A new imperative in vertical SaaS: built-in AI features

Restaurants love Toast because it focuses on their problems:

Toast is a VSaaS (vertical software-as-a-service) - software purpose built for unique industry workflows. This means faster time to value compared to generic "horizontal" solutions. For example, Toast has a dedicated "Pizza POS" system with easy topping swaps! A pizzeria wouldn't want to reinvent that wheel - but they also wouldn't want to pick a POS system that doesn't support it.

For the roofing industry, you could customize Salesforce to track "Roof Area" - or you can use a roofing CRM like Acculynx. There is a wonderful explosion of examples like this - companies that build exactly what their clients need, because they ONLY build for those clients:

• bloomerang - for non-profits
• mindbody - for wellness centers and spas
• ALINE - for senior living
• archy - for dental offices
• and hundreds more.

With growth comes competition, and the question is: what's next for Vertical SaaS?

I would argue it is incorporating AI features that are specific to their industries. For VSaaS companies that haven't yet incorporated an AI feature, or are looking to upgrade beyond first party data-powered features, there are many options on the market. Here, we organize them into 3 levels - purpose-built, data warehouse-native, and low-level API. Each is useful for different use cases and organizations.

Traditional focus on control points

Best-in-class VSaaS players have been focusing on capturing "control points". The control points are "back office" and "front office." Back office is the system of record, and VSaaS often starts by trying to become this - the "last system you turn off". Acculynx tracks roofing jobs and leads, while another Xero, an accounting VSaaS, keeps the books. From there, they can focus on integrating with or directly providing front-office technology - whatever the employee or customer interacts with. Every adult in the United States has probably held a Toast POS in their hand.

The new imperative - built-in AI features

What's next for VSaaS? How can you extend further into the value chain - or compete with an incumbent who has captured a control point? How can you avoid ChatGPT-based extinction? One possibility is adding AI features specifically tuned for an industry, which only VSaaS can provide.

There are at least 2 reasons VSaaS is well positioned for AI.

• Data. As a system of record, a VSaaS might be the only place a business can get the data it needs to make a critical decision or prediction.
• Semantic layer. Since VSaaS understands the semantics of its vertical - in other words, it knows what pizza toppings or roof sizes mean - it can automatically build and use AI models.

As Tidemark puts it, "flip your product from being reactive to proactive." For example, Acculynx rates every roof lead 🟢 🟡 🔴 and doubled conversion rates for clients who focused on green-light leads.

Taking the first step

Assuming building AI into their product is critical path, VSaaS vendors must decide where to start. It can be helpful to think of the options as organized into levels from most out-of-the-box to lowest level. Different VSaaS companies will find different levels to be the most appropriate.

Level 2 - easiest - purpose built AI

The most accessible way for VSaaS to incorporate AI into its products is to adopt an existing lower-level component whose unit of prediction is the same - one that is purpose built for predicting against people, products, or search. For example, any system that deals with people can build predictions about their behavior by using Faraday's business outcomes. A system in charge of inventory can add intuitive AI-based search by incorporating Bloomreach's product search. Systems for managing creative assets can build next generation functionality by incorporating AI features like Cloudinary's automated image editing and generative background replacement.

Level 1 - data warehouse native

Going a level down, VSaaS looking to develop its own AI capacity outside of what's already on the market in level 2 should evaluate the machine learning components built into data warehouses. These are not just for one-off analyses; entire applications can be developed on top of them. This level sits at the sweet spot between specific units of prediction in level 2 and raw HTTP APIs in level 0. Generally controlled by SQL commands, they treat structured, unstructured, and even file/object data as tables and allow feature engineering, modeling, evaluation, and prediction as SQL queries. They are designed to process data at any scale, automatically shuffling data and managing memory without requiring manual partitioning or configuration by users. So, if you build a solution on top of them, you get scaling essentially built in.

Almost all data warehouses now allow you to do generative AI. This is much more attractive for batch use cases than creating error-prone HTTP connections to LLM APIs across many rows of data (you wouldn't use this for building chatbots that require low latency - see level 0). Don't forget about predictive use cases too - time series modeling, for example, is available on all of the major data warehouses and allows sophisticated projections with built-in and custom holidays. SQL-based classification and regression using XGBOOST and random forest are bread-and-butter use cases that fit perfectly with tabular input and output data; once you've done feature engineering with standard SQL functions, it's hard to imagine a simpler way. Unsupervised clustering is also available.

Level 0 - raw API

A very rich set of services, from Google's Vertex AI to OpenAI's Enterprise-friendly models, exist as HTTP APIs. This is the lowest common denominator and is accessible from all systems. It is also particularly appropriate for just-in-time calculations such as generative prompts used by chatbots or online fraud predictions using random forest models mounted alongside feature stores for instant response. When planning an AI feature for a VSaaS, it might seem necessary to master an entirely new API. However, low-level systems are often better suited for specific real-time use cases, while asynchronous or batch workflows should first explore the potential of level 2 or level 1 solutions.

Choosing a level

A few questions can suggest where to start:

• Is there already an out-of-the-box prediction infrastructure for your use case? Companies should always evaluate level 2 purpose-built solutions before committing to building low-level systems.
• What is the time between a prediction being created and its use? For example, if you're incorporating search, people expect results immediately; using an existing level 2 vendor may be the fastest approach. However, if no solution aligns with your industry needs, you may need to skip Level 1 and develop a level 0 solution. Conversely, if you plan to rank a lot of records in asynchronous batch use cases, a level 1 solution is likely sufficient.
• Is there industry data that can extend a client's own data? For example, fraud detection vendors incorporate geographic fraud data in their scores. Faraday has a national dataset of all US consumers that automatically enriches predictions made on its platform.
• What kind of scrutiny will your AI receive? For example, if you are making predictions about people, consider how you will implement Responsible AI. Some level 2 vendors automatically build in reportable controls and metrics; some level 0 vendors let you build your own.

The imperative

If you were starting a VSaaS product from scratch, what would be your wedge? Some form of AI is a good guess. And if you are a VSaaS incumbent, how do you defend your position? You have an advantage in the data and semantic layer you already hold. A thoughtful approach to AI infrastructure options can boost your efforts and minimize your time to value.