Questions clients ask before we write code

No on all counts. Data Nexus AI is a consulting studio, not an online course or certificate programme. We deliver projects—pipelines, models, RAG systems, MLOps tooling—alongside your staff. We do not guarantee that any model will achieve a specific accuracy score in production; data shifts, labelling noise, and business rules change outcomes. We document expected performance ranges, monitoring plans, and human review steps instead of promising perfection. We also do not aim to replace your team. Our goal is transfer: your engineers and analysts should be able to operate, extend, and challenge the systems we build together.

We serve retail, healthcare operations, SaaS, financial services, and professional services firms across Canada. Engagements must respect sector-specific privacy rules—PIPEDA federally, PHIPA in Ontario health contexts, and your internal policies. We decline work that asks us to bypass lawful access controls or misrepresent automated outputs as human professional judgment.

Both, when evaluation supports the choice. Retrieval-augmented generation is our default for internal knowledge use cases because it cites sources and respects document permissions. Fine-tuning is considered only after baseline RAG and prompt engineering fail to meet agreed metrics. We advise against fine-tuning small or stale corpora that would be better served by fixing search indexing first.

We collect only what is needed for the engagement, use it for stated purposes, and store it with access controls aligned to your environment. Privacy impact discussions happen during discovery when datasets include identifiable individuals. Data processing terms are covered in our statements of work and Privacy Policy. You remain the data controller for your customer records; we act as a service provider unless otherwise agreed in writing.

Typically a half-day to two-day review of your sources, infrastructure, team skills, and candidate use cases. You receive a written brief with recommended sequencing, rough CAD ranges, and risks—not a generic maturity scorecard. Many clients proceed to a fixed-scope pilot; others use the brief internally without continuing the engagement.

We work primarily in AWS, Azure, and GCP, plus Snowflake, BigQuery, Databricks, and common orchestration stacks. MLOps tooling includes MLflow, Kubeflow, and vendor-native services. We match tooling to your existing licences and hiring profile rather than imposing a pet stack.

Yes—that is the preferred model. We embed via shared repos, stand-ups, and paired implementation. Knowledge transfer sessions are scheduled throughout the project, not only at the end. We are comfortable reviewing internal notebooks constructively and helping prioritise what belongs in production versus research sandboxes.

Fixed-scope statements of work are quoted in Canadian dollars with milestone billing. Retainers cover ongoing MLOps and pipeline support. Indicative ranges for each service line appear on our Services page. Expenses such as cloud compute or third-party API usage are usually billed pass-through with prior approval.

Our registered office and primary studio are at 18 York Street, Suite 1400, Toronto, ON M5J 2T8, in the South Core. Remote delivery is standard; on-site sessions are available by appointment Monday–Friday, 09:00–18:00 Eastern Time.