Services
RLHF and model alignment
As models get stronger the useful signal moves from whether an answer is fluent to whether it is correct, and that is a judgment a general annotator often cannot make. Corpshore Canada runs reinforcement learning from human feedback and preference data operations with layered adjudication and specialist domain evaluators, inside an AI capability ranked fifth of fifty AI outsourcing companies worldwide by Outsource Accelerator. 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 rather than by whoever is available. Preference data, red teaming and safety evaluation are produced by a Canadian workforce on Canadian contracts, with quality managed as a gate and inter-annotator agreement measured on every batch. Alignment work carries the same consent and governance chain as the rest of the pillar, because how the data was collected is part of whether the model can be trusted.
How the service works
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Rubric design and calibration
Alignment starts with a rubric, and a vague rubric produces noisy preference data no amount of volume can fix. We build the scoring rubric with you, calibrate it against a pilot set and align the evaluators before volume, because inter-rater agreement on a clear rubric is the foundation everything downstream stands on.
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Evaluator matching and credentialing
Evaluators are matched to the domain, so STEM, medical and bilingual legal reasoning is judged by people with genuine field credentials rather than general reviewers. For Canadian French and multilingual preference work, evaluators are matched by variety so the model is aligned to how the language is actually used.
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Preference collection and adjudication
Preference comparisons, rankings and rewrites are produced with layered adjudication, and disagreement is resolved by escalation to a senior reviewer rather than averaged away. Measured inter-annotator agreement gates each batch, and a batch below threshold is reworked rather than shipped into a reward model.
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Red teaming, safety and iteration
Where the use case requires it we run adversarial red teaming and bias and safety evaluation, and we feed the failures back into the rubric and the training signal. Alignment is a loop against a moving target, so the programme is built to iterate as the model changes rather than to deliver a single dataset and stop.
How we deliver it from Canada
Delivery is from a distributed Canadian workforce of evaluators, coordinated by a quality and programme team in Ontario, Quebec and Alberta, with credentialed domain reviewers matched to the work. Canadian French and multilingual preference data is matched by variety, and more than thirty-five further languages are available through the group network. Data is processed to your residency requirement, and every preference dataset carries a documented consent and governance chain under Canadian privacy law.
Calibration, defined set
A small evaluator team and a quality lead building and proving the rubric, aligning raters and reporting inter-rater agreement before preference volume is committed.
Programme, sustained preference volume
Multiple evaluator pods with layered adjudication, a rubric owner and per-batch gating, including credentialed domain reviewers where the reasoning requires them.
Practice, alignment and safety
A standing practice spanning preference data, red teaming and safety evaluation, with senior adjudication, specialist reviewers and global capacity behind it where volume justifies the blend.
Compliance and data handling
Alignment work is governed under PIPEDA and, where Quebec residents' data is in scope, Law 25, including its automated-decision provisions where preference data shapes a system that will make or support decisions about people. Evaluators are engaged under Canadian labour standards, and preference datasets carry a documented consent chain. Red teaming and safety work that touches sensitive content is run with wellbeing safeguards for the evaluators built into the work rather than added afterwards, and access is restricted with handling under the relevant group security framework.
Technology
We work in your preference collection and evaluation tooling where you have it, and bring proven ranking, comparison and adjudication tooling where you do not, integrated under your governance. Quality tooling captures inter-rater agreement, adjudication trails and provenance as first-class outputs. Specific reward-modelling and evaluation platforms are chosen with you against the use case rather than prescribed here, and evaluation runs on your metrics.
How performance is measured
- Inter-rater agreement against the rubric threshold
- Adjudication and escalation rate on contested comparisons
- Preference batch acceptance and rework rate
- Domain evaluation coverage by credentialed reviewers
- Measured model behaviour change on your safety and quality evaluation set
Reporting covers agreement against the rubric, the adjudication trail on contested items and, where the work feeds training, the measured behaviour change on your evaluation set rather than a vendor benchmark. Where the rubric produces persistent disagreement we surface it as a rubric problem and propose a revision rather than shipping noisy preference data. Governance runs on an agreed cadence with the analysis prepared by us.
Where this applies
Technology and SaaS
Preference data, reward-model evaluation and red teaming for model developers who need credentialed reasoning judgment and auditable, consent-backed data.
Healthcare and life sciences
Clinical reasoning evaluation by credentialed reviewers with conservative safety thresholds, on data that stays in Canada, where a plausible wrong answer carries real risk.
Banking and financial services
Alignment and safety evaluation for models used in regulated processes, with an adjudication trail and human judgment a risk committee can review.
Pricing and engagement models
Alignment work is priced per unit of preference data with quality gates, as a dedicated evaluation programme, or as a calibration engagement that proves the rubric and inter-rater agreement before volume. Output pricing suits stable preference collection, a dedicated programme suits evolving rubrics and red teaming, and calibration suits a buyer who wants measured agreement on a clear rubric before committing to scale.
Frequently asked questions
Why do you use credentialed evaluators for alignment?
Because the useful signal in a strong model is whether the reasoning is correct, and that is a judgment a general annotator often cannot make. STEM, medical and bilingual legal reasoning is judged by people with genuine field credentials, since a plausible wrong answer looks confident, passes casual review and teaches the reward model exactly the wrong thing.
How do you keep preference data consistent?
Consistency starts with the rubric, not the volume. We build and calibrate the rubric, align raters against a pilot set and gate every batch on measured inter-rater agreement. Contested comparisons are escalated to a senior reviewer and adjudicated rather than averaged away, and persistent disagreement is treated as a rubric problem to fix, not noise to absorb.
Can you run red teaming and safety evaluation?
Yes, where the use case requires it. We run adversarial red teaming and bias and safety evaluation, feed the failures back into the rubric and the training signal, and report behaviour change on your evaluation set. Evaluators working with sensitive content have wellbeing safeguards built into the work rather than added afterwards, because sustained exposure is a real risk.
Does alignment work handle Canadian French and other languages?
Yes. Canadian French and multilingual preference data is produced by evaluators matched by variety, across Quebec, Acadian, Franco-Ontarian and western francophone communities, so the model is aligned to how the language is actually used. More than thirty-five further languages are available through the group network with the same rubric discipline and gating.
How is this different from data annotation?
Annotation labels what something is, while alignment judges which of two model outputs is better and why, against a rubric. Alignment leans far more heavily on domain judgment and inter-rater agreement, which is why credentialed evaluators and layered adjudication matter more here, and why a vague rubric does more damage than a vague labelling guideline.
How is alignment data governed?
Under PIPEDA and, where Quebec residents' data is in scope, Law 25, including its automated-decision provisions where the data shapes a system that will make or support decisions about people. Evaluators are on Canadian contracts, every preference dataset carries a documented consent chain, and access to sensitive content is restricted under the relevant group security framework.
Build your Canadian team
Tell us the work, the languages and the coverage you need. You will have a considered response within six hours, or book a discovery call now.
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