AI Development in Canada
We are an Indian engineering team working with businesses in Canada. Written scope before any code, and a fixed quote in CAD.
Working hours
Our day ends at 07:30 in Canada (08:30 on summer time), before yours begins, so we work asynchronously: written updates land overnight and are waiting when you start.
Quoted in CAD
Quotes exclude the 5% federal GST in Canada, shown separately on the invoice.
Data protection
Work for this market is scoped against PIPEDA, and provincial privacy law where it applies, agreed before development starts rather than retrofitted afterwards.
Written scope
Every engagement starts with a written scope and a fixed CAD quote within 24 hours, so the price is set before work begins.
About AI Development
Most AI projects fail in the same place: a demo works on ten hand-picked examples, then falls apart on real user input, real data volume, and a real invoice at the end of the month. We build the unglamorous parts that decide whether an AI feature survives contact with production: retrieval that returns the right context, evaluation you can run before every deploy, guardrails on what the model is allowed to do, and cost tracking per request so the bill is a number you chose rather than one you discover.
What the engagement includes
- Retrieval-augmented generation over your own documents and databases
- AI agents with tool access, scoped so they can only do what you allow
- Model Context Protocol (MCP) servers that connect assistants to your systems
- LLM integration into existing products: OpenAI, Anthropic, and open models
- Document extraction and classification for invoices, contracts and forms
- Evaluation suites so a prompt change cannot quietly break output quality
- Cost and token controls with per-request tracking and hard budget limits
- Chatbots and support assistants grounded in your own content, not the open web
Who supervises an AI system in Canada, who validates it, and which language it answers in
There is no single Canadian AI statute to build against, and saying so in week one saves a month of scoping. The Artificial Intelligence and Data Act travelled inside Bill C-27 and died on the order paper when Parliament was prorogued in January 2025, so a compliance plan that points at a federal AI act is pointing at something that never came into force; what has replaced it by the time you read this is a question for your own counsel rather than an assumption we make on your behalf. What binds the system in the meantime is decided by who buys it and what it is allowed to decide. If a federal department will use it to make or assist an administrative decision about someone, the Treasury Board Directive on Automated Decision-Making is the document that governs it, and the build feels that even though the obligation is the institution's: an Algorithmic Impact Assessment has to be completed and its results published, and the level it returns is what sets whether notice is required, whether a person has to be able to intervene before a decision takes effect, what explanation the affected individual gets and whether the system goes through peer review first. Whether it reaches a particular buyer is worth confirming with them rather than assuming, because Crown corporations and some agencies sit outside Treasury Board direction. What it changes for us is that the assessment asks things only the code can answer: what data the system is grounded on, whether a given output is a recommendation or the decision itself, who can override it and how the override is recorded. We write those answers down while the system is being built, because reconstructing them afterwards from logs nobody designed for the purpose is where these projects lose their timeline. The narrower duty that more Canadian buyers actually meet is Ontario's. Since the start of 2026, an employer above the threshold set under the Employment Standards Act has to state in a publicly advertised job posting when artificial intelligence is used to screen, assess or select applicants, which makes a ranking or shortlisting feature something your customer has to declare in public, and that is worth knowing before anyone designs a score that cannot be described.
French breaks an AI system in a way it does not break a bilingual website, because a site has a French version and a model has an output distribution. Two failures come up again and again. The first is retrieval. If the corpus the assistant is grounded in is English only, a question typed in French returns weak matches or none, and a model handed nothing useful still answers, so your Quebec customer gets a confident invention while your English-speaking customer gets the cited, correct answer, and nothing in an English test suite catches it. That is a retrieval decision rather than a prompt one: either the corpus carries both languages with the pairs linked, or embeddings and queries are arranged so a French question can reach an English source and the answer is generated in French with the source named. The second failure is register. Left to itself a general model writes the French of France, so where your Quebec reader expects the term the Office québécois de la langue française publishes, courriel being the example everyone recognises, the model reaches for the word a Paris office would use. It also mishandles the message that switches between French and English inside a single sentence, which is common enough here that naive language detection routes it to an English reply. So the evaluation set is written in the French your Quebec users actually type rather than in textbook French, and the pass mark comes from a French reader rather than from a similarity score. Whether the Charter of the French language reaches a given automated assistant, and how far, is for your counsel to determine on your own facts, and it is a cheaper question while the corpus is being assembled than after launch; what we hold ourselves to is that French output is measured before launch rather than assumed to follow from the English working.
If your organisation is federally regulated, your own risk function will hand you a harder requirement than any AI statute would. OSFI's model risk guideline, E-23, was revised with machine learning models in view rather than only credit and capital ones, and what it asks for is not a demonstration: an entry in a model inventory with an owner and a risk rating, a written statement of intended use and known limitations, review by someone independent of the people who built it, and monitoring that continues after launch. Ask your risk function which parts apply to you and on what timetable rather than taking a date from us, but plan the build as though they do, because what a reviewer needs is evidence rather than access, and evidence is far cheaper produced as you go than assembled in the week before a first validation. In practice that means a record of which prompt, configuration and model version was live when a given result was produced, and monitoring that watches whether output quality is drifting rather than only whether the endpoint is up. Health data is the other place this bites, and it bites differently by province. Where your organisation is a custodian of health information under its provincial statute, we sit on the far side of that relationship as a service provider acting on your instruction, which constrains what may be done with the information and not only where it is kept, so the retention and sub-processing terms of whichever model endpoint the system calls belong in the contract chain rather than in an account setting somebody finds afterwards. Whether inference may leave Canada at all is answered by the rule that applies to your organisation, which differs by province and differs again for public bodies, so that answer gets settled before the architecture is fixed rather than after a demo has already been wired to whichever endpoint a vendor's example used.
How we work with Canadian clients
Two things shape Canadian projects more than anything technical. The first is language: if you serve Quebec, French is a legal requirement rather than a nice addition, and the rules tightened considerably under Bill 96. The French version has to be available on equal terms with the English one, which is a design constraint from the first wireframe rather than a translation task at the end.
The second is anti-spam law. CASL is stricter than most teams expect, and the penalties are real. Consent has to be express or clearly implied, records of it have to be kept, and every commercial message needs a working unsubscribe and accurate sender identification. We build those into the product rather than leaving them to whoever configures the mailing tool later, because that is where the exposure usually sits.
Indicative pricing in CAD
Converted from our published Indian rates and rounded. Exchange rates move, so the figure on your quote is calculated on the day it is issued.
Full service detail
This page covers how we work with clients in your market. The complete AI Development page, with the full technical detail, process and frequently asked questions, is written in English.
Read the full service page (English)Questions Canadian clients ask
We serve Quebec. What does the French requirement actually mean?
In practice, the French version cannot be an afterthought. It has to be available on terms at least as favourable as the English, which affects navigation, forms, error messages, transactional email, and support content rather than just marketing pages. We plan for two languages at wireframe stage, because French copy typically runs longer than English and a layout built for one will break on the other. Translation is done by a French speaker rather than machine output.
How do you handle CASL for email and notifications?
As a product requirement, not a setting. That means capturing and storing proof of consent with a timestamp and source, distinguishing express from implied consent along with when implied consent expires, an unsubscribe that works in every message and takes effect quickly, and accurate sender identification. Transactional messages are treated separately from commercial ones in the code, because conflating them is the most common way teams end up sending something they should not have.
GST, HST, or PST? Which applies to your invoices?
We are outside Canada, so our services are generally not subject to Canadian sales tax on our invoice, and self-assessment rules may apply depending on your province and registration status. Because the provincial position varies considerably, we would rather your accountant confirms the treatment before the first invoice than reconcile it afterwards. We quote in Canadian dollars and hold the figure for the project.
You are many time zones away. How does that work day to day?
Honestly, with a written-first working style. Our day ends before yours starts even in the east, and overnight in the west, so there is no live overlap to schedule around and we do not pretend otherwise. Work is handed over in writing at the end of our day, which lands as your morning, and questions that would block us are flagged before we stop rather than discovered overnight. One scheduled call a week in your morning covers what genuinely needs a conversation.
AI Development: common questions
Do we actually need AI for this?
Often not, and we will tell you before you spend anything. Roughly half the briefs that reach us are better solved by a database query, a rule, or a clearer form. AI earns its place when the input is genuinely unstructured and the output is genuinely open-ended. If your problem is deterministic, a model just makes it slower, costlier and less predictable.
How do you stop it making things up?
Grounding and checking. The model answers only from context we retrieved from your data, we validate the structure of what comes back, and we make it cite the source so a human can verify it. For anything where a wrong answer is expensive, we design a human approval step rather than pretending the problem is solved. No prompt makes a model incapable of being wrong.
What will it cost to run, not just to build?
That depends on model choice, context size and volume, and it is one of the first things we size rather than one of the last. We build per-request cost tracking and hard budget ceilings in from the start, and we route cheap requests to cheap models. A typical grounded support assistant runs a few thousand rupees a month at moderate volume; a heavy document pipeline can run considerably more, which is exactly why it gets measured before launch.
Whose data does the model see, and where does it go?
We use enterprise API tiers where prompts are not used for training, and we scope precisely what leaves your systems. Where data cannot leave at all, we build on open models you host yourself. This gets settled in scoping, not discovered during a security review.
What is MCP and do we need it?
Model Context Protocol is a standard way to give an assistant controlled access to real tools and data, so it can query your systems instead of guessing. It is useful when you want an assistant to actually do things rather than only talk about them. If you just need a grounded chatbot over a set of documents, you do not need it.
Other markets we work in
Tell us what you are building in Canada
Send the scope, or just the problem. You get a written scope and a fixed CAD quote back within 24 hours, from the engineers who would do the work.