenance and consent completeness on delivered data
Reporting delivers quality against the gate, throughput against schedule and the guideline changes made and why, so you can see the programme improving rather than take it on trust. Where agreement drops we surface it with the batch that caused it and the corrective action, rather than smoothing it into an average that hides the problem. Governance runs on an agreed cadence with the analysis prepared by us.
Where this applies
Technology and SaaS
Training and evaluation data for model developers who need Canadian French depth, auditable provenance and a jurisdiction with enforceable labour and data contracts.
Healthcare and life sciences
Clinical and biomedical annotation by credentialed reviewers, on data that stays in Canada, with conservative handling of sensitive categories and a documented consent chain.
Insurance
Document and claims data labelling that feeds triage and automation, produced to a gated standard so the downstream decision rests on data you can audit.
Pricing and engagement models
Annotation is priced per unit of output with quality gates, as a dedicated annotation programme, or as a calibration pilot that proves the guideline and the agreement before you commit volume. Output pricing suits sustained volume where the specification is stable, a dedicated programme suits evolving guidelines and mixed modalities, and the pilot suits a buyer who wants to see measured agreement before scaling.
Frequently asked questions
How is annotation quality actually measured?
Quality is a gate, not a report. Every batch passes layered review with measured inter-annotator agreement against a threshold agreed with you, and a batch that misses it is reworked rather than shipped. The pattern behind a miss is fed back into the guideline, so the programme gets more stable rather than repeating the same error at scale.
Can you produce Canadian French annotation at volume?
Yes, and it is a core reason buyers come to us. Canadian French data is produced by speakers matched across Quebec, Acadian, Franco-Ontarian and western francophone varieties, because models underperform on Canadian French where that data does not exist in volume. We treat regional variation as signal to capture rather than noise to normalise away.
Who does specialist or domain annotation?
Credentialed reviewers with genuine qualifications in the field, not general annotators. STEM, medical and life sciences, and bilingual legal work are routed to people who can tell a correct label from a plausible wrong one, because a domain error looks confident and passes casual review while quietly corrupting the dataset it sits in.
What happens to our data during annotation?
Data is processed and stored to your residency requirement, including in-province where a public sector health buyer requires it, and every dataset carries a documented consent and governance chain under Canadian privacy law. Access to sensitive categories is restricted, and delivered data carries its provenance and quality metrics so you can audit it rather than trust it.
Why does it matter that annotators are on Canadian contracts?
It is an ethical and a continuity position at once. Annotators engaged under Canadian labour standards and contracts are treated properly and stay longer, which keeps the people who understand your guideline on your guideline. Much of this market runs on informal arrangements that make quality fragile and provenance hard to stand behind, and we do not.
How do you handle a guideline that keeps changing?
We expect it, because a labelling programme is a loop rather than a one-time hand-off. We version the guideline, feed the model's weak spots back into the sampling and the specification, and iterate rather than restart. Where the guideline itself produces inconsistent outcomes we say so and propose a revision instead of pushing unstable volume.
Related services
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