KLDiscovery Inc (KLDI) Fair Value & Analysis
Technology · US · Market cap $5.7M
Fair value as of: Jun 26, 2026
Analysis
KLDiscovery Inc (KLDI) currently trades at $0.0050, while our model-based Fair Value estimate is $0.0050 — implying the stock looks roughly 0.6% undervalued today. We read business quality at 89/100 (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: low) — always confirm before acting.
About the company
KLDiscovery Inc. provides eDiscovery, information governance, and data recovery solutions to corporations, law firms, insurance companies, and individuals worldwide. The company offers Nebula, an end-to-end eDiscovery solution that facilitates smarter ways to cull, process, review, and manage documents in an intuitive interface; Client Portal for consolidated visualizations and reporting for portfolio intelligence; KLD Processing, a proprietary processing application; ReadySuite to perform extensive QC on a production, normalize inbound submissions, or spot check the work of a colleague or supplier; Relativity for relativity enhancements and state-of-the-art HIVE infrastructure; Nebula Processing to process data with a higher degree of quality; and Nebula AI, a technology assisted review tool combined with a deep bench of experts, as well as managed services, remote document review, and managed document review services. It also provides computer forensics, ransomware data recovery, …
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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.