Verticalized AI Stacks & How to Build One

Sachin's take: Now is the ideal moment to develop a Gen AI product tailored to a specific industry, domain, or vertical. Two critical factors will distinguish the winners:

Industry experts with deep domain knowledge, bringing invaluable insights and expertise.
Proprietary data that delivers exponential value and significant impact, setting apart the product in terms of effectiveness and innovation.

Introduction

OpenAI has made a profound impact worldwide, with consumers and businesses alike integrating it into their personal and professional workflows. While OpenAI excels at addressing generic and widespread challenges due to its specific training and architecture, there exists a growing demand for vertical AIs tailored to solve specific problems within distinct industries or domains.

Vertical AIs are like domain experts

In the past 6-9 months, I've observed a trend towards verticalization in AI stacks. Companies are:

Incorporating proprietary or vertical-specific content into their systems.
Developing models using both generic and vertical-specific data sources.
Designing workflows tailored to the specific needs of different verticals—for instance, the distinct workflows required for sales professionals versus lawyers.
Launching applications with user experiences finely tuned for the unique requirements of specific verticals.

This is great for the industries as they will experience growth and efficiency.

What does it take to build such products

There is a no secret recipe to build these product. But I think startups should have two things

Proprietary data
Domain expert founder or core team member

Proprietary Data

While every model initially trains on standard public data from the internet, within a year, these models can become indistinguishable "me-too" products. Companies possessing exclusive, inaccessible data will hold a significant advantage in the next 2-5 years.

Domain Expert

It's crucial to have a founder or early team member with industry experience, especially when developing products for traditional sectors such as insurance, legal, or pharmaceuticals.

This member:

Understands the industry's challenges.
Identifies buyer, payer, and user personas.
Helps navigate regulatory hurdles.
Secures the first set of customers to alpha test the pre-MVP version of the product.

Vertical AI Maps

VCs across the world are evaluating and investing in vertical AIs. They have built their own maps for the industries that they are interested in. Here are a few examples

It's time to develop ground breaking AI products that will revolutionize multiple industries. If you are building or interested in this space, contact me.

Contact Sachin