Article: Why a Custom AI Beats Generic Models in M&A and Private Equity

Why a Custom AI Beats Generic Models in M&A and Private Equity

Generic AI can certainly lend a hand in M&A workflows. Need to build a first-pass view of an industry or a target company? It can crunch through vast amounts of content and provide a summary. For some use cases, that’s good enough.

But for a field as intricate and context-driven as Finance, especially in M&A or Private Equity, generic models fall short. Why? Because Finance isn’t just numbers - it’s structure, logic, and nuance. And generic AI just isn’t built for that.

Using a generic AI in M&A is like casting a fishing net in a swimming pool.

The Cutting Edge of a Custom AI

A tailored AI model, trained specifically for Finance, brings domain knowledge and technical architecture together. It doesn’t just understand financial language - it interprets it with the right context. Whether you’re analysing a target company or comparing competing bids, a custom AI knows how to structure its answers according to the deal type and the key considerations.

This means:

• More relevant analysis: It knows what files to look at and how to extract the “so what.”
• Less noise: You're not reinterpreting vague or surface-level outputs.
• Greater accuracy: It maps data to the right logic for your investment decision.

What Else? If you consider the high-water mark for any investor is a systematic, structured investment process, a customised AI is the only solution that will provide that in a meaningful way. Prior deal context, benchmarks, process frameworks become your edge. With kicker.cloud you build institutional knowledge that compounds with every deal.

A generic AI doesn’t remember. It can’t store or organise deal files in a meaningful way, so there’s an element of re-inventing the wheel for each deal.

How does that translate for Custom AI users?

• Consistency: No need to re-tune the AI every time the model updates.
• Scale: You have the ability to analyse 100+ documents, not just 10.
• Reliability: With built-in safeguards, answers are grounded in your data, not guesswork

So the way we think about it - a generic AI is akin to getting a summer intern on the team, whereas with a custom AI, you’re getting an Associate with an MBA.