Agents designed for your business that work with your data and take actions in your systems.
Discipline: AI Agents
Working term: Senior people you meet are the people doing the work
Formats: ongoing monthly work or a single audit
Who you talk to: the senior lead, on every weekly call
The service
Our approach to Custom AI Agents
We design the agent around the job it has to do: which model to use, which tools it can call, what it may and may not do, and when a person steps in. Multi-step workflows, structured tool calls, retries and fallbacks are built against a written spec and a test set, so you can measure how it behaves.
Before work starts, we agree on the number that matters most to you. Each sprint is planned against it. You get a short recorded update every week and a dashboard you can open any time, and the senior lead joins every call.
A senior lead runs your account from day one
A recorded walkthrough each week and a shared reporting dashboard
We work inside the tools and CMS your team already uses
Target ranges written into the agreement before we begin
Our Process
Four stages of Custom AI Agents
The same order every time: find the constraint, plan, deliver, then review. Here is what each stage involves.
01
Find the constraint
We record a baseline for Custom AI Agents, look for what is holding results back, and write down the numbers you will judge us by.
02
Put the work in order
A senior lead drafts a 90-day plan. Fast fixes come first; the slower, harder-to-copy work is scoped to follow them.
03
Deliver every week
Work goes live in weekly batches. Tracking and reporting are kept current against the targets you approved at the start.
04
Review and adjust
Once a month we compare results to the plan and change course where the data says to. You can see where each dollar went.
A demo shows what is possible. A production agent has to handle real customers, odd requests and failures gracefully. We design the architecture, error handling, guardrails and grounding so the agent behaves consistently in live use. Data can be hosted in Canadian cloud regions where privacy requirements or client contracts call for it.
That engineering is the gap between a demo and something you can rely on.
Architecture designed for live use
Guardrails, grounding and error handling
Consistent behaviour with real users
Canadian data hosting when required
02
How progress tends to build
Commercial terms usually move later than technical fixes
Integrated & grounded
Connect the agent to your tools and data
An agent becomes useful when it can look things up and get work done. We connect it to your APIs, databases and business tools, using MCP where that fits, and ground its answers in your own documents and records. It works from your information, which keeps a general model’s guesswork out of the answers.
That integration is the difference between a helpful agent and a chat novelty.
Connections to your APIs, data and tools
MCP and tool use where appropriate
Answers grounded in your own information
Agents that complete real tasks
03
Content velocity
Briefed, drafted, edited, shipped
Evals & guardrails
Test it, limit it and keep watching
An agent nobody evaluates will drift. We build test sets based on real tasks to measure quality, guardrails that keep the agent within scope and policy and monitoring that flags regressions after changes. Where an agent makes decisions about people, we help you plan the transparency Quebec’s Law 25 and PIPEDA expect.
This is what makes it safe to put an agent in front of customers.
Quality tests based on real tasks
Scope and policy guardrails
Monitoring and regression alerts
Privacy and transparency planning
Our working terms
What a Custom AI Agents engagement includes
These describe how we staff, report on and scope the work. They hold from the first week, before any result exists to point at.
A named senior specialist leads each account
Progress shown in live Looker Studio dashboards
Measured on leads and revenue
Senior
People you meet are the people doing the work
Written
Scope and price agreed before work starts
Weekly
Progress updates in plain language
CAD
Scoped and invoiced in Canadian dollars
FAQs
Before you sign: common questions
Straight answers on scope, timing and how we report.
Why do so many AI agents fail after launch?
They are often built as demos without guardrails, grounding, error handling or evaluation. We engineer agents for live use so they cope with real users and unusual cases.
How do you prevent made-up answers and unsafe actions?
We ground the agent in your data, limit which tools it can use, add guardrails and test it against real tasks, so its behaviour is measured.
Can the agent work with our systems?
Yes. We connect it to your tools, APIs and data, including through MCP where that fits, so it can take real actions and answer accurately.
Where is our data stored?
That depends on your requirements. We can deploy on Canadian cloud regions and choose model providers whose data terms suit your privacy obligations under PIPEDA or provincial law.
Tell us about the business and a senior specialist will get back to you within one business day. If starting with AI Agents looks like the wrong first step, we will say so and suggest what to fix first.
