Ush
An AI meeting scheduler for executive assistants. Architect and sole developer, start to finish.
The problem
Executive assistants pick meeting slots using implicit factors nobody has written down: habits, travel times, how much a meeting matters, the other party’s constraints. That logic lives entirely in their heads and cannot be delegated or scaled.
There was nothing else to work from. No existing process to automate, no system to integrate. Just judgement in somebody’s head, which had to be made explicit and computable from scratch.
The insight
Scheduling is not a workflow to automate. It is a constrained optimisation problem wearing a workflow’s clothes. Once the assistant’s judgement is written down as weighted parameters, “pick a slot” stops being a sequence of steps and becomes a search for the provably best option.
The architecture
Hard constraints filter the candidates, weighted soft constraints rank what survives, and the language model only writes the human-readable explanation. The decision itself comes out of a transparent scoring engine, so every suggestion is debuggable and reproducible instead of a black box.
- Preferences learned from calendar history.
- Calendars read and written in both directions.
- Real-time travel time between consecutive meetings, so physically impossible slots are rejected outright.
- Tenancy scoped per user by construction, so an assistant can schedule for several principals with nothing leaking between them.
- The system drafts the reply and never auto-sends it. A person is in the loop on every outbound message, by design and by the granted permission scopes.
Python, Flask and a separate background worker, PostgreSQL for everything including the job queue, Docker. Polling instead of push, a single database instead of a spread of services, self-hosted compute with cloud APIs only. Each of those is a cost and control trade-off, made deliberately and written down.
What it took
First beta in one month, genuinely usable at four. The stakeholders arrived holding UI mockups they had drawn, and the shipped product replaced them, because it solved what they needed instead of what they had drawn.
The client company was small, funded by a founder of its own — the same shape of buyer as the other work on this site.
What is not on this page
No client name, no logo, no quote. No usage numbers either: how many assistants or principals use it, and how many meetings it has handled, were never measured. The panel above carries the only outcome figure on record, and it is the only figure published here.