Generative AI development for Canadian teams that ship.

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 full Bay Street business-hours coverage, we route a dedicated overlap pod through our Frisco, TX office — Frisco runs Central Time, one hour behind Toronto but covering the same workday. Western Canadian hours (Vancouver, Calgary) are thinner; we cover them async-first with twice-weekly demos in Pacific morning. Daily written async updates with eval-run numbers land before your standup, so you walk into the day already knowing what regressed overnight.

Yes. Engagement defaults align with PIPEDA federally and Quebec Law 25 for any generative system processing Quebec-resident personal information. Every model and tool call is audit-logged with prompt version, model name, input, output, and operator identity — exportable for a Privacy Commissioner inquiry or a Commission d'acces a l'information review. A Privacy Impact Assessment is run for engagements processing personal information at scale or operating on sensitive categories. For Article 12.1 of Law 25 (automated decision-making transparency), the system surfaces the model name, the input categories used, and (where applicable) the right to human review. PII redaction patterns cover SIN, provincial health card numbers, OHIP IDs, and Canadian banking identifiers.

Your call. We default to AWS ca-central-1 (Montreal), Azure Canada Central (Toronto), or GCP northamerica-northeast1 / northeast2 for Canadian clients, and we will run the entire build inside your Canadian cloud account if your DPO requires no cross-region replication and no data egress to US endpoints. For LLM inference, we route Claude (Anthropic), GPT-4o (Azure OpenAI Service Canada Central), or self-hosted Llama 3 on vLLM inside your VPC — picked per your DPA's third-party processing terms. For clients with strict no-third-party-inference requirements (federal-adjacent, defence, healthcare), self-hosted Llama 3 70B is the default; we have the deployment runbook for it.

Because every Canadian generative AI engagement we have audited that failed in production failed because nobody wrote the eval set. The team tuned a prompt until it looked good on three examples, the model swapped underneath them in a vendor update, and quality regressed silently for weeks before someone noticed in a customer complaint. The eval harness is the regression test for the LLM — a fixed reference set of inputs, expected behaviours (faithful citation, refusal when out of scope, structured output validity), and pass-fail criteria. We wire it in week one and run it on every prompt or model change. It is the difference between shipping a generative AI system and shipping a demo.

PIPEDA + Law 25-aligned DPAs covering processor obligations: documented instructions, confidentiality, security of processing, sub-processor management, breach notification, and deletion at end of engagement. Mutual NDAs before any technical detail is shared. MSAs for ongoing relationships and per-project SOWs for fixed-price builds. For healthcare engagements, BAAs or provincial-equivalent agreements. For OSFI-supervised clients, our DPA includes documentation required under Guideline B-10 for third-party arrangements. Cross-border processing safeguards are spelled out in Schedule 4. Aiinfox Pvt. Ltd. is a registered Indian entity invoicing in CAD or USD — no T4A entanglement.

Most v1 generative AI engagements at Aiinfox land between CAD $40,000 and CAD $180,000 fixed-price for a focused build — a copilot, a RAG-grounded GenAI app, a voice pipeline, or a fine-tuned domain model. Larger multi-quarter engagements with custom fine-tuning, bespoke evals, Law 25 documentation, and integration into a regulated platform typically reach CAD $220,000 to CAD $380,000. The cost difference versus a Toronto or Montreal AI consultancy lands roughly 30 to 50 percent lower on senior rates — useful, but the headline is the engineer on your kickoff call writes your prompts, your evals, and your code through launch.

Yes — takeover audits are routine. Step one is reading the code, the prompts, the eval results (if any exist), the data pipelines, and the cost telemetry. Step two is shipping the smallest valuable change to prove we understand the system — usually adding the eval harness or fixing the retrieval layer. Step three is the longer-term plan: incremental stabilization, a parallel rebuild, or shutting it down and starting over. Most takeovers we see did not need a rewrite; they needed evals, guardrails, observability, and a senior engineer on the build.

Yes. We have shipped GenAI products in English and Quebec French. For voice, Deepgram handles Quebec French STT and ElevenLabs or Azure Neural TTS produces Quebec French voices tuned on Quebecois conventions rather than Parisian French. For text generation, Claude and GPT-4o handle Quebec French natively at production quality; for self-hosted Llama 3, we evaluate the base model and fine-tune on a Quebec French corpus where the eval set requires it. RAG retrieval is multilingual by default — the knowledge base can mix English and French documents and the system retrieves correctly regardless of query language.