Research · software investing
Software investing
Software investing studies how software companies create durable revenue, workflow ownership and customer value. In the AI era, the category includes traditional B2B SaaS, vertical software, data infrastructure, automation systems and AI-native applications built around new labour and context models.
Core software categories
Major categories include B2B SaaS, vertical software, infrastructure software, data platforms, workflow automation, developer tools and AI-native applications. Each category has different buyers, margins, retention patterns and platform risks.
A useful software market map separates application software from infrastructure, horizontal products from vertical systems, and workflow ownership from lightweight productivity features.
B2B SaaS and vertical software
B2B SaaS companies usually sell recurring software to business customers. Attractive businesses often own a repeat workflow, show strong retention and become embedded in daily operations.
Vertical software narrows the buyer and workflow to a specific industry. That can create deeper product fit, better data context and stronger distribution, but it can also limit market size or require specialised implementation.
Workflow software and AI-native apps
Workflow software becomes valuable when it controls action, not just information. The product sits inside a repeat process, coordinates users and becomes a system of record or system of action.
AI-native applications can extend that logic by turning software from a passive interface into a worker-like system. Research needs to test whether the AI layer improves retention, margins and outcomes, or merely adds demo appeal.
Revenue quality
Revenue quality is usually more important than headline growth. Retention, expansion, gross margin, customer concentration, contract length, implementation burden and sales efficiency all shape the quality of software revenue.
A company with slower but durable growth may be higher quality than a faster-growing company with weak retention or heavy services dependency. Public comps can help benchmark this, but private context matters.
Data infrastructure and automation
Data infrastructure, observability, retrieval and automation are now part of the software investing map because they underpin AI workflows and enterprise decision systems.
These layers can be durable if they become operationally necessary. They can be fragile if they depend on one platform, one model provider or a thin feature that incumbents can copy.
Private-company caveats
Private software companies disclose less than public companies. Metrics can be unaudited, selective or dated. Research should record confidence levels and avoid treating marketing claims as diligence.
This page provides general market research only. It is not investment advice or a recommendation regarding any software company.
How to use this research
Use this Software investing research as a map of the relevant market structure, not as a prediction engine. The point is to clarify categories, buyers, public comparables, private-company signals and unanswered diligence questions before drawing stronger conclusions.
For searches around software investing, the useful output is a working view of the category: what belongs in the market, what should be excluded, which company types are public or private, and which signals deserve repeat monitoring.
Signals to monitor
Useful signals include new company formation, funding rounds, hiring patterns, customer evidence, product launches, public-company commentary, partnership activity, secondary-market indicators and changes in valuation tone across comparable categories.
Signals should be dated and sourced. A funding announcement, website claim or public-market multiple can be useful, but it should be treated as one piece of evidence rather than a complete view of quality, durability or risk.
Public and private evidence
Public-market evidence helps frame margins, growth expectations, valuation cycles, platform power and investor appetite. Private-market evidence helps identify category formation, founder activity, product direction and emerging customer demand before it appears in listed-company results.
The research task is to hold both evidence types together. A private company can look compelling until public comparables show weak economics; a public company can look mature until private-company formation reveals a new competitive edge.
What to avoid
Avoid treating B2B SaaS investing, vertical SaaS investing, software market map as a slogan. Durable research should define the market, separate adjacent categories, record assumptions and keep uncertainty visible. It should also distinguish company marketing from independent evidence.
This page is designed for general research context. It does not claim that Meridian manages capital, advises on securities, operates a fund, has a portfolio or offers personalised financial advice.
Next research questions
The next useful step for Software investing is usually a tighter company universe: named categories, inclusion rules, source links, confidence levels and a dated view of public comparables. That turns a theme into a working research asset rather than a broad narrative.
The second step is continuous monitoring. Categories change when budgets shift, platforms absorb features, financing markets reopen, regulation changes or customer workflows mature. A serious market map should be revisited as those signals move.
FAQs
What is software investing?
It is the study or investment analysis of software companies, business models and markets.
What makes a software company high quality?
Retention, growth, margins, customer value, workflow ownership and efficient go-to-market are common indicators.
How does AI change software markets?
AI can change product interfaces, labour leverage, pricing, defensibility and infrastructure requirements.
Disclaimer
Research content is for general information only and should not be treated as investment, legal, tax or financial advice.