Illustrative engagement across five service pillars and five industries, reflecting representative Meruksha engagement patterns.
Industry: Biotechnology
Client Profile
The client is a mid-cap, clinical-stage biotechnology company backed through a Series C round, employing roughly 400 people and preparing to advance three therapeutic programs into Phase III trials simultaneously. The organization operates in a heavily validated IT environment typical of clinical-stage life sciences firms, where every system touching trial data falls under GxP and computer system validation requirements.
Challenge
As the company scaled from Phase II to three concurrent Phase III programs, its clinical operations team found itself buried in manual cross-referencing work. Protocol documents, adverse event reports, IRB correspondence, and regulatory submissions were scattered across SharePoint folders, shared mailboxes, and a legacy clinical trial management system (CTMS) that had not been meaningfully updated in years. Clinical operations staff were spending upward of 15 hours per week manually searching for protocol deviation precedents, cross-checking adverse event patterns across sites, and drafting regulatory correspondence from scratch each time a similar issue arose. Leadership recognized the opportunity for an AI-powered assistant, but the company had already spent six months and a meaningful budget on a proof-of-concept from an external AI vendor that never made it past the sandbox stage — largely because the vendor’s tool was built outside the company’s validated environment and triggered a fresh computer system validation cycle that stalled the project indefinitely. By the time Meruksha was engaged, there was real organizational skepticism that an AI tool could actually reach production in a regulated environment.
Approach
Meruksha began by scoping the engagement narrowly rather than broadly, identifying three specific high-friction workflows — protocol deviation lookup, adverse event pattern queries across trial sites, and first-draft regulatory correspondence — instead of attempting a general-purpose clinical assistant. A retrieval-augmented generation (RAG) architecture was built to index the company’s existing protocol documents, IRB correspondence, and adverse event logs, with role-based access controls implemented from the outset so that access to sensitive trial data remained tightly scoped by role and program. Critically, the system was architected and deployed inside the company’s existing validated IT boundary, working closely with the company’s quality and IT validation teams so the tool inherited existing controls rather than requiring an entirely new validation cycle. Development proceeded in two-week sprints with clinical operations staff acting as active reviewers at every stage, testing real queries against real historical data before anything reached broader users. This iterative, compliance-aware approach stood in sharp contrast to the prior vendor’s black-box sandbox model.
Outcome
Average protocol deviation lookup time fell from approximately 40 minutes to under 5 minutes per query. More importantly, the copilot moved from initial build to full production use across all three concurrent Phase III programs in just 10 weeks — faster than the prior vendor’s six-month sandbox phase that never reached production at all. Clinical operations leadership specifically credited the decision to build inside the validated environment, rather than around it, as the difference between a tool that shipped and one that stalled indefinitely in review.