In practice
AI agents: what we build
Internal tools with an agent behind them
Boards, queues and dashboards where the agent keeps the state current — reading incoming email, updating records, flagging what changed — so staff work from an accurate picture.
Agents that use your systems
Agents connected to your CRM, accounting, scheduling or industry software through their APIs, with each action limited to what that role is allowed to do.
Guardrails and permissions
Fixed rules the agent cannot override, approval steps for anything irreversible, and scopes that keep it out of data it has no reason to touch.
Logging and evaluation
Every action and its reasoning recorded, and an evaluation suite rerun whenever the model, the prompt or the process changes.
A good fit when
- Staff spend their day moving information between systems.
- A process has clear rules but more cases than people can keep up with.
- You want AI inside the product your team uses, not another chat window.
- You need to show, afterwards, what was done and why.
How an engagement runs
One week mapping the real workflow with your team, exceptions included. Two to three weeks proving one workflow on your own documents and systems, against an evaluation set agreed up front. Then the production build — integrations, permissions, monitoring and review points — shipped behind your authentication, in your cloud, and handed over with documentation and training.
We work on site with teams in North Vancouver, West Vancouver and Downtown Vancouver.
Proof in our own products
We run this in production ourselves
ITelliBuilder
One board for insurance-funded reconstruction. An agent reads the project email and updates the board under fixed rules; the owner’s side gets a plain-English view.
Visit ITelliBuilderITelliDesk
Users describe what they need from inside the app, an AI analyst turns it into a spec, and our team triages it and answers in the same thread.
Visit ITelliDeskQuorum
An AI board of advisors: independent views, a challenge round, then a synthesis with alignment votes and the dissent on record.
Visit QuorumQuestions we are asked
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent takes actions: it reads, decides and changes things in your systems. That is why the engineering is mostly about permissions, review points and logging rather than conversation.
Which AI models do you use?
Whichever is best for the task at the time. We are model-agnostic and build with OpenAI, Anthropic Claude and open-weight models, so the same system can move to a better or cheaper model without a rewrite.
What stops an agent from doing something wrong?
It can only call the actions it has been given, inside the permissions of the role it runs as. Anything irreversible goes to a person for approval, and every step is logged so a mistake is visible and can be rolled back.
Can it work with our existing software?
Yes, if the software has an API or a reliable export. Part of the workflow mapping week is checking exactly what your systems allow before anything is promised.
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