Jointhubs

AI/ML laboratory

We help you understand
your data and enjoy it
We help you understand your data
and enjoy it

We build AI systems that help people do their work.

The first 20 minutes are free.

ALL THE ANSWERS ARE HERE. HIDDEN.

SCROLL

How we can help

Three ways to work together

Start with what hurts today — we will shape the rest around your budget and pace.

Process automationRepeatable tasks, documents and reports flow without retyping them by hand.See the offer →

Delivered, not proposed.

Nothing here is a mock-up. Open a card to see how far it got, then let’s build the next one together.

BUILT AND WAITING

Waiting for a system to join. Each card says exactly how far it got.

Automated reading of qEEG data. The slow manual part of the read happens by itself. The engine is rule-based today; AI is on the roadmap. Research-stage work: not a diagnostic device.

  • Automated qEEG reading
  • Rule-based engine today, AI on the roadmap
  • Demo available on request
Ask about Neurohubs

A workflow intelligence platform packaged to run locally, with the security and auth work already done.

  • Runs fully locally, on your own machine
  • Security, auth and data-in-transit baked in
  • Demo available on request
  • Optional cloud GPU deployment
Book a demo

PROVEN AND PORTABLE

The research is done and the parts already exist. Bring a problem shaped like one of these and the same method goes straight in.

Models how ownership is split, then reports on pay in the format the transparency rules ask for.

  • Equity and cap table split modelling
  • Pay transparency reporting for HR
  • Audit-ready report generation
Discuss this

One aggregated model, nested by category, with a separately computed model for every district rather than a single city-wide average.

  • One aggregated data model, nested by category
  • Separate computed model per district
  • City down to district granularity
  • Strong validation results
Discuss this

Reusable integration blocks that connect models to the tools a team is already working in, without a rebuild.

  • Workflow automation
  • Wires AI into systems already in place
  • Reusable integration building blocks
  • No lock-in to a single model provider
Discuss this

PUBLISHED

Open source and peer-reviewed. Open any of it right now.

Agent squads assess incoming tasks, plan them, write the code and put it through review, and run on our own work every day.

  • Planning, code and review in the hands of agents
  • Delivery tracking across projects
  • In daily use on our own delivery
  • Open source, under the linear-agents repo
View on GitHub

Your notes stay on your own machine, and ThoughtMap serves them over MCP from Docker, so a coding agent reads and writes the knowledge base directly instead of guessing at it. Any harness can connect, Claude Code included.

  • Local-first: the vault never has to leave your machine
  • ThoughtMap serves it over MCP, in Docker
  • Open to any agent harness, Claude Code included
  • Semantic clustering, local Ollama embeddings
View on GitHub

A library for building features that carry their own context, with validation patterns that do not leak.

  • Context-aware feature engineering
  • Leakage-safe validation patterns
  • Hierarchical statistical signals
  • Published on PyPI
View the library

Extends Rolling Origin Evaluation to multi-criteria model selection: the WSA, WAPM and CESDM metrics weigh accuracy, balance across scenarios and stability over repeated splits.

  • Three metrics: WSA, WAPM and CESDM
  • Correlation-adjusted variants penalise systemic errors
  • Stress-testing case study: GDP under three scenarios
  • Published in Risk.net
Read the paper
Mateusz Stachowicz

Mateusz Stachowicz

AI systems architect

View on LinkedIn

For over 9 years I have worked with artificial intelligence systems: I designed, built and maintained them for the largest corporations in the world. I turned that experience into my own methodology, proven in practice and working every time.

HOW I WORK

  1. Context

    Where and in what environment the solution has to work.

  2. Data

    What the data is and what it really says.

  3. Method

    Choosing the approach and the smallest useful way forward.

Collaboration

Engagement Tiers

From a single conversation to full production.

Discovery

€75 / h
Consultation

Frame the real question.

  • Problem definition
  • Feasibility assessment
  • Initial roadmap

Production

It depends
Every consultant says that. After 20 minutes I'll tell you on what.

Deploy, scale, maintain.

Production preview
Discuss scope
  • Enterprise SLA
  • System integration
  • Compliance & security
  • Continuous training

BEFORE YOU ASK

The questions you are actually asking.

What does it cost to find out if this is worth doing?

The first 20 minutes of the call are free. After that you can order a Discovery hour to name the real question and judge the feasibility before anything is built. If it turns out you do not need me, you will know that quickly and cheaply.

You work across property, contracts, clinical data and delivery. Does that not make you a generalist?

The domains differ. The method does not. Each one runs the same steps in the same order, and the domain knowledge comes from your team. What I bring is the part that is identical every time, which is exactly the part most projects get wrong.

Who owns the code when we stop working together?

You do, from day one. Your cloud project, your database, your repository. Nothing runs on my login.

You have no client testimonials.

I do. We can talk about them.

Our data cannot leave the company.

Then it does not. Local-first is the default, not the upsell. Neo runs entirely in Docker on your own machine, and Jointhubs OS embeds locally with cloud calls only when you switch them on.

Tell me what is broken.

One message. I will tell you honestly whether this is a real problem for AI, a plain software problem, or something you should not build at all.