Skip to content

Work

What we built for our clients and what it did for them.

We keep client work confidential. Below are the cases we can publish and 13 clients we can name. If you want a reference, ask us and we will set up a phone call.

Cases

Livesport coordinate Livesport's internal systems must stay correct under spiky load, because an error shows up as wrong scores for tens of millions of people within seconds. We designed, built and operated nine internal systems for Livesport in TypeScript and Node.js, and built them so Livesport's own team can maintain them. In production Nine internal systems running in production behind a platform with more than 100 million users. Flexiana (own product: Gitrevio) predict Companies could see commits, tickets and deploys. They learned what that work did to cost, risk and margin only after the books were closed. We built and run Gitrevio. It reads 31 engineering, project and HR sources, matches each person to one identity, forecasts what each project will cost to finish and flags margin and delivery risk while there's still time to act. In production A security-hardened product that runs inside Flexiana and is sold at gitrev.io. Flexiana Decision Lab (Rainbow) verify An allocation rule can pass every test and still break a capacity limit, a contract or an exclusivity clause on the one input no test tried. We built Rainbow, a theorem prover in Clojure with a small trusted kernel, and put it to work on our catalogue of operations-research algorithms. Formally proved Kernel-checked theorems about all 222 algorithms in our OR catalogue. Illustrative example (not a client engagement) select With more work on offer than people to do it, a business keeps choosing which orders to take, at what price and start date, with margin and penalties at stake. The model picks the orders with the best expected margin, after taking off for the risk of a big loss, for unreliable delivery and for depending too much on one client. It never books more people or skills than you have. Backtested On synthetic data it kept more margin for the same risk of loss than a simple first-come, highest-price rule.

Clients we can name

Sports data for 100M+ users, retail back office, healthcare, fintech and payments, mortgages, govtech, SaaS, video, fashion and HR technology.

  • Information System · Software

    Livesport

    We built nine internal systems for Livesport, one of the most visited sports-results services in the world, with more than 100 million users.

  • Back office · Retail

    Alza

    Alza's main back-office system, which runs the whole business of more than $1bn for thousands of employees. Logistics, warehousing, accounting, operations, tasks, BI and marketing all live in it. Custom-made, in C#, where most companies would buy SAP.

  • Design · Software

    Stylitics

    Product design and software engineering.

  • Software

    Guaranteed Rate

    Software engineering.

  • Healthcare · SaaS

    Reify Health

    Many years of work on Reify Health's products, across many different systems.

  • SaaS · Software

    Crossbeam

    We built Crossbeam's partner-ecosystem platform together with their team, through product-market fit and fast growth. Their Director of Engineering tells the story on video.

    Visit
  • Software

    Cloudpermit

    Software engineering.

  • Design · HRTech · Software

    Frankie

    HRTech product design and engineering.

  • HRTech

    Lifesize Video & Audio

    Video and audio infrastructure.

  • FinTech · Mobile

    Zimpler

    Fintech and mobile engineering.

  • Blockchain · Design · Software

    Cicada Wallet

    Blockchain wallet engineering and product design.

  • Software

    Gitrevio

    Our own product. It shows a CEO, CTO and CFO where the engineering money goes and what the business gets back for it.

    Visit
  • FinTech · Software

    Starcity

    We moved payments out of a monolith into their own service, and automated payouts from an external payment provider to clients' bank accounts.

Bring us a decision

Tell us about the decision or the problem in your operations. Jiri or Ivo will tell you how we would solve it.