Would a Ferrari be worth the price tag if you had to assemble the parts yourself?
There's a trend of AI vendors comparing themselves to cars (think Ferrari, Rolls Royce, F1). This is not a bad analogy. LLMs are like car engines: they are powerful machines, but an engine sitting in a garage won’t get you anywhere without a body, wheels, wiring, and, today, a whole operating system to manage that power.
It's the responsibility of car manufacturers - and software developers - to select the right engine for the job and build the best vehicle for their market.
At last year's Knowledge Management & Innovation for Legal Conference, I had an illuminating conversation with the CKO of an AmLaw 25 firm. They wanted to automate data extraction with generative AI, but every product in the market was a generalized platform requiring users to draft all the prompts themselves.
That's not a car. That's a build-your-own car kit.
A year later, the Centari team had the pleasure of being back at KM&I to share our perspective on data-driven dealmaking. We've built the full car, combining LLMs, data science, and proprietary methods to achieve accuracy and real world utility with no prompt engineering required by users.
As I shared at the conference, the tech adoption curve isn't static. By now, most AmLaw 200 firms have crossed the chasm of experimenting with generative AI in some form. Now the industry is on a new curve, where firms reach for higher branches: specialized applications of gen AI for complex workflows like deal point extraction, with a clear demonstration of ROI and competitive significance.
At Centari we are laser focused on providing our customers with the most advanced Deal Intelligence platform in the market. And they can drive it off the lot.
