We deploy AI where it makes the biggest impact.
Your most expensive people spend their days on work that repeats. We find that work, build AI to carry it, and show you the hours coming back, month by month.
| 01 Map · People | Start with a real org chart. Our own AI workflows draw your organization as it actually runs, inside your infrastructure, so your data stays home and nobody loses a week to interviews. Then we rank the gap roles standing between you and your objectives. |
| 02 Map · Process | Evaluate every department's workflows. We score every workflow in every department with our own framework: what to automate, and in what order. |
| 03 Map · Technology | Uncover your real tech footprint. The interdependencies between your systems decide which workflows can be automated at all. We chart them before promising anything. |
| 04 Train | Train your people. We use AI to make your team more productive, never to replace it. A capability map for every employee shows where AI can carry part of the load, and where training lifts their fluency. |
| 05 Build | Build agentic workflows. We build the best-scoring workflows inside your own infrastructure. Each one reads only what it is allowed to read, checks the answer against your own catalogue, and waits for a named person before anything moves. |
How mapping works → · The courses → · How we build →
- 30h → 30m — an agentic inbox for insurance claims intake
- 2 min — a browser agent alerting teams after a UK parliamentary committee passage is spoken
- 50% — operational efficiency gain from workforce orchestration across construction sites
- 60%+ — less non-billable email time at a UK legal practice, with attorneys approving every message
- Healthcare — PHIPA and HIPAA constrain where health information travels and who answers for it, not what a model can do.
- Commercial — Your system of record was filled in by hand over four years, and we join it up so a question in plain English has an answer.
- Startups — Support, failed payments and the board pack can all run before you are ready to hire for any of them.
- Blog — 77 posts, including Decision Model vs LLM and How to Evaluate an LLM Classifier.
- The shared stack — model, data, memory, workflows and skills, shared across everything we build.
- Academy — hands-on tutorials for non-developers who want to ship production-grade apps.
XY Space is Cho Yin Yong, Principal AI Solutions Engineer, a University of Toronto computer science lecturer, co-author of two patents and nine years of engineering leadership in regulated technology, and Mahmoud Halat, Principal AI Solutions Architect, employee two at Verto Health with 100+ hospital deployments and winner of the $100k Lovable Shipped 2025 grand prize against 5,800 builders. We scope every engagement and stay on it to the end.
Start with the map of your organization, or with the one job that hurts. Measured in hours and money, and everything we build stays yours.
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