We build prediction models, automation pipelines, and data dashboards for mid-size companies across Quebec and Ontario. Our average client sees a 31% drop in manual processing time within the first 90 days.
Get a free assessment How we work
Every project starts with a two-week discovery sprint. We map your existing data sources, interview the people who use them daily, and identify the three or four places where an AI model will save measurable time or money. Then we build only those pieces.
We train regression and classification models on your historical records, whether that is sales data, equipment sensor logs, or patient intake numbers. Typical accuracy targets sit between 85% and 93%, depending on how clean the source data is. We deploy the model behind a REST API your existing tools can call.
Invoices, insurance claims, purchase orders: our OCR plus NLP pipeline reads them, extracts the fields you care about, and writes structured records into your ERP or database. One logistics client reduced their data-entry headcount from six people to two without losing accuracy.
We connect your CRM, accounting package, and communication tools through event-driven automations. When a quote is approved in Salesforce, the invoice drafts itself in QuickBooks, the project board creates its tasks, and the client gets a confirmation email. No copy-pasting between tabs.
Real-time charts and KPI tiles that refresh every five minutes. We design them for the people who actually look at the data: warehouse managers, clinic directors, marketing leads. Each dashboard answers a specific question instead of dumping every metric onto one screen.
These figures come from projects we completed between January 2023 and March 2025. We track outcomes for at least six months after delivery so the numbers reflect sustained performance, not launch-day optimism.
Most AI consulting firms hand you a proof of concept and leave. We stay through deployment and the first quarter of live operation, because models that work in a notebook often stumble once real users start feeding them messy data.
Two weeks of interviews, data audits, and process mapping. You get a written report that lists every opportunity we found, ranked by expected ROI and implementation difficulty. There is no obligation to proceed past this step.
Our data scientists build, train, and validate the models using your real data, running inside a secure environment on Canadian servers. We share accuracy metrics weekly so you can see progress without waiting for a big reveal.
We wire the model into your production systems, set up monitoring alerts, and train your team on what the outputs mean and when to override them. Launch day is usually quiet, which is exactly what you want.
Data drifts over time. Customer behaviour shifts, product lines change, regulations update. We retrain models quarterly or on-demand, and our support agreement covers bug fixes within 24 hours on business days.
Book a 30-minute call. We will look at one of your current pain points together and tell you honestly whether an AI approach makes sense for it.
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