AI development in
Kitchener-Waterloo.
We build the AI tool, not the slide deck about it. One process, a short build on your own data, and a number it has to hit before anyone calls it a success.
Why most of these projects fail
Every organization has been told to do something with AI. Very little of it survives contact with real data and real staff. The pattern is consistent: a broad ambition ("use AI in operations"), a demo on clean sample data, and then a collision with the actual state of the systems — four places the information lives, none of them agreeing, and nobody with authority to decide what "correct" means.
The fix isn't better technology. It's picking one job small enough to finish, building against the systems you really have, and agreeing in advance what result would make it worth continuing. That's the whole method, and it's why we start by looking rather than quoting.
What we build
- Document and form processing — purchase orders, invoices, intake forms, applications, and case files read and filed without someone retyping them
- Internal assistants — answers drawn from your own policies, procedures, and records, with citations back to the source document so staff can verify
- Data extraction and clean-up — pulling structure out of years of unstructured records, feeding reports that reconcile
- Workflow automation — moving work across systems that were never built to talk to each other
- Classification and triage — routing incoming email, requests, or referrals to the right queue with the right priority
How a first project runs
1. One process, measured
We pick a single task and establish what it costs today — how long it takes, how often it's wrong, who does it. Without that baseline there's no honest way to claim an improvement later.
2. A short build on your data
A working tool against your real systems, not a demo on ours. Sensitive information stays in infrastructure you control, and we document which provider processes what.
3. Run both, compare
The tool runs alongside the manual process for a few weeks. You see where it agrees, where it doesn't, and how often a human needs to intervene — before anything depends on it.
4. Your call
Keep it, extend it, or stop. We'll tell you plainly if the numbers don't justify going further; a pilot that ends in "no" is a cheap answer to an expensive question.
The guardrails, up front
- No training on your data. Commercial API tiers that contractually exclude it, or models running entirely on infrastructure you control
- A review step where it matters. A person confirms output wherever being wrong has a real cost
- Logging. What the tool saw, what it decided, and why — so an error can be traced instead of argued about
- Named systems. A written summary of every service that touches your information, which is also what PIPEDA accountability requires
- Permissions that hold. An assistant should never surface a record the person asking couldn't already open
Start with ten questions instead of a proposal
Our AI readiness check scores your data, process, people, and guardrails, then tells you which one would sink a build. It takes about three minutes, needs no email address, and some results say "this isn't an AI problem yet".
Take the AI readiness checkCommon questions
What does an AI project actually cost?
A first pilot is fixed scope on one process, quoted before work starts, and deliberately small enough that a disappointing result is a cheap lesson rather than a write-off. We quote after seeing the process, not from a rate card.
Will our data be used to train someone else's model?
No. We use commercial API tiers that contractually exclude training on submitted data, we document exactly which provider processes what, and where the information is sensitive enough we run models that never leave infrastructure you control.
What if the tool gets something wrong?
It will, sometimes — that's why every build includes a review step and a log. A person confirms the output where the cost of being wrong is real, and we design the workflow so an error surfaces before it reaches a customer or a regulator.
Do we need to be a technology company for this to work?
No. The best candidates are unglamorous: manufacturers retyping purchase orders, non-profits copying intake forms into case management, firms summarising documents by hand. Repetitive, high-volume, text-heavy work is where this pays.
How long does a first build take?
Usually a couple of weeks from agreed scope to something working on your own data, then a few weeks running it alongside the manual process to compare results before anyone relies on it.