Progress Software Corporation (PRGS) Fair Value & Analysis
Technology · US · Market cap $1.2B
Fair value as of: Jun 24, 2026
Analysis
Progress Software Corporation (PRGS) currently trades at $28.84, while our model-based Fair Value estimate is $34.76 — implying the stock looks roughly 20.5% undervalued today. We read business quality at 95/100 (high quality), in the Technology sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: high) — always confirm before acting.
About the company
Progress Software Corporation provides software products that develops, deploys, and manages artificial intelligence (AI) powered applications and digital experiences in the United States and internationally. The company offers Agentic RAG, a next-generation Agentic Rag-as-a-Service platform; Automate MFT, a cloud-native Software-as-a-Service (SaaS) platform for automated and secure file transfers; Chef, a DevOps/DevSecOps automation software; Corticon, a decision automation platform; DataDirect, a secure data connectivity tools for Relational, NoSQL, Big Data, and SaaS data sources; Developer Tools, such as software development tooling collection, including NET and JavaScript UI components for web, desktop and mobile applications, AI-prompt components, reporting and report management tools, and automated testing and mocking tools; Flowmon, a AI-powered network security and visibility product with automated response across hybrid cloud ecosystems; and Kemp LoadMaster, an application…
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.