AI Agent Development Company in Montreal

Honest answers come in bands, quoted in Canadian dollars, and the driver is almost never the model. A scoped agent for one task, with a real evaluation set, retrieval over a defined corpus, tracing, a human escalation path and a bilingual interface, typically runs CAD 90,000 to 220,000 over ten to sixteen weeks. A production multi-agent system spanning several tasks, with tool access to systems of record, cost and latency budgets, monitoring and a staged rollout through shadow and assisted modes, typically runs CAD 250,000 to 700,000 over five to nine months. An agent platform that other teams build on, with model routing, a shared evaluation service, governance documentation and per-team guardrails, runs higher and should be phased. Inference and observability licensing sit on top of all of these and are a running cost rather than a build cost, so we model twelve months of them in the business case. Montreal senior engineering rates run below Toronto and materially below the American coastal markets. We quote fixed fee per increment against a signed statement of work.

AIDA, the Artificial Intelligence and Data Act, is not law and never came into force. It was Part 3 of Bill C-27, the Digital Charter Implementation Act introduced in June 2022, and that bill died on the Order Paper when Parliament was prorogued in January 2025 without the bill completing passage. Anyone telling a Montreal buyer that their agent must be classified under AIDA is selling compliance theatre. What actually applies is this. Federally, PIPEDA governs personal information handled in commercial activity, and it applies at all times to federal works and undertakings such as banks, airlines and telecommunications carriers. Provincially, Quebec is one of only three provinces with its own private-sector privacy statute, alongside British Columbia and Alberta, and Quebec's is the strictest of the three since Law 25. Sectorally, OSFI guidance governs models used by federally regulated financial institutions, and professional and health regulators govern their own fields. We design to those, and we design so that a future federal AI statute would be an incremental documentation exercise rather than a rebuild.

Law 25 was assented on September 22 2021 as chapter 25 of the statutes of Quebec, and its obligations arrived over the three following anniversaries: the person in charge of the protection of personal information and confidentiality-incident reporting to the Commission d'accès à l'information in 2022, the substantive consent, transparency and assessment obligations in 2023, and the data portability right in 2024. Three provisions bear directly on agents. Where a decision about an individual is based exclusively on automated processing, the individual must be informed of that fact and, on request, of the personal information used, the reasons behind the decision and the principal factors, and must be able to submit observations to a member of staff able to review it. Profiling, geolocation and identification technologies require notice and a means to deactivate them. And a privacy impact assessment is required before an information system handling personal information is acquired, developed or overhauled, and again before personal information is communicated outside Quebec. Penalties are not nominal: administrative monetary penalties reach CAD 10 million or two percent of worldwide turnover, and penal fines reach CAD 25 million or four percent.

Not by translating English output, which is the failure mode that Bill 96 reviews catch immediately. Language is decided at four layers. At the model layer we test candidates on Quebec French specifically rather than on European French, because vocabulary, register and everyday usage differ enough to change how an answer reads to a Montreal customer. At the retrieval layer, documents are indexed with a language tag and the retriever is scored separately per language, since a French question retrieving English passages produces a confident answer nobody asked for. At the evaluation layer we keep two frozen regression sets and require both to pass before release, so French quality cannot quietly decay while an English score improves. At the interface layer, French is the default for Quebec users rather than an option behind a toggle, no strings are concatenated, and layouts absorb French text expansion of roughly fifteen to thirty percent. A francophone reviewer working to the terminology standards published by the Office québécois de la langue française signs off on customer-facing and employee-facing output before launch, and again whenever prompts change.

Yes, and it is worth deciding early because retrofitting residency into a working agent usually means changing the model. Storage and retrieval sit in AWS ca-central-1 or Google Cloud northamerica-northeast1, both in Montreal, or in Azure Canada Central with Canada East in Quebec City. Inference runs through Amazon Bedrock or Azure OpenAI in Canadian regions, through a Canadian-headquartered vendor where procurement asks for one, or on self-hosted open-weight models on Canadian GPU capacity where nothing may leave a controlled environment. Quebec's hydroelectric generation gives the province some of the lowest industrial electricity rates in North America, which is a genuine reason self-hosting pencils out here more often than it does elsewhere, though we model the crossover against real token volume rather than assuming it. The parts that break residency designs are rarely the model: they are logging pipelines, evaluation tooling, support access and vector stores whose control plane lives elsewhere, so we inventory every dependency by actual processing region. Our own offshore access is in scope too, which is why the outside-Quebec transfer assessment is a deliverable and Chandigarh works against de-identified or synthetic data by default. The direct line is +1 (403) 604-8692.

Usually not, and we say so during scoping rather than after invoicing. The great majority of agent projects are engineering problems: retrieval quality, evaluation discipline, tool reliability, latency, cost control, escalation design and bilingual operation. None of that requires novel research, and attaching an academic partner to it adds coordination overhead and calendar time without improving the outcome. Genuine research collaboration is worth arranging when the problem is actually open, for example novel coordination between agents, learned policies where no supervised signal exists, or a guarantee about behaviour that has to be argued rather than measured. Montreal is an unusually good place to arrange that, through Mila's industry programs, the IVADO consortium and graduate labs at Université de Montréal, McGill, Polytechnique Montréal and Concordia, and the funding structures exist precisely for it. What we will not do is describe ourselves as a research lab. Our work is applied engineering and production operation, which is the stage where most Montreal agent pilots stall, and we tell clients which of the two categories their problem is in before the statement of work is written.