How we approach it
Workiy has spent seventeen years inside the systems institutions actually run on — Oracle and PeopleSoft ERP, student and patient records, claims and case management, finance and HR. That is where the data an AI system needs is locked up, and it is why so many promising pilots never leave the sandbox.
We build AI that reaches into those systems safely. Every engagement produces something that runs in production, with an audit trail, a named owner, an escalation path and a measurable result — not a demo.
What sits under AI
AI Strategy & Advisory
We turn a long list of AI ideas into a short, sequenced, costed plan — with the constraints of your sector built in from the start.
ExploreAI Readiness Assessment
A fixed-fee assessment with a fixed deliverable. You get a data inventory, a scored use-case shortlist, a governance gap analysis and a costed roadmap. No open-ended discovery.
ExploreAI Architecture & Platform Engineering
Model choice is the easy part. We design and build the platform layer — data access, identity, evaluation, observability, cost control — that everything else depends on.
ExploreCustom AI & Generative AI Development
We build the AI systems that do not exist off the shelf: document processing against your forms, classifiers trained on your taxonomy, copilots that read your systems of record.
ExploreAgentic AI & Automation
Multi-step automation where the interesting risk is not what the agent says but what it does. We build the guardrails first.
ExploreEnterprise Knowledge Assistants
Retrieval-augmented assistants that respect the permissions of the systems underneath, cite what they used, and decline when the answer is not there.
ExploreAI Governance & Responsible AI
Frameworks, controls and evidence built for organisations that have to explain their AI systems to a regulator, an auditor, a board or the public.
ExploreAI Workshops & Enablement
Executive sessions, engineering enablement and role-based training designed around your systems and your policies rather than generic curriculum.
ExploreCTO & Chief AI Officer Advisory
Fractional CTO and Chief AI Officer support for organisations making decisions that outlast the people making them.
ExploreThe sequence, in the order it actually happens
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.
AI in practice
Representative engagements, described at the level our clients permit. Sector and shape are accurate; identifying detail is withheld.
Setting AI direction for a provincial agency
A ministry with sixteen candidate AI projects and no way to compare them. We ran a six-week assessment across service delivery, back office and case management, scored each project against benefit, data readiness and privacy exposure, and produced a three-year sequenced plan.
Outcome — A defensible portfolio that survived internal privacy review and treasury board scrutiny.
Finding the one viable use case among twelve
A health authority had a backlog of AI requests from clinical and administrative teams. The assessment found that most depended on data that could not legally leave the province, narrowing the field to three candidates buildable on in-region infrastructure.
Outcome — A shortlist that had already cleared the residency question before design began.
A shared AI platform for multiple agencies
Rather than each department procuring separately, we designed a shared platform with per-tenant isolation, central governance and chargeback, so new departments onboard in weeks against pre-approved controls.
Outcome — New use cases onboarded without repeating the security review each time.
Automating intake for a high-volume application process
A programme receiving tens of thousands of applications a year processed them by hand. We built extraction and validation against the submitted forms with confidence-based routing — clean submissions flow through, ambiguous ones go to an officer with the uncertain fields highlighted.
Outcome — Officer time redirected from transcription to the cases that need judgement.
Reconciliation and exception handling in finance
An agent reads incoming remittance data, matches it against open items in the ERP, posts clean matches and routes exceptions to a clerk with the probable match and reasoning attached. Postings above a threshold require approval.
Outcome — Clerks working only the exceptions, with the routine volume handled and logged.
Policy answers for front-line student services
Advisors searched across academic regulations, financial aid rules and procedure manuals held in four systems. The assistant answers from the current approved version and cites the clause.
Outcome — Consistent answers across advisors, with the citation available if a student challenges one.
Start 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.