At kicker.cloud, many of us started our careers in finance. We know firsthand how the excitement and intellectual thrill of dealmaking can be drained by an avalanche of repetitive, menial tasks - even for the most enthusiastic and driven individuals.
So, when we set our sights on building a tech company, we honed in on M&A and Private Equity, but we made sure our problem statement was broad.
We wanted to make our former selves and the current M&A community feel better in the thick of a deal, with their senses sharpened and their brain whirring, with the overall business thriving because of it. Yet, we knew that while technology had certainly made a step-change to facilitate this, a step-change would only be felt if the entire M&A process - from origination to deal execution to post-integration - was targeted, given the deep interconnectedness of the deal lifecycle.
Optimising one area while ignoring the rest would be like strengthening your strikers while leaving your defense wide open.
So, we built an end-to-end solution.
The beauty of this end-to-end approach is that it also gives the AI model a superpower. While it may not feel anything itself, it does create a single, unified AI operating system.
This means it can search, triangulate and process data across sourcing, analysis and PMO, gaining the advantage of feedback loops and metadata across the deal lifecycle. In effect, it can generate responses that are akin to an Associate working across a deal instead of an Analyst working on siloed tasks.
What does that mean practically? In our view, it creates many benefits, but to provide two examples:
Questions:
Actions:
Outcome: Less time spent on bad deals, deeper analysis on the ones we want to move forward with
Author: Gavin Crawford
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