Almost every agency now says it does AI. Some have built real capabilities in automation, custom development and AI-assisted creative. Others have added a few tools to their existing services and updated their website. For a brand trying to choose a partner, telling the difference is harder than it should be.
This guide is written for marketing, e-commerce and technology leaders in Toronto, Montreal and across Canada who want an AI partner that delivers measurable value. It covers what an AI agency should actually do, the questions to ask and the warning signs to watch for.
What an AI agency should do
A credible AI-first agency should be able to help across three areas, or be clear about which ones it focuses on.
Strategy
Identifying where AI creates real value in your business, prioritizing use cases and building a realistic roadmap. This should start with your goals and data, not with a favourite tool.
Implementation
Building and integrating the systems: AI agents, automation workflows, custom models or applications, and connections to your existing platforms such as your e-commerce store, CRM, help desk and analytics.
Content and marketing
Using AI responsibly in content, creative, SEO and paid media, with brand voice, accuracy and quality controls in place.
The strongest partners connect all three, because AI projects usually touch marketing, technology and operations at once. Our services are organized around exactly that combination of AI, Marketing and Development.
Questions to ask any AI agency
About experience
- Can you show work you have delivered, not just concepts?
- What platforms and tools do you build on, and why?
- Have you worked with businesses of our size and in our industry?
About approach
- How do you decide whether AI is the right solution for a problem?
- How do you measure success, and what baseline will you compare against?
- What does the first phase look like, and what will we have at the end of it?
About data and security
- What data will you need access to, and how will it be protected?
- Which third-party AI services will process our data, and under what terms?
- How do you handle PIPEDA and, for Quebec, Law 25 requirements?
About ownership and handover
- Who owns the prompts, workflows, code and models you create?
- Will our team be trained to run and improve the system?
- What happens if we stop working together?
Red flags
- Tool-first pitches. If the proposal leads with a specific product before understanding your problem, be cautious.
- Guaranteed results. No one can honestly guarantee specific performance from AI before testing it on your data.
- Vague deliverables. "AI transformation" isn't a deliverable. Look for concrete outputs and milestones.
- No human review. Any agency that plans to publish AI content or let agents act without oversight is taking risks with your brand.
- Lock-in. Proprietary systems you can't export or understand make it hard to switch later.
Why location still matters
Remote collaboration works well, but there are real benefits to working with a partner in your market. A Toronto or Montreal agency understands Canadian privacy law, bilingual requirements and the local business landscape, can meet in person for workshops and kickoffs, and works in your time zone. For brands serving Quebec, experience with French-language content and Law 25 is especially valuable.
Comparing proposals
When you have two or three proposals, compare them on:
- Problem understanding: Does the proposal reflect your goals and constraints?
- Scope clarity: Are deliverables, timelines and responsibilities specific?
- Measurement: Is there a clear plan for proving value?
- Team and process: Who will do the work, and how will you collaborate?
- Total cost: Include licences, usage fees, maintenance and internal time, not just the agency fee.
Start small
The best way to evaluate an AI partner is a focused first project with a clear goal, such as automating one support flow, building one content pipeline or launching one AI-assisted campaign. It shows how the agency works, produces measurable results and builds the foundation for larger initiatives.
Good first projects include an AI customer support agent, an AI-enhanced email flow or an AEO content audit.
What a good first engagement looks like
A strong agency will usually propose a short discovery phase before committing to a large build. Expect it to include:
Discovery workshops
Sessions with marketing, e-commerce, support and technology stakeholders to map goals, current processes, data sources and constraints.
A use-case shortlist
A prioritized list of opportunities, each with the expected value, effort, risks and data requirements spelled out.
A pilot plan
A clearly scoped first project with success criteria agreed in advance, a timeline and named owners on both sides.
A roadmap
How the pilot connects to longer-term work, so early decisions don't create problems later.
At the end of discovery you should understand exactly what will be built, why, and how you'll know whether it worked. If a partner can't explain that simply, keep looking.
Working together day to day
Ask how communication will work: regular check-ins, shared project boards, access to the people doing the work and a clear process for feedback and approvals. AI projects evolve as you learn from real data, so a collaborative rhythm matters more than a perfect plan on day one.
How Startup Slang helps
Startup Slang is an AI-first agency for marketing and development, with offices at 545 King St W in Toronto and 1155 Metcalfe St in Montreal. We work with e-commerce and enterprise brands on AI automation, custom AI development, marketing and e-commerce builds. Browse our work, learn about our AI Services, or get in touch to talk about a first project.
Preparing your team
Before you speak to agencies, align internally on what you want to achieve. Agree on one or two business goals, identify who will own the project day to day, gather examples of the processes you want to improve and check what data you can share. Agencies can give much more specific and useful proposals when you arrive with this groundwork done, and you will be better placed to compare them fairly.
Key takeaways
- Look for proven delivery across AI strategy, implementation and marketing.
- Ask detailed questions about data handling, privacy law and ownership.
- Treat guaranteed results and tool-first pitches as warning signs.
- A local Toronto or Montreal partner brings Canadian privacy and bilingual experience.
- Start with a focused pilot that has clear success criteria.
If you are weighing next steps, the most useful thing you can do this week is pick one process, write down how it works today, and decide what a better version would look like. That simple exercise makes every conversation with a partner, a vendor or your own team far more productive.
Frequently asked questions
What does an AI agency cost?
Costs vary widely with scope, from focused audits and pilot projects to ongoing development and content programs. Ask for proposals that separate one-time build costs from ongoing fees and usage costs.
Should we hire an AI agency or build an in-house team?
Many companies do both: an agency to move quickly, bring experience and build initial systems, and an internal team to own and evolve them over time.
Do we need clean data before starting?
Better data leads to better results, but a good agency can help you assess and improve your data as part of the first phase.
Can a Toronto agency work with companies outside Ontario?
Yes. Startup Slang works with clients across Canada and internationally from our Toronto and Montreal offices.