Agentic workflows for Canadian teams — production automation, not demo-ware.
We build agentic AI workflows for Canadian companies: structured automation with LLM decision points, narrow action spaces, observability, and human-in-the-loop where it matters. We design for what survives production — document triage, research automation, monitoring agents, and multi-agent pipelines — with PIPEDA-aware data handling and collaboration in your time zone. We already automate real operational workflows in-market.
This page sits inside our broader ai and machine learning service cluster and is designed for teams searching with clear commercial intent.
Who this is for
- Canadian companies automating routine, high-volume operational work
- Teams that want agents constrained and observable, not autonomous and unpredictable
- Operators replacing manual triage, reminders, or reconciliation with reliable automation
- Businesses that need PIPEDA-aware data handling and a same-time-zone partner
Service Scope
What we typically deliver
- Agentic workflows with LangChain, LangGraph, and custom orchestration
- Constrained action spaces, validation, step/budget limits, and human approval
- Document triage, research automation, monitoring, and multi-agent pipelines
- Observability, evaluation, and cost controls for production operation
Delivery Process
How we move from scope to launch
Find the workflow worth automating
We identify the routine, rules-heavy work where an agent earns its keep, and design the action space and guardrails before building.
Build with constraints
We validate every tool call, add step and budget limits, require human approval for high-stakes actions, and instrument everything — demoed in Canadian hours.
Measure and harden
We run evaluation continuously, track cost per run, and tune the system so it stays reliable as volume grows.
Continue exploring
Portfolio, related services, and ways to connect
Automation that runs itself, in Canada
Our Fresh Tracks Canada payment service automated schedules, reminders, and statements end to end — a real example of reliable, system-managed workflow automation.
Read the Canada case studyHow we approach agents
See our broader take on building agentic systems that hold up in production rather than in a demo video.
Agentic workflow approachTalk through your workflow
Tell us the routine work you want to automate. We will tell you honestly whether an agent fits and how we would build it.
Discuss your workflowFrequently asked questions
Yes, with careful design. Fully autonomous agents are still unreliable for most production use, but structured workflows with LLM decision points, narrow action spaces, observability, and human-in-the-loop are production-ready — and that is what we build.
We constrain the action space, validate every tool call before execution, add step and budget limits, require human approval for high-stakes actions, and log everything with continuous evaluation. Most “agent went wild” stories trace back to systems missing these.
We design with PIPEDA and, where relevant, Quebec Law 25 in mind. For sensitive data we can use open-source models hosted in your own cloud with in-region data residency, so data never leaves your environment.
Yes — collaboration in overlap with Toronto and Vancouver hours, CAD or USD contracts, and full ownership of the prompts, pipelines, and orchestration we build.