Frequently asked questions about our AI software

We specialise in four core areas: predictive analytics platforms that forecast demand, revenue, or churn; natural language processing solutions such as chatbots, document classifiers, and sentiment engines; computer vision systems for quality inspection, surveillance, and medical imaging; and workflow automation bots that integrate with your existing ERP, CRM, or helpdesk tools. Each solution is custom-built — we do not resell off-the-shelf products.

We work with organisations of every size. For smaller companies, we offer scaled engagement models — such as a focused four-week sprint that delivers a single high-impact model — so you can experience the value of AI software without committing to a six-figure contract. Many of our longest-running relationships started with a small proof-of-concept that grew as the client saw results.

Our team has delivered projects in finance, healthcare, logistics, manufacturing, retail, insurance, and energy. That said, our process is industry-agnostic at its core — the discovery workshop is specifically designed to immerse our engineers in your domain so they can build models that reflect your operational reality, regardless of sector.

Most engagements run between 8 and 16 weeks from kick-off to production deployment. The timeline depends on three main factors: the complexity of the use case, the readiness and quality of your data, and the depth of integration required with existing systems. During the discovery workshop we provide a detailed timeline with milestones, and we track progress against it in bi-weekly demos.

Yes — high-quality, domain-specific data is the fuel for any AI model. However, you do not need to have a perfectly clean dataset before we start. Our data-audit phase identifies gaps and quality issues, and we build automated pipelines to clean, transform, and augment your data. In some cases we can supplement with synthetic data or publicly available datasets, but the core training signal should come from your operations.

We support both deployment models. Many clients in regulated industries (healthcare, finance, defence) require on-premise or private-cloud hosting for compliance reasons, and we have extensive experience configuring Kubernetes clusters, GPU nodes, and monitoring stacks in air-gapped environments. For clients without such constraints, we typically deploy on AWS, GCP, or Azure using infrastructure-as-code for reproducibility.

Data security is non-negotiable. All data in transit is encrypted via TLS 1.3, and data at rest is encrypted with AES-256. We enforce role-based access controls, maintain audit logs, and sign NDAs before any data is shared. Our processes align with PIPEDA and, where applicable, GDPR and HIPAA. We never use client data to train models for other clients, and we can contractually guarantee data residency within Canada or any other jurisdiction you require.

Our primary stack is Python (PyTorch, scikit-learn, Hugging Face Transformers) for model development, with Go or Rust for performance-critical inference services. We use Apache Airflow or Prefect for orchestration, MLflow for experiment tracking, and Docker plus Kubernetes for containerised deployment. On the front-end side, dashboards are typically built in React or Vue, depending on client preference. We are framework-agnostic where it matters — the right tool for the job is always our guiding principle.

We offer three pricing models: fixed-price for well-scoped projects with clear deliverables, time-and-materials for exploratory or research-heavy engagements, and retainer-based for ongoing model management and support. Every engagement begins with a free discovery call, after which we provide a detailed proposal with transparent cost breakdowns. There are no hidden fees — infrastructure costs, third-party API charges, and licensing are all itemised upfront.

Our smallest standard engagement is a four-week focused sprint, typically priced between CAD 15,000 and CAD 30,000 depending on complexity. This is enough to deliver a validated proof-of-concept with measurable results. For larger programmes that span multiple models or departments, we structure phased roadmaps with independent budgets per phase so you can evaluate ROI before committing to the next stage.

In select cases, yes. If the success metric is clearly quantifiable — for example, reduction in defect rate or increase in conversion — we can structure a portion of the fee as a performance bonus tied to achieving agreed targets. This aligns our incentives directly with your business outcomes. We discuss eligibility during the discovery phase.

We offer three tiers of post-deployment support. The basic tier includes 90 days of bug-fix coverage and documentation handover. The standard tier adds automated drift monitoring, quarterly retraining, and a dedicated support channel. The premium tier provides 24/7 on-call support, monthly performance reviews, and proactive optimisation recommendations. Most clients choose the standard or premium tier to ensure their AI software stays performant as data and business conditions evolve.

Absolutely. Knowledge transfer is a core part of every engagement. We conduct hands-on training sessions covering model architecture, retraining procedures, monitoring dashboards, and troubleshooting common issues. We also provide written runbooks and recorded walkthroughs. Our goal is to make your team self-sufficient — not to create a permanent dependency on our consultants.

The easiest way is to fill out the contact form on our home page or call us at +1 418 669-1623. We will schedule a free 30-minute discovery call to understand your goals, assess feasibility, and outline next steps. There is no obligation — if AI is not the right fit for your problem, we will tell you honestly and suggest alternatives.