LLM development for Canadian teams that need models to actually 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 for an LLM build where eval-run review and prompt-change debugging happen async with daily written updates. For Eastern clients that need full Bay Street business-hours coverage on a complex LLM build, we route a dedicated overlap pod through our Frisco, TX office — Frisco runs Central Time, one hour behind Toronto, covering the same workday. Western Canadian hours (Vancouver, Calgary) are thinner; we cover them async-first with twice-weekly demos in Pacific morning showing eval-run numbers and cost telemetry. Daily written async updates with overnight regression and cost data land before your standup.

Because every LLM 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 (Claude 3.5 to 4.6, GPT-4o snapshot changes, Llama 3 to 3.1), 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, and pass-fail criteria. For bilingual Canadian LLM apps, we wire separate English and Quebec French eval sets in week one so translation-quality regressions get caught explicitly. Frameworks we use: Braintrust, Langfuse, Phoenix Arize, or a bespoke harness when the standard tools do not fit.

Yes. Engagement defaults align with PIPEDA federally and Quebec Law 25 for any LLM application processing Quebec-resident personal information. Every model call is audit-logged with prompt version, model name, input, output, retrieval citations (where applicable), and operator identity — exportable for an Office of the 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 LLM application surfaces the model name, the input categories used, and (where applicable) the right to human review. PII redaction patterns cover SIN, OHIP, RAMQ, MSP, driver licence numbers, 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), Cohere (Toronto-hosted), or self-hosted Llama 3 on vLLM inside your VPC — picked per your DPA's cross-border processing terms. For clients with strict no-US-inference requirements (federal-adjacent, OSFI at higher risk, provincial healthcare), self-hosted Llama 3 70B in ca-central-1 is the default; we have the deployment runbook for it.

Yes for the controls that affect the LLM application. For OSFI-supervised use cases, deterministic-output controls are wired where regulators expect them — temperature pinning at 0 or near-0, structured-output schema validation, refusal layers with measurable out-of-scope rates, and audit logs that capture the full prompt and the full output. Documentation aligned with Guideline E-23 on model risk management is provided as part of the engagement — model inventory, validation evidence, performance monitoring, and change management. FINTRAC-aware controls cover transaction-related LLM outputs and suspicious-activity flagging. For Guideline B-10 third-party arrangements, our DPA includes the documentation required for material outsourcing risk assessment. We do not provide regulatory advice; we build the controls and ship the documentation your CRO can defend.

Yes. Every bilingual engagement gets separate English and Quebec French eval sets in week one, a Quebecois language reviewer on the team, and prompt engineering that respects Quebec French conventions rather than translating from Parisian French or transliterating from English. Claude and GPT-4o handle Quebec French natively at production quality on most tasks; for self-hosted Llama 3 we evaluate the base model on the Quebec French eval set and fine-tune on a Quebec French corpus where the eval bar requires it. For RAG, retrieval is multilingual by default — the knowledge base can mix English and French documents and the system retrieves correctly regardless of query language. For Law 25 francisation expectations, the customer-facing surface ships in both languages from day one.

Yes — LLM takeover audits are routine. Step one is reading the code, the prompts (both languages where applicable), the eval results (if any exist), the retrieval pipeline, the bilingual handling quality, the model and provider choices, and the cost telemetry. Step two is shipping the smallest valuable change to prove we understand the system — usually wiring the eval harness (with bilingual evals if missing) or fixing the retrieval layer the previous vendor skipped. Step three is the longer-term plan: incremental stabilization, a model swap to a better-suited build, or a parallel rebuild if the architecture is unsalvageable. Most takeovers we see did not need a full rewrite; they needed evals, guardrails, observability, and a senior engineer on the build.

Most v1 LLM engagements at Aiinfox land between CAD $45,000 and CAD $190,000 fixed-price for a focused build — a copilot, a RAG-grounded LLM app, a bilingual customer-facing LLM application, or a fine-tuned domain model. Larger multi-quarter engagements with custom fine-tuning, bespoke bilingual evals, OSFI Guideline E-23 documentation, and integration into a regulated platform typically reach CAD $230,000 to CAD $400,000. The cost difference versus a Toronto or Montreal LLM 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. No swap-out to a junior pool mid-engagement.