Predictive analytics
Forecast demand, churn, equipment failure, or revenue with models trained on your historical records. We integrate directly with your BI dashboards so predictions appear where your team already looks.
Your operations deserve AI software that actually ships results
We design, train, and deploy machine-learning systems tailored to your data, your workflows, and the real problems your team faces every quarter. No black-box magic — just measurable improvement.
Request your diagnostic session
From raw data to running system in four steps
Most AI projects stall because scope is unclear. We follow a tight, repeatable process that keeps timelines short and results visible.
Diagnostic audit
We spend two days inside your existing data pipelines, CRM exports, and spreadsheets. The goal is not to impress you with jargon but to find the three or four places where a trained model can remove bottlenecks. You receive a written brief with expected impact ranges before any contract is signed.
Data preparation and feature engineering
Cleaning, normalising, and structuring your data is where most value is created — and most vendors cut corners. Our engineers build reproducible pipelines so the model can be retrained whenever your data changes, without starting from scratch.
Model training and validation
We select the architecture that fits the problem — sometimes a gradient-boosted tree outperforms a neural network, and we are honest about that. Every model is validated against a hold-out set and stress-tested with adversarial inputs before it touches production.
Deployment, monitoring, and iteration
The model ships behind an API your existing tools can call. We set up drift-detection dashboards so you know the moment accuracy begins to slip, and we schedule quarterly retraining cycles to keep performance sharp over time.
What we build
Each engagement is custom, but these are the capability families we draw from most often.
Document intelligence
Extract structured fields from invoices, contracts, or medical forms with high accuracy. Our OCR-plus-NLP pipeline handles multi-language documents and learns from corrections your staff make over time.
Recommendation engines
Surface the right product, article, or next step for each user. We combine collaborative filtering with content-based signals and respect privacy constraints so you stay compliant with Canadian data law.
Computer vision
Detect defects on a production line, count inventory from shelf photos, or verify identity documents. We optimise models for edge deployment when latency matters, and for cloud when scale matters.
Internal knowledge assistants
Build a retrieval-augmented chatbot grounded in your internal documentation. Employees get accurate answers sourced from your own policies, manuals, and wikis — with citations, not hallucinations.
Anomaly detection
Flag fraudulent transactions, unusual sensor readings, or suspicious access patterns in real time. Our models learn the baseline of "normal" from your own data and alert only when something genuinely deviates.
What it looks like in practice
A logistics company in Montréal asked us to reduce manual freight classification. Here is what happened.
"Within eight weeks of deployment, the model was classifying 94 percent of inbound shipments correctly — a task that previously required three full-time staff to handle manually."
— Operations director, mid-size 3PL provider, Montréal QC
The remaining six percent of edge cases are routed to a human reviewer whose corrections feed back into the model each week. Total processing time dropped from 14 hours per day to under 90 minutes, and error rates fell by 71 percent in the first quarter.
Common questions
We hear these regularly during first conversations. If yours is not listed, reach out below.
How long does a typical engagement last?
Most projects move from diagnostic to first production deployment in 8 to 14 weeks. Simpler automation tasks like document extraction can ship in as few as four weeks, while multi-model systems with real-time inference may take closer to five months. We agree on milestones before work begins so both sides know exactly what to expect at each stage.
Do we need to share sensitive data?
We work under strict NDAs and can operate within your own cloud environment so data never leaves your infrastructure. Where possible, we use anonymised or synthetic data during the exploration phase. All our processes comply with PIPEDA and Quebec's Act 25 privacy requirements.
What if our data is messy or incomplete?
That is the norm, not the exception. Our diagnostic audit specifically evaluates data quality and identifies gaps. We build cleaning pipelines as part of every project and can advise on collection strategies to improve coverage over time. A model trained on imperfect but representative data almost always outperforms waiting for perfect data that never arrives.
How is pricing structured?
We offer fixed-scope project pricing after the diagnostic phase so there are no surprise invoices. Ongoing monitoring and retraining are billed as a monthly retainer, typically a fraction of the original build cost. The diagnostic itself is a flat-fee engagement you can walk away from with the written brief in hand.
Can you integrate with tools we already use?
Yes. Our deliverables are API-first, meaning they slot into Salesforce, SAP, Shopify, custom ERPs, or any system that can make HTTP calls. We also provide webhook connectors and pre-built integrations for popular platforms so your developers spend minimal time on glue code.
Start with a conversation
Tell us what you are trying to improve and we will respond within one business day with an honest assessment of whether AI is the right tool for the job.
Other ways to reach us
Phone: +1 418 678-9740
Email: [email protected]
Office: 846 Ruben Greens, G1R 2L3 Québec, Quebec, Canada
