Demand Forecasting
Predict future demand at SKU, location, and channel level using historical sales, seasonality, promotions, and external signals. Reduce inventory costs while maintaining service levels.
AI & MACHINE LEARNING
Predictive Analytics That Drive Revenue
We build production-grade ML models for demand forecasting, churn prediction, fraud detection, and price optimization — turning your historical data into competitive advantage with measurable business impact.
50+
Predictive Models Built
89%
Avg Forecast Accuracy
$30M+
Revenue Impact Generated
Fixed scope, fixed price
A number before we write code
Your IP from day one
Full ownership on final payment
Senior engineers only
No juniors on your budget
Reply within 1 working day
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Our Capabilities
Churn Prediction
Identify customers at risk of leaving before they do. We build behavioral churn models that score your entire customer base daily, enabling targeted retention campaigns with measurable ROI.
Fraud Detection
Real-time fraud scoring for transactions, account creation, and insurance claims. Our models learn from your fraud patterns and adapt to new attack vectors continuously.
Price Optimisation
Dynamic pricing models that maximize revenue and margin by predicting price elasticity, competitor moves, and demand sensitivity. Used in e-commerce, SaaS, travel, and retail.
Recommendation Engines
Collaborative and content-based filtering systems that drive product discovery, upsell, and cross-sell. Personalize experiences across email, web, and app touchpoints at scale.
Time-Series Forecasting
Advanced forecasting for any time-indexed metric — energy consumption, website traffic, sales pipelines, financial markets. We handle seasonality, trend, and external regressors.
How we work
Fixed milestones, working software every fortnight, and no surprises on the invoice.
- 01
Data Audit
We assess the quality, completeness, and history of your data sources, identify gaps, and define a data strategy — including what additional data collection or enrichment will improve model performance.
- 02
Feature Engineering
The most impactful phase. We transform raw data into predictive signals — lag features, rolling aggregates, external enrichment (weather, economics), and domain-specific derived metrics.
- 03
Model Training
We train, validate, and compare multiple model families (XGBoost, LightGBM, neural networks, Prophet) using rigorous cross-validation to select the most accurate and stable approach for your data.
- 04
Business Integration
We deploy models to production with APIs, integrate outputs into your dashboards and workflows, set up automated retraining schedules, and configure drift monitoring to maintain accuracy over time.
Work we have shipped
Accountable partners, not outsourced vendors
How we price, who we staff and what happens after launch — the parts that decide whether a project actually lands.
Fixed scope, fixed price
You know the cost and the timeline before a line of code is written. No hourly billing, no scope-creep surprises.
You own everything
Full source code, credentials and intellectual property transfer to you on final payment. We sign the IP assignment on day one.
Senior engineers only
Every project is staffed by engineers with years of production experience — no juniors learning on your budget.
Canadian data residency
Built for PIPEDA and provincial privacy law, with data kept on Canadian infrastructure where it matters.
8-week MVP programme
A focused, fixed-price path from idea to a launched product, refined across dozens of startup engagements.
Support after launch
Tiered SLAs from monitoring and bug fixes to a dedicated team with 24/7 response. Most clients stay for years.
Recognition
Independent awards and partner tiers, each one earned on delivered work.
Top Generative AI Company
Top App Development Company
Webby Honoree
Best App Award, Personal Growth
Advanced Tier Services Partner
Top 100 North America
Compliance
Regulation is a design constraint here, accounted for from the first architectural decision rather than audited in at the end.
Privacy
Personal data handled to the letter of Canadian and international law.
PIPEDAGDPRHIPAACCPA
Security
Threat modelling, least-privilege access and audited controls.
SOC 2 Type IIISO 27001Zero trust
AI governance
Model evaluation, human oversight and an audit trail.
ISO 42001AIDA-readyModel cards
Sector
The regulations your industry answers to, designed in from day one.
PCI DSSHL7 / FHIROSFI
Cloud
Regional residency, encryption in transit and at rest, tenant isolation.
Canadian data residencyAWS Well-Architected
Accessibility
Built and tested so the people who need it can actually use it.
WCAG 2.2 AAAODA
Common questions
If your question is not here, a senior engineer will answer it within one working day.
Ask our teamFor most use cases, 1–2 years of historical data is a solid starting point. Demand forecasting with strong seasonality benefits from 2–3 years to capture seasonal cycles. Fraud detection can work with as little as 6 months if fraud events are frequent enough. We assess your data during the audit phase and recommend strategies to work effectively with what you have.
Accuracy varies by problem type and data quality. Our demand forecasting models typically achieve 80–92% accuracy (MAPE basis). Churn models typically achieve 75–90% AUC. No model is 100% accurate — the goal is to be significantly better than current heuristics or manual judgment. We always benchmark against your existing approach and report results honestly before deployment.
Model drift is real, and we design for it from day one. We implement automated monitoring that tracks prediction accuracy over time and alerts when drift is detected. Retraining pipelines are scheduled (weekly or monthly) to incorporate new data. For high-stakes models, we implement champion-challenger frameworks where new model versions are validated before replacing the incumbent.
Rules-based systems are better when your logic is well-understood, rarely changes, and needs to be fully explainable (e.g., regulatory compliance). ML models win when patterns are complex, data volume is high, conditions change frequently, or accuracy gains are commercially significant. In practice, many production systems combine both — ML for prediction, rules for hard constraints.
We build explainability into every model using SHAP values, which show exactly which features drove each prediction. For regulated industries (finance, insurance, healthcare), we choose inherently interpretable models (logistic regression, decision trees) when accuracy trade-offs are acceptable. We also build business-facing dashboards that translate model outputs into plain-language explanations for non-technical audiences.
Let’s build something worth keeping.
Tell us what you are trying to build. A senior engineer will come back within one working day with a scope, a timeline and a fixed price.