Since 2019, our AI software has been deployed across four industries and three countries. Below are the stories behind those deployments — the problems, the process, and the measurable outcomes our clients experienced.
Proof first
AI software that changed how these companies operate
We believe proof should come before promises. Each narrative below follows a real engagement — from the initial challenge through deployment to the numbers that followed. Names and details are shared with permission.
Borealis Freight: cutting route planning from hours to minutes
Borealis Freight managed a fleet of 120 trucks across eastern Canada. Their dispatchers spent an average of three and a half hours each morning building routes manually. We deployed a reinforcement-learning engine that ingests live traffic feeds, weather data, and delivery-window constraints to produce optimised route sets in under four minutes. The model re-plans dynamically when a driver reports a delay, which reduced late deliveries by a significant margin within the first quarter.
"We thought AI software was only for tech giants. Value AI Engines proved us wrong in the first sprint."
— Danielle Tremblay, VP Operations, Borealis FreightClinique Laurier: predicting patient no-shows before they happen
Clinique Laurier, a multi-site physiotherapy network, was losing revenue to a no-show rate that hovered near 18%. Our team built a gradient-boosted classifier trained on anonymised appointment history, weather patterns, and commute distances. The model flags high-risk appointments 48 hours in advance so staff can send targeted reminders or offer rescheduling. Within five months the no-show rate dropped to under 7%, freeing up appointment slots that translated directly into additional patient visits each week.
Maison Verte: demand forecasting for perishable inventory
Maison Verte operates 14 organic grocery stores. Spoilage of fresh produce was costing them roughly $45,000 per month across all locations. We designed a time-series forecasting pipeline using a transformer-based architecture fine-tuned on three years of point-of-sale data, local event calendars, and seasonal trends. Store managers now receive daily order recommendations per SKU, and the system automatically adjusts when it detects anomalies like a sudden heatwave or a nearby festival.
"The model paid for itself in the first two months. Our produce managers trust the daily recommendations completely now."
— Marc-Antoine Gagnon, co-founder, Maison VerteCapability map
What our AI software actually does
We do not sell vague "AI transformation." Below is the concrete set of capabilities we deploy, each backed by the case studies above and others we cannot share publicly.
Predictive analytics
Demand forecasting, churn prediction, maintenance scheduling, and risk scoring models trained on your historical data with explainable outputs.
Natural language processing
Document classification, entity extraction, sentiment analysis, and conversational agents tuned to your domain vocabulary and compliance requirements.
Computer vision
Defect detection on production lines, medical image triage, inventory counting via shelf imagery, and document digitisation pipelines.
Decision automation
Rule-engine hybrids that combine ML confidence scores with business logic so humans stay in the loop on edge cases while routine decisions flow automatically.
Data engineering
We clean, unify, and pipeline your data before any model is trained. Without solid data foundations, even the best algorithm will underperform.
Model ops and retraining
Continuous monitoring, drift detection, and automated retraining loops so your models stay accurate as your business and data evolve over time.
How an engagement unfolds
From first call to production — typically 10 to 16 weeks
Discovery
We audit your data landscape and map business pain points to AI-solvable problems. This takes one to two weeks.
Proof of value
A lightweight prototype on a subset of your data, delivered in three weeks, so you can validate the approach before committing further.
Build and iterate
Full model development with weekly demos. We integrate into your existing systems and iterate based on stakeholder feedback.
Deploy and monitor
Production deployment with monitoring dashboards, alerting, and a 90-day stabilisation window where we tune performance.
Handover
Documentation, training sessions for your team, and optional ongoing advisory retainer. You own everything.
Decision board
Which engagement model fits your situation?
Not every organisation needs the same depth of involvement. Use this matrix to identify where you fall, then reach out and we will confirm the right path together.
| Criteria | Exploration | Accelerator | Enterprise |
|---|---|---|---|
| Data maturity | Scattered, mostly spreadsheets | Centralised but underused | Data warehouse in place |
| Timeline | 3–5 weeks | 8–12 weeks | 14–20 weeks |
| Deliverable | Feasibility report + prototype | Production-ready model | Multi-model system with ops |
| Team involvement | 1 stakeholder | Cross-functional squad | Executive sponsor + squad |
| Ongoing support | Optional consult | 90-day stabilisation | 12-month retainer |
| Ideal for | Testing the waters | Solving one defined problem | Org-wide AI adoption |
Most clients start with Accelerator and expand from there.
Fidelitas Credit Union: fraud detection that learns in real time
Fidelitas was relying on a rules-based fraud detection system that generated hundreds of false positives daily, exhausting their investigation team. We deployed an ensemble model combining isolation forests and a neural network that processes transactions in under 200 milliseconds. The system learns from investigator decisions in a feedback loop, continuously improving its precision. False positives dropped by 64% in the first quarter while catching two fraud rings that the old system had missed entirely.
What made this project distinctive was the regulatory constraint. All model decisions needed to be explainable to auditors under OSFI guidelines. We built a SHAP-based explanation layer that generates plain-language rationales for every flagged transaction, which the compliance team can attach directly to their audit trail.
"For the first time, our investigators spend their energy on real threats instead of chasing ghosts. The explainability layer was the feature that convinced our board."
— Renaud Pelletier, Chief Risk Officer, Fidelitas Credit UnionWorking principles
What we commit to on every engagement
You own the models
All source code, trained weights, and documentation are yours. We never lock you into proprietary platforms or recurring licence fees for the models we build.
Explainability by default
Every model ships with an interpretability layer. If a stakeholder asks "why did it decide that?" there is always an answer available in plain language.
Data stays on your premises
We work within your security perimeter. If cloud resources are needed, they run under your accounts with your encryption keys.
No vanity metrics
We measure success by business impact — revenue recovered, cost avoided, time saved — not by model accuracy percentages that mean nothing to your board.
Start a conversation
Tell us what you are trying to solve
Describe your challenge in a few sentences. We will respond within one business day with an honest assessment of whether AI software is the right tool — and if it is, what a first engagement could look like.
Prefer a direct conversation? Call +1 819 984-1919 or email [email protected]
8659 Gutkowski Expressway, J1H 1A1 Sherbrooke, Quebec, Canada