Practical AI, not a chatbot for the sake of it
Every business is being sold AI right now. Most of it is a generic chatbot pinned to a website. We start from the other end: which tasks in your week are repetitive, language-heavy and rules-based? Answering the same customer questions, qualifying enquiries, drafting quotes, summarizing calls, sorting documents. Those are the jobs where current AI models are genuinely good, and where the payback is easy to measure.
We then build a tool scoped to that job, trained on your real material, with a human in the loop wherever a mistake would be costly. If a task does not need AI, we say so and build a plain automation instead. It is cheaper and more predictable.
What we build
- Customer assistants that answer in French and English from your own documents, pricing and policies, hand off to a human when needed, and log every conversation.
- Lead qualification that reads incoming enquiries, scores them against your criteria and routes hot leads to the right person with a summary.
- Content systems that draft product descriptions, listings, proposals or first-draft articles in your voice for a human to finish.
- Document and data processing that extracts what matters from invoices, forms, contracts and emails into your systems.
- Internal copilots that let your team ask questions of your own knowledge base instead of searching folders.
Most of these ship as part of a larger system, which is why AI work often sits inside our custom software projects. We also wrote about how AI changes the economics of web design itself in this piece.
Your data, your control
Trust is the whole game with AI. We design each solution so you know what data it can see, where it is stored, and what it will never do. Customer data stays in Canada where the use case requires it, sensitive fields are excluded or masked, and every answer can be traced back to its source. You get an admin view to review conversations and correct the assistant's knowledge without a developer.
We choose models on fit and cost for the task rather than loyalty to one vendor, and we build so that swapping the model later is a configuration change, not a rebuild.
Measuring the return
Every AI project starts with a baseline: how many hours a week the task takes today, how many enquiries go unanswered after hours, how long a quote takes to go out. After launch we measure the same numbers. That is the return, in hours and in revenue, and it is how we decide together whether to extend the tool or stop.
Bring the repetitive task to the project form. We will tell you within a day whether it is a good fit for AI and roughly what it would take.