Predictive analytics
Demand forecasting, churn prediction, and anomaly detection models trained on your historical data. We favour gradient-boosted ensembles and temporal fusion transformers for tabular and time-series tasks.
// Montréal-based, globally curious
We build AI software that solves problems you thought were unsolvable
Not another chatbot wrapper. We design, train, and deploy custom machine-learning systems that integrate with your existing stack and deliver measurable business outcomes within weeks, not quarters.
Show me what's possible
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Projects deployed
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Models in production
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Avg. prediction accuracy
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System uptime (12 mo.)
// How we work
From messy data to working product in five moves
Every engagement follows the same rigorous arc. We compress discovery, prototyping, and deployment into a single continuous loop so you see results before the budget review.
1. Deep-dive diagnostic
We spend the first week inside your data ecosystem — auditing pipelines, interviewing domain experts, and mapping where AI software can create the highest-leverage impact. No slide decks, just a working brief.
2. Rapid proof of concept
Within two weeks we deliver a functional prototype using a representative subset of your data. You interact with real predictions, not mockups, and we iterate based on your feedback in 48-hour sprints.
3. Model hardening
We stress-test for edge cases, bias, and drift. Explainability reports accompany every model so your compliance team and stakeholders understand exactly why the system makes each recommendation.
4. Production integration
Our engineers embed the model into your existing infrastructure — whether that is a REST API, an event-driven microservice, or a batch pipeline feeding your data warehouse. Zero-downtime deploys are the default.
5. Ongoing monitoring and retraining
Models degrade. We set up automated drift detection, performance dashboards, and scheduled retraining cycles so your AI software stays sharp long after launch day.
// What we build
Six disciplines, one integrated team
Computer vision
Object detection, semantic segmentation, and visual inspection pipelines for manufacturing, retail, and logistics. We deploy on edge devices when latency matters and on cloud GPUs when throughput does.
Natural language processing
Custom entity extraction, intent classification, document summarisation, and retrieval-augmented generation systems that ground answers in your proprietary knowledge base — not the open internet.
Intelligent automation
End-to-end workflow orchestration that combines rule engines with ML classifiers. Invoice processing, contract review, and support ticket routing are common starting points for our clients.
Recommendation engines
Collaborative filtering, content-based, and hybrid recommenders for e-commerce, media, and SaaS platforms. We optimise for business KPIs — revenue per session, not just click-through rate.
MLOps and infrastructure
CI/CD for models, feature stores, experiment tracking, and GPU cluster management. We help teams that already have data scientists but struggle to get models into production reliably.
// Real results
How a logistics firm cut mis-sorts by 73%
A Québec-based third-party logistics provider was losing over $2 million annually to package mis-routing. We deployed a computer vision system that reads damaged, rotated, and partially obscured labels in real time, feeding corrections directly to the conveyor PLC.
The model runs on edge GPUs at each sorting hub, processes 1,200 packages per minute, and paid for itself within four months of deployment. Our AI software continues to learn from new label formats as the client onboards new retail partners.
73%
Fewer mis-sorts
4 mo.
To positive ROI
1,200
Packages / min
99.4%
Label read accuracy
// Questions we hear often
Straight answers, no jargon
Do we need a large dataset before starting?
Not necessarily. We often begin with as few as a thousand labelled examples and use transfer learning or synthetic data augmentation to bootstrap performance. During the diagnostic phase we assess data readiness and recommend the most cost-effective path to a training-ready corpus.
How long does a typical engagement last?
A proof of concept usually takes three to four weeks. Full production deployment ranges from eight to sixteen weeks depending on integration complexity. We scope every project with a fixed timeline and clear milestones so there are no open-ended invoices.
Can you work with our existing cloud provider?
Absolutely. We deploy on AWS, GCP, Azure, and OVHcloud. If you have on-premise requirements for data sovereignty or latency reasons, we support bare-metal and hybrid configurations as well.
What happens if the model underperforms after launch?
Every deployment includes automated monitoring. When accuracy dips below an agreed threshold, our pipeline triggers a retraining run using fresh data. We also conduct quarterly model reviews to evaluate whether architectural changes could yield further gains.
Is our data safe with you?
We sign a mutual NDA and data processing agreement before any data transfer. All data is encrypted at rest and in transit, and we operate under Canadian privacy law (PIPEDA) as well as Québec's Law 25. We never use client data to train models for other clients.
// Let's talk
Tell us about the problem — we'll tell you if AI is the right answer
We are honest about scope. If a rules engine or a simple SQL query solves your problem, we will say so. But when the data is complex and the stakes are high, our AI software earns its keep.
Visit us
7030 Vince Loaf, H2X 1Y4 Montréal, Quebec, Canada
Call
+1 514 938-5931
Email
[email protected]