Strategy through execution. The decision first, then the build.
Platforms we build on
You do not have to build the entire foundation before anything moves. Put one piece of the data foundation in place, start driving value with it, then take the next piece.
Chart the course before you sail it.
Your agents are only as capable as what they can see.
Technology adapts to you, not the reverse.
Grow beyond one size fits all.
The industry is settling on a name for this model: forward deployed engineering. Senior specialists inside the client's environment, building what the business actually needs, then handing back the keys.
Business centre, Montréal
A mapping workshop, then a portal that swallowed month-end.
Month-end ran on paper and a three-person team. We mapped it, then built a SharePoint portal with Power Automate flows where every task starts and ends: 20 hours recovered a month. AI on invoicing then saved 5 more, because the context it needs was already in the portal.
Montréal
One hour of workflow mapping, then a system they own.
We started with a one hour workflow mapping workshop across their operation. That produced a custom Notion CMS synced to Square, feeding a website their team runs themselves with no agency in the loop.
*Enterprise outcomes achieved by members of the founding team before founding QAI.
The people who run the work every day, on what changed.
“I used to have a desk buried in paper. Now every task starts and ends in the same place: our SharePoint portal. It changed the way I work. And when we work with Claude, all the context it needs is already there.”
“JP gave us agility. We update our site ourselves and test a promo or a new idea the same day. No developer, no waiting: we're independent.”
Strategy without tactics dies. Whether your plan comes from your own boardroom or a strategic advisory firm, we are the partner that builds it: we extend your advisors, we don’t replace them.
| Quantumize AI | Strategic advisory firms | Technology vendors | |
|---|---|---|---|
| A clear plan for AI in your business | Yes | Yes | Biased toward their product |
| Maps how your teams work, task by task | Yes | Sometimes | No |
| Gets your people on board before the build | Yes | Rarely | No |
| Builds the systems and keeps them running | Yes | Usually handed off | Their platform only |
| Puts senior specialists inside your teams | Yes | Rarely | Only to deploy their product |
| The best AI for each job, no lock-in | Yes | Advice only | No, theirs |
| Retunes your systems as the models improve | Yes | Engagement ends | Their product, their pace |
The workflow assessment: one or two of your workflows, four weeks, three workshops. Your team gives about an hour and a half a week for three weeks, and comes out with a map of the work and a ranked list of where to start.
Week 1
The whole team of one domain, finance for example, in a room for an hour and a half. What works, what does not, then a vote on the biggest pain points.
Week 2
Only the people who run the chosen workflows. We break each step down to the task, with volumes and time per task, then vote on where to start.
Week 3
The same people, one question: with a magic wand or a hundred interns, how would you split this work? Between sessions, we sit with the people who do the work.
Week 4
We present the task map and the ranked list, by hours saved and by effort. You confirm the priorities, and we send a proposal for the option you choose.
Mostly time. About an hour and a half a week for three weeks, from the people who know the domain and from the people who do the repetitive work. They gain the most from the change, and later they are the ones who review what the agents produce.
A map of the chosen workflows down to the task, and a ranked list of where to start, by hours saved and by effort. Then a written proposal for the option you choose, and you decide whether to go further.
No. Your people know the work, and the workshops are built to draw it out of them. In a previous role, one of our founders joined a procurement team knowing nothing about supply chain. Three workshops, under five hours in all, set that team's roadmap for the next twelve months.
One of our three founders leads every engagement, from the first workshop to the handover. When the work needs more hands, a Montréal partner we have worked with since our previous roles supplies them: data and software developers, data scientists, architects, business analysts, and specialists in cloud, security, testing and change management, on the major cloud and data platforms. A team can be in place within two to three weeks, and several projects can run at once, each with a founder in charge, and none of the work is offshored.
Both. We can take one project from start to finish, or act as your AI office over time: hands-on sessions on the problems your teams bring, office hours every two weeks, and a review of the tools your people build for themselves. Either way, your team learns to run and fix what we build.
An agent gets access the way a new employee does: only what its task needs, with every action logged. Rules are enforced by settings your IT pushes to every user, because a prompt alone cannot enforce a rule. If a model must stay off foreign servers, an open model can run on your own infrastructure.
Quantumize AI is a Montréal data and AI consulting firm for businesses of every size, from SMBs to large enterprises.
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