Sergio and Marta's financial computing advances within a major digital bank grabs the attention of a cybersecurity manager who demands their help, ad-hoc. SciTheWorld is born.
They start exploring networks of thousands of servers from a defender ("Blue Team") and attacker perspective ("Red Team") with a trader mindset.
Sergio's Thesis' algorithmic trading platform gets the prize to the Best of the Industry.
He joins SciTheWorld full-time until he finds a way for the platform to become a "Digital Brain".
He then becomes Global Head of Data Science at one of the most laureated digital banks.
Sergio starts the platform that would beat the one already prized by the industry.
Sales are strategically postponed to grant a full focus on Financial Computing R&D - there is a natural bottleneck in its innovation (expertise across finance, tech and science) that can't be tackled by simply adding people.
Marta joins SciTheWorld full-time and underpins the platform to secure its major challenges are solved by the end of 2018.
Even though the industry shows actively interest on several CAT's modules, sales are still postponed to keep SciTheWorld's main resources focussed on the bottleneck.
The partnership with Oliver Wyman becomes official. Commercial reach is exponentially boosted towards 2019.
CAT's agile skeleton is finally shaped. The bottleneck has passed.
Still zero external funding.
Given CAT's state, R&D going forward is easier to manage. It will be improved in terms of both features and autonomy.
The forthcoming challenge becomes the delivery of innovative projects (assets & services) across industries. Often, completely new approaches will be offered - e.g. external audit of algorithms through Red Teams. Rough but niche and fun.
External funding will for the first time be considered.
Looking fwd to seeing how it all starts settling.
Once CAT has been used as the backbone of innovative A.I. assets & services, and further evolved during 2019, the R&D plan during 2020 follows a dive deep into trading's alpha.
CAT should be a game changer to its users. They should be able to onboard complexity into their algo trading at a much faster pace than those who did not pay enough attention to the bottleneck. Hence, the natural step at this stage - to simply help them take over.