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AI implementation: from scoped pilot to a system your team runs.

We find the right projects, pick or build the right tools, integrate them with what you already run, and leave your people able to operate the result.

Vozwin AI's implementation service takes an AI use case from scoped pilot to a production system your own team operates: pilot design against a measured baseline, build-versus-buy and vendor selection, data pipelines, architecture, integration with the ERP, MES, CRM or fleet systems you already run, and the documentation, runbooks and training that keep it working after we leave. We deliver for companies across Canada, on cloud, on premises or air-gapped, in English or French.

Pilot success is a decision changed, not a dashboard built. Every engagement starts by writing down what the system has to change in the business, and ends when that change is measured on equipment, data or cases the model never saw during development.

Finding the right projects

If you arrive with a roadmap, we start there. If you arrive with an idea, we start by mapping the actual opportunity: the decision the system would change, the metric it would move, the data that already exists to support it, and the people who would have to act on its output. Candidates are prioritized by value and tested for feasibility before anyone writes code. Then we run smart pilots: narrow, measured against a baseline, and decided in advance.

Picking vendors, or not

Most use cases are better served by configuring a proven product than by custom development, and we say so even though we build. When buying is the right call we review the vendor landscape, test and compare the shortlisted products on your data, run the RFP if you need one, and write the build-versus-buy analysis in terms your finance team can audit. When the product does not exist, or the data advantage is yours to keep, we build.

Getting it built

  • End-to-end delivery: data pipeline, model or product configuration, user-facing workflow and monitoring, with one accountable team.
  • Architecture that fits your constraints: cloud, on premises or air-gapped, with the security and privacy posture your industry requires.
  • Integration with existing systems. The ERP, MES, CRM, maintenance or fleet software you run today is where the output has to land, and most of the engineering effort goes there.
  • Performance tuned to the decision. Accuracy is measured on held-out data, latency against the workflow, and cost against the value the roadmap promised.

How a pilot runs

  1. Pick one component, case type or workflow, and one decision it changes.
  2. Write down the baseline: how that decision is made today, how often it goes wrong, and what each outcome costs. Without a baseline, any result looks like success.
  3. Collect and condition the data. This step usually takes longer than the modelling, and it is where most of the value in the data is unlocked.
  4. Hold out data the model never sees. Results on the examples a model trained on tell you nothing about the next one.
  5. Run in shadow mode alongside the current process, and compare what the system would have done with what actually happened.
  6. Decide on the result you agreed to in step one. If the pilot would have changed the right decisions, it moves to production on part of the operation.

Built on shipped research

Vozwin funded and owns peer-reviewed research, carried out through its Labs division with McGill University and Université de Sherbrooke and supported by the Mitacs Accelerate program, that predicts drone battery state of health within 2.26 percent from flight logs alone, on a battery the model had never seen, with no added sensors. That work became Vermilion, a production platform incubated in Vozwin's venture studio and now operating as its own company. Vozwin AI is the part of the group that takes an operation from interest to a working pilot and from pilot to production, on drones and on any other equipment or process that logs its own operation.

Capability transfer, written into the contract

The system is not done when it works. It is done when your team can run it through a bad week without calling us. Every implementation includes:

  • Documentation that explains decisions, not just code: why this architecture, why this model, what was tried and rejected.
  • Training sessions with your actual team on your actual systems, scheduled during the engagement rather than promised after it.
  • Runbooks for operations: what to monitor, what alerts mean, how to retrain or roll back, and who decides when the model drifts.
  • Hiring support when the roadmap calls for in-house roles: job descriptions, technical screening and participation in final interviews.

Cost and funding

Published market ranges for Canadian AI pilots run from $25,000 to $100,000 CAD over six to twelve weeks, and full production implementations from $100,000 to $500,000 and up; our pricing guide explains what moves those numbers. Implementation work is where Canadian funding does the most: SR&ED tax credits on eligible development, NRC IRAP for small and mid-sized companies, Mitacs for university partnerships, and Scale AI for supply-chain projects. We scope engagements with those programs in mind and tell you when one does not apply.

Questions

Questions? We've got answers.

How long does an AI pilot take?

Six to twelve weeks is the typical published range, and it holds for most of our work. A pilot on clean, accessible data lands at the short end. One that needs data engineering first takes longer, and we tell you that before it starts.

Do you build custom systems or configure vendor products?

Both, and we recommend the one the use case needs. For most SMB use cases a proven product configured well beats custom development. We build when the product does not exist or when the data advantage is one you should own.

Can you work with our existing IT team and systems?

Yes, and the engagement goes better when we do. Integration with the systems you already run is where most of the engineering goes, and your IT team knows those systems. We work inside your security and change-management requirements, including on-premises and air-gapped environments.

What happens after go-live?

Your team runs it. We hand over documentation, runbooks and trained operators during the engagement, not after it. If you want ongoing support, retraining or monitoring, that is a separate, optional retainer.

Can you help us hire the people to run it?

Yes. When the roadmap calls for in-house roles, we write the job descriptions, screen technical candidates and sit in on final interviews, so the first hire is the right one.

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Have a use case and the data to match?

Tell us what you record today and what decision you want to change. We'll tell you whether a pilot would prove it, what it would take, and what we'd measure.