Choosing an AI development company in Canada comes down to one question: can this team get a system into production in your environment, and keep it running? Most vendors can build an impressive demo. Far fewer can connect it to your ERP, pass your security review, prove its accuracy on your data and hand you something your team can own.
This guide gives you 12 questions to ask any AI development partner, the answers that should reassure you, the red flags that should not, and a simple scoring table to compare proposals side by side. It is written for operations, IT and procurement leaders at mid-size and enterprise Canadian companies. Full disclosure: we are one of the companies you might be evaluating, so we have kept the criteria to things you can verify yourself.
Why choosing the right AI partner matters so much
RAND’s 2024 research estimated that more than 80% of AI projects fail, about twice the rate of IT projects without AI. The causes it found were mostly about problem definition, data and infrastructure rather than the algorithms themselves. That is good news for buyers: those are exactly the things a capable partner manages, and exactly what you can probe in a selection process.
The market is also noisy. In 2025 Gartner estimated that only about 130 of the thousands of vendors claiming “agentic AI” offered genuine agentic capabilities, calling the rest “agent washing”: rebranded chatbots, RPA and assistants. Labels tell you little. The questions below are designed to get past them.
What are the 12 questions to ask an AI development company?
About delivery
1. What do you have running in production today, and how is it measured? Listen for specific workflows, volumes, accuracy figures and who monitors them. If every example is a pilot, a proof of concept or a hackathon, you are talking to a demo shop.
2. Walk me through your process from kickoff to launch. A good answer has phases, durations and a decision at the end of each one. Ours, for example, is discovery in weeks 1–2, a prototype on real data in weeks 3–5, the build in weeks 6–11 and launch around week 12, as described on our delivery process page. Whatever the vendor’s version, you want to hear where you can stop if the evidence is weak.
3. When will we see it working on our own data? The answer should be weeks, not months. A prototype on real examples is where the hard problems appear: messy scans, contradictory policies, missing fields.
4. Who exactly will do the work? Ask to meet the engineers. If senior people sell and junior people build, or the work is subcontracted, you should know before signing.
About quality
5. How will you prove the system is accurate enough? Expect a concrete method: a test set built from a few hundred of your real cases with agreed correct outcomes, run on every change. If the answer is “we’ll do user acceptance testing at the end”, the quality bar is a feeling.
6. What happens when the model is wrong or unsure? Look for confidence scores, approval queues, thresholds for money or compliance decisions, and an audit trail. A partner who says the model “won’t make mistakes” has not run one in production.
7. How do you choose the model, and can we switch? A good partner selects models by evaluation on your task (Claude, GPT, Gemini, Cohere, Mistral or open-weight models) and designs so that switching is a configuration change tested against the evaluation suite, not a rebuild.
About integration and security
8. Which of our systems have you integrated with, and how? Production AI lives inside NetSuite, SAP, Dynamics 365, Salesforce, ServiceNow, Microsoft 365 or your own databases. Ask how they authenticate, what permissions the AI gets and how they handle failures. Screen-scraping with a person’s login is a red flag.
9. Where will our data be stored and processed? For most Canadian enterprises, the expected answer is: in your own cloud account, in Canadian regions, with private model endpoints that do not train on your data. Our security and data handling approach is a reasonable baseline to compare against.
10. How do you handle PIPEDA and Québec’s Law 25? The vendor should know what a privacy impact assessment is, when Law 25 requires one and how they document data flows. If you operate in Québec, ask whether the system and its documentation will work in French. Our article on Law 25 and PIPEDA compliance for AI covers what to expect.
About commercials and ownership
11. How do you price, and what is excluded? Phased, fixed-scope pricing with written exclusions is a sign of a team that has done this before. An open-ended time-and-materials engagement with no phase gates shifts all the risk to you. For a full breakdown of cost drivers, see how much AI development costs in Canada.
12. What do we own at the end? You should own the code, prompts, configuration, evaluation suite, documentation and infrastructure, deployed in your accounts. Ask what happens if you end the relationship: could another team take over next month?
Red flags when evaluating AI vendors
- The demo is the proposal. A slick demo on public data, with no plan for your data, integrations or security.
- Platform first. You are asked to license a platform before anyone has looked at your workflow.
- Accuracy promises without a test set. “95% accurate” means nothing without knowing what was tested and how.
- No exit. Proprietary formats, vendor-hosted everything and no handover plan.
- Security later. “We’ll sort out privacy once the pilot works.”
- Everything is agentic. Every product in the deck has been renamed to include the word “agent”.
- No one says no. A partner who agrees that every idea on your list is a good fit is not filtering for value.
How to compare AI development proposals side by side
Score each proposal from 1 to 5 on the criteria below. Weight them to your situation; the defaults suit a first production system in one workflow.
| Criterion | Weight | What a 5 looks like |
|---|---|---|
| Production evidence | 20% | Specific systems running today, with metrics and an owner |
| Delivery process | 15% | Phases, durations, deliverables and go or no-go points |
| Evaluation method | 15% | Test set from your data, measured before launch and on every change |
| Integration depth | 15% | Named experience with your systems, API-based, scoped permissions |
| Security and privacy | 15% | Canadian hosting, private endpoints, audit logs, privacy impact assessment |
| Ownership and handover | 10% | You own code, prompts, evaluation suite and infrastructure |
| Team | 10% | Senior engineers you have met, no unexplained subcontracting |
Price is deliberately missing from the table. Compare prices only once the scopes match; otherwise you are comparing a demo with a production system.
Big consultancy, specialized studio or in-house?
| Option | Strengths | Watch out for |
|---|---|---|
| Large consultancy | Organization-wide programs, change management, large teams | Leverage models where juniors build, slower start, higher overhead |
| Specialized AI studio | Senior hands-on engineers, speed on a defined workflow, production focus | Capacity limits for very large programs; check depth in your systems |
| In-house team | Long-term ownership, deep business context | Hiring time, limited production AI experience, competing priorities |
Many mid-size companies use a partner for the first one or two systems, then take ownership internally. That works well when the partner builds in your accounts and hands over the evaluation suite, documentation and runbooks from day one. If you are still working out where to start, our about page explains how we work and who does the engineering.
A simple selection process that takes three weeks
- Week 1. Write a one-page brief for one workflow: volumes, systems, the metric you want to move and constraints such as Law 25 or bilingual users. Send it with anonymized examples to three to five vendors.
- Week 2. Hold 60-minute working sessions, not pitches. Ask the 12 questions. Note who asks the sharpest questions back.
- Week 3. Score written proposals with the table above, check references for production systems, and start with a paid discovery phase rather than a full commitment.
A short, paid discovery is the best due diligence you can buy. You see how the team works on your real problem, and you leave with a design and a test set you own, whoever builds the rest.
Ready to put us through the questions?
Start with our free AI blueprint: describe one workflow and get a solution outline, the systems involved and a realistic timeline in about two minutes. Then book a call and ask us all 12 questions. We will answer them with specifics, including the ones where the honest answer is that your project isn’t a fit.
