at Index VenturesWe believe that the emergence of vertical SaaS (vSaaS), cloud-based software customized for specific industries, is part of a broader trend in which end users are increasingly demanding great technology offerings.
Consumers want solution-oriented software that is purpose-built to solve their business problems precisely. In a software-infested environment, the specific and specific are in place, rather than the broad generalization.
This concept is not new. Even the largest horizontal technology companies will verticalize their sales organization and product functions if they have enough scale within each vertical and that is a sensible approach.
Cloud giants AWS, Azure, and Google Cloud Platform, along with other large platforms such as Salesforce, ServiceNow, Snowflake, and Workday, feature vertical industry solutions with dedicated sales teams.
These technology leaders will verticalize their products over time. Technology vendors with a deep understanding of the industry, sales and support representatives attending the same conferences as our users, and rapid product evolution to suit our customers ensure a quality experience for our customers and their end users. Because you get needs.
The AI category, which is evolving rapidly, has evolved into three layers: underlying models, AI infrastructure, and AI applications.
With the move to AI platforms, the next logical iteration of vertical SaaS is vertical AI, a vertical bundled in parallel with workflow SaaS built on models independently trained on industry-specific datasets. We believe it will be a type AI platform.
Why Vertical AI?
The AI category, which is evolving rapidly, has evolved into three layers: underlying models, AI infrastructure, and AI applications.
AI stack startup example. (Index Ventures is an investor in Causaly, Cohere, Scale, ServiceTitan and Weaviate.) Image credit: Index Ventures
The underlying model is the foundation of the AI stack. Leaders in this space include Anthropic, Cohere, and OpenAI. Because building and training models requires significant funding, we believe that the number of vendors that can participate in his foundational LLM space is limited.
AI “picks and shovels” reside in the infrastructure layer, which includes various categories such as data augmentation, fine-tuning, databases, and model training tools. For example, vector databases such as Pinecone and Weaviate have been widely adopted.
Other companies like Scale are also used for data generation, labeling and training. Hugging Face has emerged as a leader in model discovery and inference. Weights & Biases is widely recognized within his MLOps. LangChain is an open source development framework used to simplify the creation of new applications using LLM. These are just a few of the many companies helping companies transform their models and data into products.
Foundational Models and Infrastructure Enable Explosive Growth of AI Businesses application. These AI-powered applications can be used by any end-user in any industry to perform a variety of tasks.
