Frequently Asked Questions

Project timelines depend on scope and complexity, but most initiatives are completed within 8 to 16 weeks, including planning, development and testing phases.

Yes, we offer ongoing maintenance, updates and performance monitoring to ensure your AI solution remains reliable, accurate and adaptive over time.

Our team assesses your current infrastructure and designs integration layers that connect new AI models with workflows, databases and third-party tools seamlessly.

Timelines vary based on project complexity and data readiness. Initial discovery and requirement gathering can take 2–4 weeks, model design and prototyping 4–8 weeks, and integration and testing another 4–6 weeks. We work closely with your team to set clear milestones and adjust schedules as needed.

We follow industry best practices, including secure data storage, encrypted transmissions, and role-based access controls. Our processes align with applicable regulations in Canada and globally. Every dataset is handled according to agreed protocols to protect sensitive information.

Yes. We design modular AI components with standard APIs and microservices to fit into CRM, ERP, cloud applications or custom solutions. Integration plans include compatibility checks, performance monitoring, and smooth data interaction without disrupting your current workflows.

Our service includes ongoing monitoring, performance tuning, and version upgrades. We offer service-level agreements tailored to your needs, ensuring issues are addressed promptly and models stay accurate and efficient as business requirements evolve.

We work across manufacturing, retail, logistics, healthcare, and professional services. Our experience spans predictive maintenance systems for factories, recommendation engines for e-commerce, demand forecasting, and process automation in healthcare administration.

Success metrics are defined during the discovery phase and may include accuracy rates, processing speed improvements, error reduction, user adoption levels, and operational efficiency gains. We provide dashboards and regular reports so you can track progress against those metrics.

Our team includes data scientists, machine learning engineers, software architects, DevOps specialists, and UX designers. Each professional brings hands-on experience with Python, TensorFlow, PyTorch, cloud platforms like AWS and Azure, and agile methodologies for iterative delivery.

We follow a transparent change-management process. All change requests are documented, assessed for impact on timeline and resources, and presented in a revised project plan. You approve adjustments before work proceeds, ensuring alignment with budget and schedule constraints.