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GenArtifactAI

AI engineering · Private AI · Training

Turn Intelligence Into Reality.

Practical AI engineering and training: automated workflows, private knowledge assistants, connected integrations and hands-on learning, with clear choices about where your data and models run.

Explore the five divisionsFind a solution
Deployment
LocalCustomer-hostedHybridCloud(cloud and hybrid where supported)

Five divisions

One engineering standard. Five ways to put AI to work.

  1. 01

    Automate

    GenArtifactAI Automate

    Business workflows, documents and reporting that run themselves, with people in control.

    • Workflow automation
    • Document processing and reporting
    • AI-assisted operations
  2. 02

    Private AI

    GenArtifactAI Private AI

    Knowledge assistants over your own documents, deployed where your data is allowed to live.

    • Private knowledge assistants and RAG
    • Local and customer-hosted deployments
    • Cloud and hybrid options where supported
  3. 03

    Academy

    GenArtifactAI Academy

    Project-based training from Python fundamentals to LLMs, RAG and agents.

    • Python, data science, ML and deep learning
    • Generative AI, LLMs, RAG and agents
    • Workshops, courses and project-based training
  4. 04

    Automation Studio

    GenArtifactAI Automation Studio

    Integrations, permission-controlled agents and custom software built to fit your systems.

    • API integrations and connected workflows
    • AI agents with controlled permissions
    • Custom software and automation
  5. 05

    Labs & Products

    GenArtifactAI Labs & Products

    Research, prototypes and reusable AI tools, released only once they are validated.

    • Research and development
    • Reusable AI products and developer tools
    • Prototypes and validated releases

Deployment options

Your data, your choice of deployment.

Every GenArtifactAI project will start by deciding where data and models are allowed to run, with the trade-offs visible before anything is built.

  • Local

    Your data
    On your own devices or on-premises servers.
    The model
    Open-weight models running on your hardware.
    Best for
    Confidential documents, restricted networks, offline use.
    Trade-offs
    Hardware you own limits model size and speed.

  • Customer-hosted

    Your data
    In your servers or your own cloud account.
    The model
    Runs in the same environment, under your access controls.
    Best for
    Organisations with existing IT infrastructure and policies.
    Trade-offs
    You operate the infrastructure and its access controls.

  • Hybrid

    Your data
    Stays in your environment.
    The model
    A hosted model API for selected tasks, where your policy allows.
    Best for
    Mixed sensitivity: private data, demanding language tasks.
    Trade-offs
    What is sent to the API must be reviewed; usage-based API costs.

  • Cloud

    Your data
    In a cloud provider's environment.
    The model
    Provider-hosted models and services.
    Best for
    Fast starts with low-sensitivity data.
    Trade-offs
    Provider terms and data policies apply; ongoing usage costs.

Cloud and hybrid options are offered where the provider and your data policy support them.

Solution finder

Not sure where to start? Answer three questions.

Tell us your goal, how sensitive your data is and who will use it. We point you to the right division and deployment options.

Find a solution
  1. 01What do you want AI to do?Automate a business process · Answer questions from our documents · Train people · Connect systems or build an agent · Explore a product or research idea
  2. 02How sensitive is the data involved?Public or low sensitivity · Internal business data · Confidential or regulated
  3. 03Who will work with it?A business team · A technical team · Students or educators

How we work

Engineering you can inspect.

  • Practical engineering

    Systems that run in your environment and hold up after the demo.

  • Transparent deployment

    You see where data and models run, and what each option trades off, before you commit.

  • Privacy-aware design

    Data minimisation and access control are design inputs, not afterthoughts.

  • Affordable implementation

    Start small, reuse what you already have, and scale what proves useful.

  • Useful over impressive

    If a simpler tool solves the problem, that is the recommendation.

Case studies

Coming soon, with real evidence.

Case studies are published only with genuine, publishable evidence and the client’s permission. Until then, this section stays empty: no customer names, testimonials or results.

Turn intelligence into something that works.

Start with the solution finder, or send an enquiry about the division that fits.

Find a solutionSend an enquiry