AI Consulting in Canada
Example · AI Consulting
Context
Many organisations in Canada are interested in AI but are overwhelmed by tooling, buzzwords, and one-off experiments. Operational teams already feel the pain of manual work, scattered information, and decisions buried in documents or people’s heads. They don’t necessarily want another platform – they want help turning AI into reliable infrastructure.
What this engagement focuses on
In this example, Flowtica works as an AI consultant in Canada for an operations-heavy team. The goal is to design and implement a concrete system – such as an internal assistant or workflow – rather than a generic “AI strategy”.
Typical questions we help answer with the system include:
- “How do we route and answer routine questions consistently, without creating more tools for staff?”
- “How do we let people self-serve answers from our policies and procedures without losing control?”
- “How can AI draft structured tickets, emails, or summaries that our team can quickly review and approve?”
- “How do we monitor and improve this over time instead of running a one-off pilot?”
Architecture at a glance
The diagram below shows a high-level pattern we reuse across different consulting engagements. It stays tool-agnostic so it can work with your existing stack and can evolve over time.
Your organisation & workflows
- Existing tools (CRM, ticketing, intranet, chat)
- Policies, procedures, and reference documents
- Typical requests, edge cases, and constraints
- People and teams responsible for decisions
Private workflow engine
- Runs in an environment that you control
- Connects to your systems and document sources through APIs or scripted integrations
- Encodes how to handle different request types and when to escalate to humans
Language model
- Receives only the relevant context and the user’s request
- Drafts grounded answers, summaries, or structured payloads (tickets, forms)
- Operates within clear guardrails defined in the workflow
Your team & tools
- Staff interact via chat, web, or existing channels
- Draft outputs appear in your tools for review and approval
- Feedback is captured so behaviour can be tuned over time
How the consulting process behaves in practice
- We start by mapping a specific workflow or assistant use case, including inputs, decisions, and handoffs.
- We design how the workflow engine and language model should interact with your tools, where to retrieve context, and when to involve humans.
- We implement a first version that runs in a controlled environment, with logging and clear ways to review model outputs.
- Together with your team, we iterate on prompts, routing, and UI so the system fits into day-to-day work instead of sitting on the side.
Data ownership & deployment model
We avoid locking you into a single, opaque stack. As much as possible, the deployment follows these principles:
- The workflow layer runs in your cloud account or on a dedicated, isolated instance.
- The system connects to your existing tools and document stores, rather than copying everything into a new platform.
- The choice of language model provider can be revisited later without redesigning the entire system.
This makes it easier to adapt to changing requirements, vendor options, or internal policies without starting from scratch.
Where Flowtica fits in
As an AI consultant in Canada, Flowtica’s role is to work with your team to identify the right starting point, design the underlying architecture, and then implement and operate the system until it behaves like part of your infrastructure.
We focus on a small number of high-impact workflows, document our choices, and keep the system observable so your team can own it in the long run.