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Spreadsheets, emails, repeated data entry, and manual approvals may work for a while, but they often become barriers as laboratory operations grow. If your team spends too much time tracking status, searching for information, or managing disconnected processes, it may be time for a different approach.[Read More]

A LIMS is easy to buy but difficult to implement. Success depends on decisions made early—often before a contract is signed. A LIMS makes the sample the unit of record, linking every transfer, method, instrument reading and approval in a traceable history. How well it works ultimately depends on four key decisions made before purchase.[Read More]

Modern drug discovery relies on vast amounts of proprietary data, making R&D environments prime targets for cyber threats. Protecting genomics, screening, clinical, and AI-driven systems requires more than traditional defenses. Domain-aware cybersecurity is essential for safeguarding IP, data integrity, and research continuity.[Read More]

LIMS upgrades can take anywhere from 1–18+ months, depending on scope, resources, and lab-specific requirements. This blog explores the key factors that drive delays and practical strategies to accelerate execution, including data cleaning, out-of-the-box functionality, dedicated backfill staff, and risk-based Computer Software Assurance (CSA).[Read More]

The FDA and EMA’s 2026 Good AI Practice principles establish a framework for responsible AI use across the drug product life cycle. Regulatory Affairs is uniquely positioned to lead AI governance while ensuring GxP compliance, data integrity, and regulatory confidence. The challenge: accelerating AI adoption while meeting evolving compliance standards.[Read More]

Our team attended the July 29, 2026 PHUSE webinar, “Building a Trustworthy RWD Analytic Platform,” presented by SAS. The session highlighted modern RWD/RWE architecture considerations. Below is our perspective on the webinar’s key themes and how Kalleid supports validation of RWD analytics environments, with a focus on reproducible, compliant, transparent, and audit-ready outputs.[Read More]

Lab automation pays when a task is high volume, low judgement and methodologically stable. When any condition is missing, exceptions can erase the savings. That’s why candidate selection matters more than technology choice. Labs often prioritize tasks by how annoying they are—a poor predictor of automation’s return.[Read More]