Organisations we have delivered for
Four practices, one delivery spine
AI leads. Data makes it trustworthy. Managed services keep it running. Enterprise applications are where it has to integrate. Each stands on its own, and together they are why our AI work ships.
AI
Most AI pilots stall because nobody solved the boring part: where the data lives, who is allowed to see it, and what happens when the model is wrong. We start there — then ship.
Explore AIData & Analytics
Pipelines, platforms, governance and analytics — built to the standard AI workloads demand rather than the standard reporting tolerates.
Explore Data & AnalyticsManaged Services
Managed operation of the platforms and systems you depend on, with service levels written against outcomes — data freshness and model quality, not only uptime.
Explore Managed ServicesEnterprise Applications
ERP, custom applications, digital platforms and quality engineering. This is where our team started in 2008, and it is why our AI work lands in production.
Explore Enterprise ApplicationsAI Readiness Assessment
Three weeks. Fixed fee. Four deliverables you own.
- Data and systems inventory — where the data an AI system needs actually lives, and what governs it.
- Scored use-case shortlist — candidate projects ranked on value, feasibility, data readiness and risk, with business cases.
- Governance gap analysis — your current policies measured against what production AI requires.
- Costed roadmap — a phased plan with dependencies, decision gates and run-cost estimates, presented to your leadership.
Several assessments have concluded that the client should not build. That answer is also worth having in week three rather than month nine.
Built for organisations that answer to someone
A regulator, a ministry, an accreditation body, the public. Our clients cannot deploy what they cannot explain, and we design for that from the first session.
Public Sector & Government
Government work carries obligations most vendors treat as an afterthought: residency, accessibility, records, procurement and public explainability.
Healthcare
Health authorities, providers and payers — where the data is the most sensitive category there is and the operational pressure is constant.
Higher Education
Universities and colleges running complex student systems, federated governance, and rising expectations from students who use AI daily.
Financial Services
Banks, insurers and advisory firms operating under model risk expectations that predate the current wave of AI and apply to it regardless.
Retail & Commerce
Platform, forecasting, personalisation and operations work for retailers and consumer businesses — with a clear line on return.
Interior Health
Provincial health authority, British Columbia
Workiy was able to translate our requirements into a website that met our needs. The site has been well received by the organisation and we continue to work closely with them on enhancing it. They have been very responsive to our requests and patient as requirements change.Mark LierManager, Collaboration Systems, Interior Health Authority
Workiy rebuilt interiorhealth.ca on Drupal as a primary public service channel — an accessible content platform editorial teams operate without developer help, with ongoing enhancement under managed service. More project references
Five stages, no skipped steps
Every AI engagement follows this sequence. The steps most often skipped elsewhere — the data assessment and the production engineering — are the ones that decide whether anything ships.
Frame the decision, not the technology
We start with a business process that is expensive, slow or error-prone, and work backwards. If AI is not the cheapest fix, we will tell you that in week one.
Assess the data foundation
A structured review of source systems, data quality, lineage, access controls and residency constraints. This is where most AI programmes are actually won or lost.
Prove the value on a bounded scope
A working system against real data in a controlled environment, with success criteria agreed before we build, and a go / no-go decision at the end.
Engineer for production
Integration with systems of record, identity and access controls, evaluation harnesses, human-in-the-loop checkpoints, logging and cost controls.
Operate and improve
Monitoring for drift and regression, model and prompt versioning, cost optimisation, and a quarterly review against the outcomes we agreed.
What clients get that they do not get elsewhere
Canadian delivery with global depth
Headquartered in Vancouver with offices in Toronto, Chicago, Chennai and Doha. In-region delivery and data residency for Canadian and US clients, with follow-the-sun coverage for managed services.
We have implemented the systems your AI needs
PeopleSoft, Oracle, SQL Server, Drupal, Salesforce. Getting data out of these safely — and writing back to them safely — is where most AI programmes fail. It is where we started.
Practitioners screen our talent placements
Our staffing practice draws on our delivery consultants to technically assess candidates. A recruiter who has never run a pipeline cannot tell you whether the candidate has.
Governance treated as engineering
Controls enforced in the platform, not in a policy document. Audit evidence produced by the system as it runs. Frameworks mapped to NIST AI RMF, ISO/IEC 42001 and Canadian guidance.
Fixed-scope where it can be
Assessments and defined builds are priced as statements of work. Delivery risk sits with us. Open-ended work is called open-ended, and resourced honestly.
Willing to say no
Our assessments name the cheapest option for you, including buying a product or not building. Several have concluded exactly that.
Technology partners
Recent thinking
Why most AI pilots never reach production — and what the successful ones do differently
The gap between a working demo and a production system is rarely the model. It is integration, entitlements, evaluation and the question of who owns it on a Tuesday afternoon in eighteen months.
Read PerspectiveData residency for Canadian public sector AI: what is actually required
A practical read on where regulated data can and cannot go, which deployment patterns satisfy provincial requirements, and how to document it for a privacy impact assessment.
Read TechnicalPutting AI on top of an ERP without breaking the ERP
Extraction patterns, write-back safety, approval gates and why querying the transactional system directly is almost always the wrong answer.
ReadStart with three weeks and a straight answer
The AI Readiness Assessment is fixed in scope, fixed in price and produces four deliverables you own — whether or not you continue with us.