Empowering fee earners as revenue managers
2023 cohort member of
As featured in
Senior fee earners moonlight as revenue managers. Smart Lockup Assistant makes it easy for them to excel in that second job and achieve unprecedented efficiency.
The Assistant represents a paradigm shift in software: it autonomously brings suggested actions to fee earners, instead of relying on their instructions.
Fee earners with better lockup outcomes
Our Adaptive AI learns revenue management tactics (e.g. propensity to update scope/estimate, to bill or to support credit control, given matter descriptive parameters) of the fee earners with better lockup outcomes and steers the rest of their peer groups to adopt similar approaches.
Our system is assistive by design, so that the human always remains in control. Indeed, our models treat user interactions as an additional input – that creates a positive feedback loop that reinforces the algorithm.
Alerts fee earners of revenue deterioration risks (promoting engagement / avoiding fatigue) & helps take the next step
Delivered via email or MS Teams
Individual performance visibility, available at an unprecedented level of granularity
Admin console and PowerBI integration
Continuously scans matter lifecycle for factors that cause revenues to deteriorate and activates the Agent
Connected to your Practice Management System
We are on a mission to redefine knowledge work. Our core thesis is that knowledge workers tend to, in fact, have more than one job and those multiple responsibilities compete for their attention. Take lawyers as an example – in addition to their core mission of providing client advice, they are responsible for matter management, compliance oversight and business development to name a few. We want to alleviate the burdens of those second jobs whilst keeping knowledge workers in the loop and in control.
We have been there ourselves: our founding team is made up of a former surgeon (turned engineer), a (reformed) investment banker and a scientist-consultant. And we know how to solve this problem: we are building a decision assistance system powered by a blend of advanced machine learning and behavioural sciences.