Since 2019 — Laval, Quebec
Most machine learning projects fail not because the math is wrong, but because nobody asked the right questions first. We start with your problem, not our algorithms. The result is AI software that your team actually understands, uses, and relies on every single day.
Start a conversation about your data
How we work
Every engagement follows a deliberate arc. We never jump straight to model training — because the most expensive mistake in AI software is solving the wrong problem with the right algorithm.
We spend the first two weeks understanding your business context, interviewing stakeholders, and auditing your existing data infrastructure. This phase surfaces hidden assumptions and data quality issues before a single line of model code is written. We document every data source, its lineage, refresh frequency, and known gaps.
Using a subset of your real data, we build a minimum viable model and test it against your actual decision-making workflow. We present results in plain language — confusion matrices become business impact tables. If the prototype does not demonstrate clear value, we pause and recalibrate the approach rather than pushing forward blindly.
Validated models get hardened for production: containerized deployments, automated retraining pipelines, monitoring dashboards, and graceful fallback logic. We integrate with your existing tech stack — whether that is a legacy ERP system, a cloud data warehouse, or a mix of both. Every deployment includes comprehensive documentation your ops team can actually follow.
Data drifts. Business rules change. We provide ongoing model monitoring and quarterly performance reviews. When accuracy degrades, we retrain and redeploy — often before you even notice a problem. Our stewardship contracts are month-to-month because we believe retention should be earned, not locked in.
What we build
Demand forecasting, churn prediction, and anomaly detection models trained on your historical data. We favour interpretable approaches — gradient-boosted trees and linear models — unless the problem genuinely demands deep learning complexity.
Quality inspection on manufacturing lines, document digitization for insurance claims, and satellite imagery analysis for agriculture. We handle the full pipeline from image labelling strategy through edge deployment on embedded hardware.
Sentiment analysis for customer feedback, automated contract review, and bilingual (English/French) chatbot development. We fine-tune large language models on your domain-specific corpus so outputs reflect your terminology and compliance requirements.
Before models can learn, data must flow. We design and build ETL pipelines, feature stores, and data quality monitoring frameworks using tools like Apache Airflow, dbt, and Snowflake — tailored to your scale and budget.
Experiment tracking with MLflow, automated model registries, A/B testing infrastructure, and bias auditing dashboards. We help your team adopt responsible AI practices without slowing down iteration speed.
REST APIs, webhook-driven workflows, and embedded AI features inside your existing SaaS products. We write clean, tested code with proper versioning so your engineering team can maintain and extend what we build long after the engagement ends.
Who we serve
Patient readmission prediction, clinical trial matching, and medical imaging triage — all built within HIPAA and PIPEDA compliance frameworks from day one.
Predictive maintenance for CNC machines, route optimization for last-mile delivery, and real-time quality control using edge-deployed vision models.
Credit risk scoring, fraud detection pipelines, and regulatory document analysis. We understand the auditability requirements that come with deploying AI in regulated environments.
Personalized recommendation engines, dynamic pricing models, and inventory demand forecasting that accounts for seasonality, promotions, and external market signals.
Common questions
Discovery through production deployment usually takes eight to sixteen weeks, depending on data readiness and model complexity. Simple predictive models on clean, structured data can reach production in as little as six weeks. Projects involving computer vision or NLP with custom training data tend toward the longer end. We always provide a detailed timeline estimate after the discovery phase.
No. Many of our clients have no dedicated data science staff. We design systems that can be monitored and maintained by general software engineers or IT operations teams. If you plan to build an internal ML team eventually, we offer knowledge transfer sessions and pair-programming sprints to accelerate that transition.
Most real-world data is. Our discovery phase includes a thorough data quality assessment where we identify gaps, inconsistencies, and labelling issues. We then recommend pragmatic fixes — sometimes that means building a data collection pipeline before building a model. Honest assessment upfront saves significant rework later.
We sign NDAs before seeing any data. All work happens in your cloud environment or in isolated, encrypted infrastructure that we provision specifically for your project. We comply with PIPEDA, and we have experience working within HIPAA, SOC 2, and GDPR frameworks. Data never leaves the agreed-upon perimeter without explicit written consent.
We offer fixed-price discovery engagements starting at $8,000 CAD. Production projects are scoped and quoted after discovery, typically ranging from $25,000 to $150,000 depending on complexity. Ongoing stewardship is billed monthly. We do not do hourly billing — it creates misaligned incentives.
Reach out
99453 Turcotte Plain, H7A 0A1 Laval, Quebec, Canada
Phone: +1 450 537-7325
Email: [email protected]
We typically respond within one business day. If your inquiry involves sensitive data, mention that in your message and we will set up a secure channel.
