FAQ
Candid answers about how our Toronto data studio works, what things cost, and what we refuse to promise.
No to all three. We are an applied data & AI studio that builds machine-learning models, analytics dashboards and LLM apps on your own data for client organizations — not a course, not a healthcare or heart-rate product (Pulse means a live signal from your data and systems, not a medical reading), and we never sell or broker personal data. We keep humans in the loop and do not guarantee accuracy, predictions or ROI.
Both. Fixed-scope projects suit a defined build: a data audit, a proof of concept, or a production deployment with a clear deliverable list. Retainers suit ongoing model evaluation, MLOps support, roadmap work and iterative improvements. We agree project scope, cadence and CAD project fees upfront. Discovery sprints often convert to either model once we understand your data.
Data audits: C$6,000–C$14,000. Proof-of-concept builds: C$25,000–C$55,000. Production deployments: C$50,000–C$175,000 depending on data complexity and integrations. Retainers from C$5,500/month. We quote in Canadian dollars for Canadian businesses. These are ranges, not quotes — your actual project scope drives the number.
A data audit usually takes one to two weeks. Proof of concept: four to eight weeks. Production deployment with monitoring: eight to sixteen weeks. Timelines depend on data access, stakeholder availability and integration complexity. We will tell you if your deadline is unrealistic rather than miss it quietly.
We are pragmatic, not tribal. We work with major cloud platforms, open-source ML frameworks, and enterprise LLM APIs where retrieval-augmented generation (RAG) is appropriate. On-prem and hybrid deployments are available when data privacy policy requires it. Tool choices follow your constraints — security, latency, cost and maintainability — not our preferences.
Weekly standup — scope and timelines reviewed openly.
We follow PIPEDA-compliant practices for personal information and align with your internal security policies. Client data stays in agreed environments; we do not train models on your data for other clients. Access is logged, NDAs are standard, and we document retention and deletion procedures. See our privacy policy for details.
Custom code and models built under a fixed-scope agreement are assigned to you on payment, unless we agree otherwise in writing. We retain rights to pre-existing tools, libraries and general know-how. Third-party model licences pass through per vendor terms. We clarify IP in every statement of work before work begins.
Not perfectly accurate — no honest ML system is. We document expected error bands, run model evaluation before release, and design human-in-the-loop review for high-stakes decisions. Guardrails and monitoring catch drift after launch. We do not guarantee accuracy, predictions or ROI.
We do not sell data, run AI-income schemes, offer courses or accredited training, build crypto or AI-trading bots, claim AGI or sentient AI, replace your team, promise zero errors, or provide legal, financial, investment or medical advice. We do not fabricate client logos or guarantee specific business results.
AI & results disclaimer: Our studio provides AI and data design, development and consulting services, including strategy, machine-learning and generative-AI applications, analytics, data engineering, AI assistants, workflow automation and monitoring for client organizations. AI systems can produce errors, biased or inaccurate outputs and require human review and oversight; we design for humans-in-the-loop. We do not guarantee specific business results, predictions, cost savings, revenue, accuracy or return on investment. Outcomes depend on data quality, scope, budget and adoption. Any case studies or metrics shown reflect past client work and are not a promise of future performance. This is a professional AI services firm, not legal, financial, investment or medical advice, and not a course or income opportunity.