Vertical SaaS Is a Trap. Here's What Replaces It.
Vertical SaaS platforms have been selling the same dream for twenty years. One place for your risks, your contracts, your workflows, your reports. A single system for your entire function.
The dream is compelling. The reality is a data entry system wrapped in a six-figure renewal.
I ran into this directly working on compliance and risk programs. But the problem is not unique to that space. Any function that requires judgment faces the same trap: legal, finance, HR, operations. The platform models your work as the vendor understood it when they built it. When your reality diverges from their model, you lose.
Why Vertical SaaS Fails Functions That Require Judgment
The pitch is always the same. Pre-built templates. Automated workflows. Dashboards for leadership. Coverage across whatever frameworks or regulations you care about.
Here is what that actually means in practice.
The templates are starting points. Someone still has to populate them, maintain them, and update them when your environment changes. The platform does not do that work. It gives you fields to fill in.
The automated workflows automate a narrow slice. Everything else gets handled by hand: the edge cases, the exceptions, the things that do not fit the vendor’s data model. Usually by exporting data into a spreadsheet and doing the work outside the system you paid for.
The dashboards look like enterprise software from 2015. Rigid templates. Vendor branding. Customization is a professional services engagement, not a configuration option.
And when AI gets more capable, you wait on the vendor’s roadmap to access it.
The Real Problem Is the Ceiling
Vertical SaaS creates a ceiling. The vendor’s data model, their roadmap, and their pricing define what is possible. Your needs grow. Their assumptions do not. You file a support ticket or write a check.
Your data is inside their system. Your workflows are inside their system. When you need to do something the tool was not designed for, you are working against the grain of software you are paying to use.
The Alternative: A Repo-Based Model
Here is what a different approach looks like.
Your function’s data lives in a shared code repository. Risks, contracts, workflows, assessments. Structured data and scripts, version-controlled by the whole team. Every change is a commit. Every output is reproducible. When someone asks how you made a decision a year ago, you check out the tag and run it.
Two things make this work at scale now in a way that was not practical before.
Skills are reusable task definitions. Think of them as documented workflows your AI model knows how to execute. Run a vendor assessment. Generate a monthly report. Produce an audit-ready summary. Each skill encodes the steps, the data sources, and the expected output. You run them on demand or on a schedule.
MCPs (Model Context Protocols) are the connections. They let your AI model reach directly into your actual systems (your ticketing tool, your documentation platform, your cloud environment) to pull data, create records, and update status without manual translation. No more copying findings from one system into another. The model handles the integration.
Put them together and you have a function that executes on demand, reasons about what it finds, and produces outputs your team can use.
Why This Wins Long-Term
A repo has no ceiling. When your needs change, you change the code. When AI models get more capable, your function gets more capable with them. You control the integration, not the vendor.
Every major vertical SaaS category is vulnerable to this. The functions with the most structured data, the most templated workflows, and the most predictable outputs are exactly the ones where this model performs best. Compliance is obvious. Finance ops is next. Legal ops is close behind.
The platform was never the point. The work is.