Predictive analytics
Demand forecasting, churn prediction, and lead scoring models trained on your historical data. We specialise in time-series methods that handle seasonality and sparse signals well.
We build AI software that actually ships
Most AI projects stall at the proof-of-concept stage. Ours reach production — integrated into your operations, monitored, and continuously improving. Based in Laval, Quebec, we partner with mid-market companies across Canada to turn raw data into working intelligence.
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How an engagement unfolds
We follow a four-phase approach that keeps scope tight, timelines honest, and outcomes measurable from week one.
1. Discovery audit
We spend one to two weeks inside your data landscape. We interview stakeholders, catalogue existing pipelines, and identify the single highest-leverage problem to solve first. No generic roadmaps — just a prioritised backlog with expected ROI estimates for each item.
2. Rapid prototyping
Within four weeks we deliver a functional prototype connected to real data. You can interact with predictions, review accuracy metrics, and stress-test edge cases. If the model does not meet the agreed performance threshold, we pivot before any production investment.
3. Production deployment
We containerise the solution, wire it into your existing stack through REST or event-driven APIs, and set up automated retraining schedules. Every deployment includes a monitoring layer that tracks data drift, latency, and prediction quality around the clock.
4. Ongoing optimisation
Models degrade over time as the world changes. We provide quarterly model health reviews, feature refreshes, and A/B testing of challenger models so your AI software keeps delivering value long after launch.
What we build
Six core capability areas — each backed by production experience, not just research papers.
Computer vision
Quality inspection, document digitisation, and video analytics pipelines. We deploy models on edge hardware when latency or bandwidth constraints demand it.
Natural language processing
Sentiment analysis, entity extraction, and conversational agents fine-tuned on domain-specific corpora. We integrate with large language models while keeping sensitive data on-premise.
Intelligent automation
End-to-end workflow automation combining RPA with ML decision points. We target repetitive, high-volume tasks where even a small accuracy gain compounds into significant savings.
Data engineering
Before models can learn, data needs to be clean, unified, and accessible. We design lakehouse architectures, build ETL pipelines, and implement governance frameworks that scale with your organisation.
MLOps and governance
Model registries, experiment tracking, automated retraining, and bias auditing. We help regulated industries satisfy compliance requirements without slowing down iteration speed.
Measurable outcomes
Selected engagements where our AI software moved the needle — names anonymised per client agreements.
Supply-chain forecasting for a national distributor
Replaced spreadsheet-based planning with a gradient-boosted ensemble model that ingests weather, holiday, and promotional signals. Forecast accuracy improved from 68 % to 94 %, reducing overstock write-offs by $2.1 M annually.
94 % forecast accuracy
Defect detection for an automotive parts manufacturer
Deployed a convolutional neural network on edge GPUs at three inspection stations. The system identifies surface defects in under 45 ms per part, catching 99.3 % of flaws that previously reached customers as warranty claims.
99.3 % defect recall
Frequently asked questions
Honest answers to the things prospective clients usually want to know before signing.
How long does a typical project take?
Most engagements move from discovery to a production-ready first release in 10 to 16 weeks. Complex multi-model systems with heavy data engineering prerequisites can stretch to six months. We scope conservatively and prefer delivering early over padding timelines.
Do we need to share our data with you?
We can work within your infrastructure. Many of our clients in healthcare and finance require that data never leaves their VPC. We set up secure development environments inside your cloud tenant and operate under strict NDAs and data-processing agreements.
What if the model does not perform well enough?
We define success metrics before any code is written. If the prototype does not hit the agreed threshold during the rapid-prototyping phase, we either pivot the approach at no extra cost or recommend that the problem is not yet solvable with available data — saving you from a sunk-cost deployment.
How is pricing structured?
We offer fixed-price project engagements and monthly retainer packages for ongoing optimisation. Discovery audits are billed separately at a flat rate so both sides can evaluate fit before committing to a larger scope. We do not charge by the hour for development work.
Which industries do you serve?
Our strongest track record is in manufacturing, logistics, financial services, and healthcare. That said, the underlying techniques — classification, regression, anomaly detection, NLP — transfer well. If your problem involves structured data and a clear business metric, we can likely help.
Start a conversation
Tell us about the problem you are trying to solve. We will respond within one business day with an honest assessment of whether AI is the right tool.