About
Technology with purpose.
VERMYN is an AI and data technology studio focused on building practical digital solutions for real-world business problems.
We combine software engineering, artificial intelligence and data engineering to help businesses automate processes, understand their data and build intelligent systems.
- Structure
- Founder-led technology studio
- Focus
- AI, data engineering, automation
- Approach
- Production systems, not prototypes
- Engagement
- Direct with the engineer building it
Why VERMYN exists
Most businesses don’t have a technology problem.
They have a process that takes three days and should take an hour. Data in five systems that disagree with each other. A decision made every week on instinct because the number nobody trusts arrives too late to use.
These are engineering problems, but they are rarely solved by buying more software. They are solved by understanding the process properly and then building the specific thing that fixes it — which is unglamorous, well-understood work that a large consultancy is not structured to do at a sensible price, and a general software agency is not equipped to do at all.
VERMYN exists in that gap: specialist enough to build data and AI systems properly, small enough that you talk to the person writing the code.
Where we are today
VERMYN is a founder-led studio, not a large firm, and we would rather say so than imply otherwise.
In practice that means you speak directly to the engineer building your system, scope is set against real capacity rather than an org chart, and we tell you plainly when a project needs more hands than we have.
It also means we are selective. We take on work where the problem is well-defined enough to be solved properly, and we will say no to work we cannot do well. As the studio grows, that standard is the thing we intend to keep.
Engineering philosophy
How we build.
Four positions that decide most of the arguments before they happen.
01
The problem comes before the technology
Plenty of projects begin with a technology someone wants to use. Those projects tend to produce something impressive that nobody needs. We start from what is failing — a slow process, a decision made blind, a report nobody trusts — and work backwards to the smallest system that fixes it.
02
A demo is not a system
The gap between a working notebook and a working system is where most AI and data projects die. Scheduling, retries, validation, monitoring, access control, deployment and handover are not extras added at the end. They are most of the actual engineering, and we scope them in from the start.
03
Boring technology, chosen deliberately
We use well-understood tools with good documentation and long support horizons, because someone has to maintain this after we leave. When a novel tool is genuinely the right answer we will use it, and we will explain why it earned its place.
04
You should be able to leave
Every engagement ends with documentation, source code in your version control, and infrastructure you own. If you decide to take the work in-house or hand it to someone else, nothing about our setup should make that difficult.
Technology philosophy
Tools are a means, not an identity.
We work primarily in the Python data and AI ecosystem because it has the deepest tooling, the widest hiring pool and the longest support horizon. The list below is what we use routinely, not everything we could name.
AI / ML
- Python
- scikit-learn
- PyTorch
- MLflow
- OpenAI APIs
Data
- Python
- SQL
- PySpark
- Apache Airflow
- PostgreSQL
Cloud & Engineering
- AWS
- Docker
- FastAPI
- GitHub Actions
Analytics
- Power BI
- Grafana
- Pandas
What you can expect
Engineering first. Business always.
01
Practical Engineering
We focus on solutions that can actually be deployed and maintained. A system nobody can operate is not finished.
02
Business-first Thinking
Technology decisions start with the problem, not the technology. If a simpler approach solves it, we will say so.
03
Clear Communication
Plain language, transparent progress and clearly defined deliverables. You should always know what is being built and why.
04
Production Mindset
We design for reliability, scalability and maintainability, because the demo is the easy part and the next two years are not.
Long-term vision
Where this goes.
- Now
Founder-led AI and data studio
Selective client work in machine learning, data engineering and automation, delivered end to end.
- Next
A small specialist engineering team
Adding engineers who share the same standard, so we can take on larger systems without diluting how they are built.
- Later
Services alongside our own products
Turning the patterns we build repeatedly into products, while keeping the consulting work that keeps us close to real problems.
Let's talk about the problem.
No pitch deck required. Describe what is not working and we will tell you honestly whether we are the right people to fix it.