Book
Applied Higher Order Functions
Jiri Knesl's book on the algorithms we build with, in hardback.
See the bookYour team decides things like which crew takes which job, which order to accept or what price to quote hundreds of times a month, usually in a spreadsheet. Being a few per cent off each time adds up to a lot of money. We build software that gives a better answer. It works on your data, fits into your team's daily work, and a person can always change its answer.
What is going to happen before it shows up in the numbers?
When it goes wrong
Where should limited people and money go to earn the most?
When it goes wrong
In what order, and on which machine or crew, should the work run?
When it goes wrong
Which of these should we take on, and which should we turn down?
When it goes wrong
Is the work delivering what the plan promised?
When it goes wrong
Can we trust this logic where a mistake is expensive or unsafe?
When it goes wrong
Workforce scheduling, shift planning, dispatch, routing, field-service allocation, fleet utilisation, capacity planning, maintenance scheduling.
Quoting, order acceptance, contract selection, delivery promises, pricing when you don't yet know the cost, which projects come first, which work to decline.
Stock and spare parts, buying and supplier choice, working capital, receivables, warranty and claims recovery.
It pays most in companies with a lot of operations and few analysts, like field and industrial services, maintenance, inspection, logistics and fleets, ports, e-fuel plants, laboratories, waste, specialised distribution, and software firms with a service team.
How we measure the gain
First we work out the gain on your own history. Then we measure it live against the way you decide today, so the number you show your board comes from your own business.
We've used every one of these in paid work.
Finds the best schedule or allocation for the goal you set, within the limits you work under.
Runs a plan through the disruptions it will meet before you commit to it.
Uses your history to forecast what comes next, and tells you how far off the forecast might be.
Machine learning when you have enough data for it. We measure it the same way as the other methods.
We prove that critical logic does what it should, with Rainbow, our open-source theorem prover.
Book
Jiri Knesl's book on the algorithms we build with, in hardback.
See the bookFormal methods
Our open-source theorem prover, with kernel-checked theorems about all 222 algorithms in our catalogue.
See RainbowWe learn it fast, usually within the first two weeks. The maths behind scheduling, pricing or stock is much the same from one industry to the next.
If your business makes the same choice again and again, and a wrong answer costs money or time, it fits. Tell us about it, and we work out what a better answer could be worth.
Jiri or Ivo will reply and tell you what a better way could be worth. Talking to us costs nothing.
Who you deal with
One partner is responsible for your project from the first call to the running system. Your work stays confidential.