VERMYN

Founder-led technology studio · AI & Data

From complex data to intelligent systems.

We build AI, machine learning, data pipelines and automation that solve real business problems, engineered to run in production rather than to demo well.

  • AI
  • DATA ENGINEERING
  • AUTOMATION
DATAPROCESSINGINTELLIGENCEOUTCOME

Problems we solve

Where we create impact.

Every engagement starts from something that is not working. These are the four we are asked about most.

  • 01

    Problem

    Data is scattered across multiple systems.

    Solution

    Build centralised pipelines that turn disconnected sources into one reliable, queryable version of the truth.

  • 02

    Problem

    Teams spend hours on repetitive manual processes.

    Solution

    Automate the workflow with Python, APIs and AI, keeping a human review path for the cases that genuinely need judgement.

  • 03

    Problem

    Business decisions rely on spreadsheets and assumptions.

    Solution

    Create analytics systems with tested metric definitions, so the discussion moves from whether the number is right to what to do about it.

  • 04

    Problem

    AI prototypes work in demos but fail in production.

    Solution

    Engineer the parts a demo skips: APIs, pipelines, evaluation, monitoring and deployment.

How we work

From idea to production.

A short, legible process. You always know which stage we are in, what is being built, and what comes next.

  1. 01

    Understand

    Understand the business problem, data and requirements.

    We start with the problem, not the technology: what decision or process is failing, what data exists, who is affected, and what a good outcome actually looks like.

  2. 02

    Design

    Design the architecture, technology and implementation approach.

    We agree the architecture and the technology choices before building, including what is out of scope, so there are no surprises halfway through.

  3. 03

    Build

    Develop the solution using modern engineering practices.

    Version control, tests, code review and short feedback cycles. You see working software throughout, not only at the end.

  4. 04

    Deploy

    Integrate the solution into the required environment.

    Deployment into your environment with configuration, access control, documentation and a handover your team can act on.

  5. 05

    Improve

    Monitor, optimise and continuously improve the system.

    Monitoring and alerting so problems surface early, and a clear route for changes as the business and the data move.

Technology

Built with modern technology.

Tools we work with directly, chosen per project. Nothing listed here for decoration.

  • 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

Case studies

Selected work.

Engineering write-ups of systems we have built, each labelled for exactly what it is. We do not name clients who have not agreed to be named, and we do not publish results we cannot evidence.

  • Technical case study

    Data Engineering · Industrial / manufacturing

    Industrial Data Intelligence

    Machine telemetry arriving continuously from industrial equipment, turned into a consistent time-series store that engineers can query and chart.

    • Python
    • ETL
    • InfluxDB
    • Grafana
    View Case Study
  • Demo project

    AI / ML · Industrial / manufacturing

    Equipment Condition Modelling

    A worked baseline for condition monitoring: feature engineering over sensor history, an evaluated model, and a serving path.

    • Python
    • scikit-learn
    • MLflow
    • FastAPI
    View Case Study
  • Demo project

    Automation · Operations / back office

    Document Processing Pipeline

    Structured extraction from recurring business documents, with confidence thresholds and a human review path for anything uncertain.

    • Python
    • OpenAI APIs
    • FastAPI
    • PostgreSQL
    View Case Study

Why VERMYN

Engineering first. Business always.

How we make decisions when a project gets difficult, which is the only time principles matter.

  • 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.

VERMYN

AI | DATA | REAL SOLUTIONS

About VERMYN

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.

VERMYN is a founder-led technology studio. That means you talk directly to the person building your system, scope is set honestly against real capacity, and we say so when a project needs more hands than we have.

Have a problem worth solving?

Tell us what you're trying to build, automate or improve. Let's explore what technology can do.