Find the bottleneck.
Then apply the intelligence.
We embed alongside your teams to map how the work actually flows, quantify where it stalls, and deploy AI that makes people measurably faster — across any industry, any function.
Five steps, in this order, every time
Most AI programmes fail because they start at the model. We start at the work. Nothing gets built until the constraint is named, measured and agreed with the people who live in it.
Observe
We sit with the teams doing the work and map the process as it truly runs — including the spreadsheets and workarounds nobody documented.
Output · Process map
Quantify
We measure where time, rework and waiting actually accumulate, and rank the constraints by the value of relieving them.
Output · Bottleneck register
Design
We specify the smallest system that moves the metric — with the data, controls, oversight and failure modes written down before a line of code.
Output · Solution spec
Deploy
We build and ship into production alongside your team, with an evaluation harness that proves the system behaves before it carries real volume.
Output · Working software
Transfer
We hand over the code, the documentation and the operating runbook, and train your people to run and extend it without us.
Output · Ownership
The bottleneck audit
Every engagement opens the same way: a structured audit of how work moves through your business. It is deliberately unglamorous, and it is the reason the build that follows is short.
You keep the findings whether or not you engage us further.
What we look for
- Where work waits — queues, approvals, handoffs between systems
- Where people re-key data that already exists somewhere else
- Where exceptions consume the day and the standard path is rare
- Where critical knowledge sits with one person and stops when they do
- Where the data you would need to automate is missing or untrusted
What you receive
- A ranked bottleneck register with the measurement method attached
- A baseline you can hold any future system against
- A build / buy / leave-alone recommendation for each candidate
- A sequenced roadmap with effort, risk and dependency called out
Three ways to start
Fixed scope, stated duration, defined deliverables. Each stage stands on its own — you decide whether there is a next one.
Diagnostic audit
We map one value stream end to end, quantify the constraints and hand you the register, the baseline and the roadmap.
Best when you know something is slow but not why.
Pilot build
One workflow taken into production — with evaluation, human oversight, a measured baseline and a documented handover.
Best when you need proof inside your own operation.
Embedded partner
We work inside your team on a standing basis — governance, capability building, and scaling what has already been proven.
Best when AI becomes a programme, not a project.
What we will not do
A short list, held firmly. It is the fastest way to know whether we are the right firm for you.
No black boxes
If your team cannot explain how a decision was reached, we have not finished the work.
No lock-in
You own the code, the data, the prompts and the infrastructure. We build for the day you no longer need us.
No automation without oversight
Consequential decisions keep a human in the loop, with escalation paths designed in from the start.
No numbers we have not earned
We are a young firm. We quote results only from work we have done, with the measurement method attached — never borrowed industry averages.
No surprises with your data
Nothing leaves your environment without written agreement on what, where and for how long.
Grant Schick
Founder & Director
// Twenty-five years of operating experience, now pointed at applied AI.
Twenty-five years inside operating businesses — not observing them from a deck.
Grant has spent his career in the parts of a business where the work actually happens: operations, delivery, and the systems that hold them together. He has run teams through growth, restructuring and system replacement, and he has been accountable for the results afterwards — which is a different discipline from recommending them.
Across those years the pattern repeated in every industry he worked in. The technology was rarely the limiting factor. The limit was a handful of specific places where information stopped moving, decisions waited on one person, or skilled people spent their day re-entering what a system already knew. Fix those, and everything downstream gets faster. Automate around them, and you simply industrialise the problem.
He founded AI Technologies to close that gap with the current generation of AI tooling — not as a technology showcase, but as an operator's instrument. The work begins where it always did: on the floor, with the people doing the job, mapping the process as it truly runs. Then the constraint gets measured, the smallest useful system gets designed around it, and it goes into production with the oversight and evidence that let a board sign it off.
The firm is deliberately industry-agnostic. A queue is a queue, whether it holds patients, pallets or loan applications — and twenty-five years of seeing them in different clothing is the advantage clients are buying.
Start with one workflow.
Tell us where the work slows down. We will tell you honestly whether AI is the right instrument — and if it is not, we will say so.