PIPEDA AI development for Canadian teams that ship.

PIPEDA and Law 25 do not have third-party vendor certification schemes that AI vendors typically hold. What both require is documented evidence of appropriate technical and organizational measures — and that is what Aiinfox provides. We sign a DPA before any personal information is shared, run a Privacy Impact Assessment where the engagement processes personal information at scale, pin inference to a Canadian region where required, write audit logs on every model and tool call, and document the lawful basis and consent capture for each processing purpose. For Quebec Law 25 cohorts, we layer the additional obligations on consent transparency, automated decision-making disclosure, data portability, and de-indexation. For BC, PIPA controls layer on. For Alberta, PIPA-AB. We will not market a certification we cannot hold; we will sign the DPA and stand behind the controls.

Law 25 (Act respecting the protection of personal information in the private sector, as amended 2021-2023) adds harder obligations on top of PIPEDA for Quebec-resident personal information. Specifically: explicit and informed consent for each processing purpose (not bundled consent), disclosure to the individual when personal information is transferred outside Quebec, transparency on automated decision-making with the right to request a human review, a right to data portability, and a right to de-indexation. For AI builds, this means consent capture is per-purpose and recorded in the audit log, the privacy notice spells out cross-border data flows (including LLM inference if it crosses the border), and any deployment with material automated decision-making includes a documented human-review path. We design Law 25 obligations into the system from week one — not retrofitted after a Commission d'access a l'information inquiry.

Inside your AWS, Azure, or GCP Canadian account by default — AWS ca-central-1 (Montreal), Azure Canada Central (Toronto) or Canada East (Quebec City), GCP northamerica-northeast1 (Montreal) or northamerica-northeast2 (Toronto). For inference, you have three options. One: managed LLMs with documented cross-border transfer — Anthropic Claude via AWS Bedrock, OpenAI via Azure OpenAI Service, with the cross-border flow disclosed in the privacy notice and DPA. Two: self-hosted Llama 3 / 3.1 on vLLM inside your Canadian VPC — zero cross-border LLM exposure, full control of logging. Three: hybrid — non-personal-information prompts route to managed LLMs, personal-information-bearing prompts route to self-hosted Llama. For Law 25 cohorts that have ruled out cross-border transfer, option two is the default.

Yes. We have shipped AI chatbots and voice agents in English and Quebec French. For voice agents, we route Deepgram for Quebec French STT and ElevenLabs or Azure Neural TTS for Quebec French voices, with prompt tuning on Quebecois conventions rather than Parisian French. RAG retrieval is multilingual by default — your knowledge base can mix English and French documents and the agent retrieves from both. Consent notices, transparency statements, and automated decision-making disclosures are drafted in both languages with French legal review where the engagement touches Law 25 obligations. The audit log records the language of interaction so a Commission inquiry can reconstruct the conversation in the original language.

Eastern Canadian hours (Toronto, Montreal, Ottawa) get a native two-to-three-hour late-afternoon overlap with our Mohali IST day, which is workable but not full coverage. For Eastern clients that need same-zone synchronous time, we route a dedicated overlap pod through our Frisco, TX office — Frisco runs Central Time, which is one hour behind Toronto but covers the same workday. Western Canadian hours (Vancouver, Calgary) are thinner; we cover them async-first with twice-weekly demos scheduled in Pacific morning. Written async updates land before your standup. If your engagement genuinely cannot survive without synchronous coverage at all hours, we will say so on the first call.

Yes — this is the most common Canadian deployment pattern. We work inside your AWS ca-central-1 (Montreal), Azure Canada Central (Toronto), Azure Canada East (Quebec City), or GCP northamerica-northeast1 / northeast2 account using your IAM, your VPC, and your customer-managed encryption keys. For inference, we route to Canadian or US endpoints depending on your DPA and Law 25 posture, or we self-host Llama 3 on vLLM inside your Canadian VPC if your privacy officer has ruled out cross-border LLM inference. No personal information leaves your cloud unless your runbook says it should.

Senior engineering rates at Aiinfox land roughly 30 to 50 percent below equivalent Toronto, Montreal, or Vancouver AI consultancies — useful, but it is not the headline. The headline is the delivery model: senior engineers only, fixed-price six-week scope, overrun cost on us if we miss for reasons on our side, DPA and PIA documented before kickoff. Most Canadian AI consultancies bill timesheets, run multi-month discovery in a tight senior-engineer market, and either swap senior staff onto bigger accounts mid-engagement or staff a junior pool behind a senior nameplate. We bill shipped systems; the engineer on your kickoff call writes your code through launch.

Yes. We have shipped KYC automation, FINTRAC-aware transaction monitoring, fraud signal extraction, and deterministic-output compliance copilots for fintech and lending operators serving Canadian and US markets. Every model and tool call is audit-logged with input, output, prompt version, retrieval sources, and operator identity — so the Chief Compliance Officer accountable for the system has the evidence they need for an OSFI examination or FINTRAC audit. For OSFI E-23 (Enterprise-Wide Model Risk Management), we structure the deployment to fit inside your model risk framework: documented purpose, documented data, documented evaluation, documented change management. Humans approve everything that touches a regulated outcome.

Yes — takeover audits for PIPEDA and Law 25-scoped builds are routine. Step one is a data-flow audit: where does personal information actually touch storage, inference, and logging; which provinces' residents are affected; and which cross-border transfers have a documented mechanism? Step two is reading the code, evals, refusal layer, and audit-log schema, then shipping the smallest valuable change to prove the system is operable. Step three is the longer-term plan — incremental fixes, a parallel rebuild, or shutting it down. Most takeovers we see did not need a full rewrite; they needed a missing PIA, residency pinning, and audit logs that could survive a Commissioner inquiry.