AI & technology leadership · Canada
We help owners work out where AI is worth the money, and what to change first.
The experience behind veryAI
Buying the tool is the easy part.
Getting value out of it isn't.
Which is why we start with the business rather than the technology. What that turns up differs every time.
An exploratory call is the quickest way to find out whether there is anything here worth doing.
For owners who know AI matters but aren't sure what to do with it. Start with the business, not the technology, and leave with a decision — not a list of possibilities.
AI adoption depends on the engineering underneath it. Senior technical judgment when delivery is slowing, quality is slipping, a team needs direction, or business and engineering have stopped speaking the same language.
For founders building ambitious technology — often with AI at the core — with finite resources to spend.
Who you'll be working with
Since 2012, the work behind veryAI has spanned startups, growing technology companies, and large enterprises — from CNN and Paramount to early-stage manufacturing and MedTech. veryAI was founded by Stanislav Derpoliuk to bring that experience to owners making AI decisions, with specialist collaborators brought in when an engagement calls for it.
The domains differ. The engineering that makes them work doesn't.
CTO and Interim CPO
A manufacturing startup understood its customers' operational problems but had no engineering function and no clear technical direction for the product.
Set product and technology direction, hired the core engineering team, and defined an architecture that absorbed a pivot from discrete manufacturing into heat treatment and batch processing. Introduced AI into the product and into how the company itself worked.
A production platform serving several manufacturers across multiple factories, and a company running AI in its own work, from engineering through to the leadership team.
VP of Software
A MedTech startup was building a connected medical device with one person on the software.
Went from building the MVP to running a nine-person team across iOS, Android, backend, QA, and design, leading backend development, a Windows proof of concept, and the QA processes.
First release on iOS and Android followed by regular releases, with engineering work aligned to medical-device regulatory process. Early AI/ML work became a poster presented at NVIDIA GTC.
Interim lead
Starting a tvOS app inside a large organisation meant agreeing architecture, standards, and engineering practices with the iOS team and several dependent groups before the work could move.
Stepped in as interim lead, helped shape the architecture and development processes, aligned dependent engineering groups, and took on high-risk foundational work.
With the direction settled, the team grew to fifteen engineers and kept building against it.