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Questions from Toronto fintech and SaaS product teams.

What does an AI product spec for a Canadian fintech need that a US fintech spec does not?

Three sections that most US AI PM frameworks do not cover. First, the PIPEDA data use analysis: a description of what personal information the AI feature uses as input, what the original collection purpose was, and whether using that data for AI inference or training falls within reasonable customer expectations under PIPEDA's purpose limitation principle. A fintech using transaction data to train a fraud detection model needs this analysis before development starts, not before launch. Second, the OSFI model documentation: for AI models used by or sold to federally regulated financial institutions in Canada, OSFI Guideline E-23 requires a model development memo covering training data, methodology, and limitations. A vendor selling into TD, Scotiabank, or Manulife will face this requirement through the institution's procurement process. The spec includes the documentation the model risk team will ask for. Third, the data residency specification: for AI features that process personal information, the spec states where model inference runs and whether that location satisfies any data residency obligations in the product's contracts or regulatory context. Canadian government contracts and some financial sector agreements require data to remain in Canada. A Toronto fintech routing inference through US-based model APIs may be non-compliant for those contracts without knowing it.

How do you write AI product specs for Shopify's merchant ecosystem. What do merchants care about that enterprise buyers do not?

Shopify merchants care about three things that enterprise buyers rarely prioritize. First, setup time and configuration burden: an AI feature that requires 30 minutes of configuration before it produces useful output will not be adopted by a merchant running a 10-person operation. The spec defines the maximum steps to first value for the AI feature, and engineering builds to that constraint. Second, explainability in plain language: merchants are not technical buyers. When an AI feature recommends a pricing change or a product restock, the merchant wants to understand why in terms that map to their business, not in model confidence scores. The spec defines the explanation format and tests it with actual merchant users before launch. Third, error tolerance and recovery: a merchant whose AI feature produces a wrong recommendation and has no clear path to correct or override it loses trust in the feature and stops using it. The spec defines the override mechanism, the feedback path for wrong outputs, and how the feature communicates uncertainty. For a Shopify app developer, the spec requirement is more user experience than compliance, but the discipline of writing it before building prevents the same class of problems.

What does Cohere vs. OpenAI mean for a Toronto AI product spec. How does the vendor choice change the spec?

The vendor choice changes three sections of the spec. First, the data residency section: Cohere offers Canadian data residency options for inference and fine-tuning. If the spec requires Canadian data residency, Cohere is a viable option and OpenAI currently is not. The spec states the data residency requirement explicitly before vendor selection, so the requirement drives the choice rather than the choice constraining the requirement. Second, the fine-tuning section: Cohere's Command models are designed for enterprise fine-tuning on proprietary corpora. If the AI feature requires fine-tuning on company-specific documents, Cohere's fine-tuning infrastructure and contract terms differ materially from OpenAI's. The spec defines whether fine-tuning is required, and on what dataset, before the vendor is selected. Third, the pricing model section: Cohere and OpenAI price differently at different volume tiers. The spec defines the expected inference volume at 12-month horizon, and the vendor evaluation includes a total cost comparison at that volume. A Toronto fintech that evaluates Cohere only on benchmark performance and not on data residency or pricing at projected scale is missing the factors that most differentiate it for Canadian use cases.

How do you spec an AI feature for a Toronto financial services company when the AIDA regulatory text is not yet final?

Speccing for AIDA uncertainty requires separating the decisions that can be made now from the decisions that depend on final regulatory text. The decisions that can be made now: document the intended use at the level of specificity that AIDA's high-risk definition will require regardless of how the final text is written. If the AI feature makes or assists decisions about employment, credit, insurance, or healthcare for individuals in Canada, document the intended use, the population affected, and the known risks before development starts. This documentation is required under any plausible final AIDA text and is reusable for compliance assessment after the final text is available. The decisions that should wait for final text: the specific format of the risk assessment, the exact content of the transparency statement, and the incident reporting threshold. These depend on the final regulation and should be built in during a post-passage compliance review, not specced to a draft. The spec includes a section noting the AIDA dependency and the documentation that satisfies any plausible outcome, so the product team is not caught unprepared when AIDA passes.