Your Trusted AI Implementation Partner
Many theorize about AI's potential. We are the implementers who bring it into production—using your data, inside the workflows your people already run. That is the difference between a concept on paper and a result you can measure.
What Separates a Pilot From a Result
Most AI work does not stall on the technology. It stalls because nobody agreed, at the start, on what the technology was supposed to change.
That makes fit a question of temperament more than size. We work best with organizations that treat technology as leverage rather than overhead—teams actively looking for where to grow, willing to change how the work gets done, and clear-eyed that the useful changes are rarely the comfortable ones. Three commitments shape every engagement we take on.
Strategy Before Technology
Every engagement starts with your business case, not a tool selection. You name the outcome that matters—cycle time, capacity, cost to serve—and we work backwards from it. If AI is not the shortest path to that outcome, you will hear that from us first.
Adoption Is the Measure of Success
A model nobody uses returns nothing. We design around the people who will live with the system—their workflow, their exceptions, their reasons to keep using it after launch. You get training, documentation, and something your team reaches for rather than routes around.
Measured, Not Promised
We define what success looks like with you before the work starts, then report against those numbers afterwards. You will know what changed, by how much, and what it cost—including when the answer is smaller than anyone hoped.
How an Engagement Runs
The same five stages every time, so you always know which one you are in — and what you should be holding at the end of it.
Discover
You get an honest read on where AI could carry weight in your operation—and where it could not, yet. We map how the work actually runs before recommending anything.
You end up withA readiness assessment and a shortlist of candidate workflows, ranked by effort against return.
Define
You set the measures with us before anyone builds: what improves, by how much, and how we will both know it happened.
You end up withA written success measure with the baseline number it will be judged against.
Implement
We build into your environment, on your data, integrating with the systems your team already uses instead of replacing them.
You end up withA working system inside your environment, integrated and access-controlled.
Train and Embed
Your people get trained on the system, and the system gets tuned around them, until using it is the path of least resistance rather than an extra step.
You end up withTrained users, written documentation, and an adoption figure you can track.
Report and Evolve
You get reporting against the measures you set, then continued tuning. Most of the value arrives after launch, which is where we stay.
You end up withA report against the measures agreed in stage two, and a prioritised tuning backlog.
From Practical IT to Proven AI
We started as an IT company, and that shaped how we approach AI. Connecting a model to a chat box is not the hard part. The work that matters is keeping answers grounded in your data, integrating with the systems you already run without rebuilding them, and giving AI access your security posture can live with. Years of running the infrastructure underneath taught us the difference between a demo and something that holds up in production.
Meet Our Leadership Team
Experienced leaders dedicated to successful AI adoption within your business
Ian Strom brings 14+ years of technology leadership to Davinci, evolving from hands-on IT infrastructure to C-suite strategy. A co-founder of the firm in 2016, he champions AI-driven transformation while holding to one principle: technology should amplify human capability, not complicate it. A serial entrepreneur with ventures spanning multiple industries, Ian approaches business challenges as opportunities to build better systems for human growth.
Ryan Hulsker combines deep infrastructure expertise with innovative, cross-disciplinary thinking as one of Davinci's partners and co-founder. From his early work on foundational cloud systems to architecting enterprise transformations, he brings a unique ability to simplify the complex. Ryan's approach—connecting ideas from diverse fields to create innovative yet practical solutions—defines Davinci's technical vision.
Where That Confidence Comes From
Three commitments are a claim. These are the things you can check.
Employee Augmentation, Not Replacement
Your existing team becomes more capable, not redundant. Every implementation augments the workflows your people already own, so the work they do gets more valuable rather than obsolete.
Validated in Our Own Operations
We run our own business on the systems we build—ticketing, reporting, internal copilots. We are practitioners before we are advisors, so when we tell you something holds up under daily use, it is because it has held up under ours.
Implementation, Then Operations
Most of an AI system's working life happens after launch. For the clients who want it, we run the operations underneath—monitoring, cost control, model updates, and the IT helpdesk around them—so the system keeps earning once the project closes.
Vendor-Agnostic by Design
We hold partnerships across Microsoft, Google, AWS, and the rest — but we choose tools for your problem, not for our margin. Nobody pays us to recommend them. When we tell you a platform is the right fit, that recommendation is the whole reason you can trust it.
Find out where you actually stand.
The readiness assessment takes a few minutes and gives you a score, plus the workflows in your operation most likely to return something. You keep the output whether or not we work together.
Get your AI readiness scorePlatforms We Work Across
We work fluently across the major platforms, so the choice stays yours. We pick the tools that fit your problem — not the ones we're paid to resell.
