Services
AI strategy and implementation
Most AI programmes fail at the same point, which is the gap between a promising demonstration and a system that runs in production without a person watching it. Corpshore Canada works that gap directly, from readiness assessment through use case definition to a system in production, out of a practice ranked fifth of fifty AI outsourcing companies worldwide by Outsource Accelerator. Engagements begin with honest discovery, including the cases where the proposed use will not work, because that is what makes the rest of the recommendation credible. We are based in the same country and often the same time zone as the buyer, under enforceable Canadian contracts, with data governance designed in from the first conversation rather than retrofitted before launch. The output of a strategy engagement is a decision you can defend to a board, not a slide deck of possibilities.
How the service works
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Readiness and honest discovery
We assess the data, the process and the appetite as they actually are, and we name the use cases that will not work before we scope the ones that will. A grounded no is more valuable than an optimistic yes, because a failed AI programme costs both parties more than a declined one.
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Use case definition and business case
We translate an ambition into a defined use case with a data plan, a governance posture and a business case a finance function can review. The success measure is agreed up front, on your metrics, so the programme has a target it can be held to rather than a demonstration it can be excused by.
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Implementation
We build against the defined scope, with human oversight, logging and an override path designed in rather than added at the end. Where the work needs annotation, preference data or evaluation, the group data operations and evaluation capacity sits behind the same programme under one governance framework.
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Production, handover and monitoring
Systems go live with monitoring, drift detection and a documented operating model, and we either run them as a managed service or hand them to your team with the runbook and the training to own them. The measure of success is a system still working in six months, not a launch.
How we deliver it from Canada
Delivery is from Canada, with solution consultants and engineers in Ontario, Quebec and Alberta working in your business hours across Eastern, Central and Mountain time. Canadian French capability is available where the use case touches Quebec or federal bilingual obligations, and data residency is configured to your requirement including Canadian-only hosting where a public sector buyer asks. Group data operations and evaluation capacity supports implementation under one contract where the programme needs it.
Assessment, fixed scope
A solution consultant and specialists delivering a readiness assessment, a defined use case and a go or no-go recommendation with a costed path to production.
Implementation, project team
An engineering and data team under a delivery lead, with an embedded evaluation function and a named governance owner, sized to a defined use case rather than a headcount target.
Programme, multi-use-case
A standing team across several use cases with a programme director, shared governance and platform standards, and global data operations capacity behind it where volume justifies the blend.
Compliance and data handling
Strategy and implementation work is governed under PIPEDA and, where Quebec residents' data is in scope, Law 25, including its provisions on automated decision-making and an individual's right to be informed a decision was made by a system and to request review by a person. Privacy impact assessment support, consent handling and retention are built into the design rather than assessed after launch. We build for the direction of AI regulation as well as its current state, so the governance posture does not need reworking as the rules mature.
Technology
We work in your cloud and your stack, across common model providers, retrieval frameworks, orchestration tooling and MLOps platforms, and we integrate rather than impose a proprietary layer that locks you in. Specific platform choices are made with you against the use case, cost and residency requirement rather than prescribed here, and evaluation runs on your data and your metrics so the result stays portable.
How performance is measured
- Use cases assessed against a documented readiness and viability standard
- Time from use case sign-off to a production-ready system
- Measured business outcome against the agreed success metric
- Human review rate and override rate in the live system
- Model and system evaluation coverage before go-live
Reporting runs on an agreed cadence through the life of the engagement, with a steering review that covers progress against the business case, risk and governance rather than activity. The assessment output is a written recommendation you can put in front of a board, and implementation reporting tracks the agreed success metric on your data rather than a vendor benchmark.
Where this applies
Technology and SaaS
Product teams embedding AI features that need a defensible data and evaluation posture before they ship to their own customers.
Banking and financial services
Use case definition and implementation for regulated processes, with model risk, auditability and human review treated as design requirements a risk committee can review.
Government and public sector
Readiness assessment and implementation with Canadian-only data residency, bilingual capability and the transparency an automated decision under Law 25 requires.
Pricing and engagement models
Strategy and implementation is priced as a fixed-scope assessment, a defined implementation project, or an implementation followed by a managed operation. The assessment stands alone and is deliberately low commitment, because the honest recommendation is the product. Implementation is priced against the defined use case, and the managed operation is priced as a running service where the system has to be maintained.
Frequently asked questions
What does an AI readiness assessment actually produce?
It produces a written recommendation you can defend to a board: which use cases are viable, which are not and why, the data and governance work each needs, and a costed path to production. It is deliberately low commitment, because the honest recommendation is the product rather than a lead into a larger contract.
Will you tell us not to build something?
Yes, and we do regularly. Honest discovery means naming the use cases that will not work in production before we scope the ones that will, because a failed programme costs both parties more than a declined one. A grounded no protects your budget and our credibility, which is the only basis a long engagement can run on.
How is a strategy engagement different from an implementation?
A strategy engagement decides what to build and whether to build it, ending in a costed recommendation and a defined use case. An implementation builds and ships it. The two are priced separately so you are never committed to a build to get an honest assessment, and many buyers stop after the assessment with a clear answer.
Do you hand the system over or run it?
Either. We can hand a system to your team with the runbook, the evaluation set and the training to own it, or run it as a managed operation under one contract. The choice is made on where the capability should sit long term, and we will tell you honestly which parts belong in-house.
How do you handle governance and Canadian privacy law?
Governance is designed in from the first conversation under PIPEDA and, where Quebec residents' data is in scope, Law 25, including its automated-decision provisions. Privacy impact assessment support, consent handling, human review and an audit trail are built into the design rather than assessed after launch, and data residency is configured to your requirement including Canadian-only.
Can you support work that needs annotation or evaluation?
Yes. Where an implementation needs annotation, preference data or credentialed evaluation, the group data operations and evaluation practice sits behind the same programme under one governance framework and one contract. That keeps the data pipeline, the model work and the oversight in a single accountable line rather than split across vendors.
Related services
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