AI and Agentic AI Consulting in Vancouver

Why most AI pilots don't reach production

The demo works. Then security asks what the agent can access, legal asks where the data goes, finance asks what it will cost at scale, and nobody has a good answer. The pilot sits in limbo. None of these are model problems. They're design decisions that were put off.

We make them at the start. Before any build we agree what the agent may read and change, how its identity is managed, which data can leave your environment, how quality is measured and what a month of usage will cost. That turns the security review into a sign-off rather than a redesign.

Choosing your first use case

A good first agent has most of these properties:

• A task that happens often enough to matter, with a clear business owner
• Information the agent can reach through APIs or connectors you already control
• A measurable result, such as time per ticket or documents processed per day
• Low harm if the agent is wrong, with a person approving anything consequential
• Data you're permitted to process with AI under your privacy obligations

In practice, good first agents are usually internal. They answer staff questions from policy documents, triage service requests, extract data from forms or draft responses for a person to approve. Customer-facing agents come later, once your governance has been tested on lower-stakes work.

Governing AI agents

Treat an agent like a new employee with system access. It needs its own identity, only the permissions its job requires and a record of what it did. Microsoft Entra now supports dedicated identities for AI agents, so agents can be inventoried, governed with Conditional Access and reviewed like other identities instead of hiding behind shared service accounts.

On top of identity we add content safety filters, evaluations that test answers against a known set of questions before every release, prompt injection testing, and logging into the monitoring you already use. When something changes, whether a prompt, a model or a tool, the evaluations run again before it reaches users.

AI and privacy in BC

If an agent handles personal information, BC's privacy laws apply just as they would to any other system. Public bodies under FIPPA need a privacy impact assessment, and private organizations under PIPA need a reasonable purpose and appropriate safeguards.

In practice that means knowing which data the agent reads, where prompts and responses are processed and stored, and how long they're kept. We document these for your privacy officer as part of the design, and deploy models in Azure's Canadian regions where the models you need are available there.

What AI costs to run

AI costs are usage-based, which makes them easy to underestimate. The main drivers are the model you choose, how much text goes in and out of each request, how many requests you expect and supporting services such as search indexes and storage. A larger model isn't always better. Smaller models are often accurate enough for classification and extraction at a fraction of the cost.

We estimate running costs during discovery, set budgets and alerts in Azure, and track token usage per agent after launch, so there are no surprises when adoption grows.