Healthcare and life sciences organizations have access to enormous volumes of information, yet finding the evidence needed for a critical decision can remain difficult. Clinical records, scientific literature, safety narratives, and regulatory documents often reside in disconnected systems. Bringing them together requires an understanding of the information itself, the processes surrounding it, and the standards it must meet before it can be used. Straive helps organizations address this challenge by combining life sciences expertise, technology, and operational delivery. Its focus is not simply on developing sophisticated tools, but on making information easier to interpret, reducing manual workloads, and supporting decisions across complex scientific and business processes. “We describe ourselves as a One-stop Partner for Data & AI Operationalization,” says Rishi Raina, Vice President & Senior Client Partner, Straive. This approach underpins the company’s “Build AI, Run AI” philosophy, which connects solution development with implementation, monitoring, and continuous improvement.

Straive serves a broad range of healthcare and life sciences functions, including research and development (R&D), clinical analytics, pharma co-vigilance, supply chain, quality and regulatory compliance, medical affairs, and commercial analytics. Its services include scientific content analysis, document processing, data curation, and decision support. Rather than applying the same solution to every situation, the company tailors its work to the decisions clients need to make and the processes they want to improve. For a safety team, this might mean identifying a potential adverse event in a clinical narrative and tracing it to the supporting evidence. A supply chain team, meanwhile, may need to identify savings opportunities, evaluate possible actions, and improve planning. Although the objectives differ, both require reliable information that can be accessed and assessed without expensive changes to existing systems. These challenges become more complicated when relevant material is scattered across structured records, clinical notes, scientific publications, and separate document repositories. Inconsistent terminology and poor data quality can undermine analysis, while regulated activities demand careful review and traceability. Straive begins by examining the task at hand, identifying the information required, and determining where improvements can make the greatest difference. It then connects relevant sources, establishes quality controls, and introduces automation where appropriate.

Scientific and regulatory judgment remains essential throughout this process. Identifying a possible adverse event, for example, is only the first step; professionals must also examine the supporting evidence and assess the result. Straive’s AI verification architecture principles accommodate expert review and help maintain a clear record of how outputs were produced. The intention is to reduce repetitive work while preserving the scrutiny required in high-stakes environments. A drug-labeling engagement illustrates the operational benefits. One life sciences organization was processing large volumes of documents, requiring employees to classify information manually and complete attributes before uploading files to a document management system. Straive developed a natural language processing solution trained on the client’s material and integrated it into the existing workflow.

According to a recent client incident, manual effort fell from more than 45 minutes to approximately two minutes per document. Entity-extraction accuracy exceeded 90 percent, while reported annual savings reached $5.2 million. The results demonstrate how a targeted intervention can reduce administrative effort when designed around a specific operational bottleneck. In fact, Straive’s involvement continues after implementation. As requirements change, deployed solutions need monitoring and refinement to remain useful. The company develops reusable tools and domain-informed workflows, adapting them to individual clients’ processes, controls, and requirements. “That ongoing responsibility is what ‘Run AI’ means to me,” Raina explains.

Looking ahead, Straive sees opportunities to connect activities across clinical development, safety, regulatory affairs, and commercial operations. Advanced analytics and AI agents could help teams review larger volumes of material and coordinate routine tasks. The company is also focusing on improving verification and optimizing model costs, with the broader objective of making emerging capabilities practical for sustained use rather than limiting them to demonstrations. Straive was also recognized as a Leader in AIM Research’s 2026 assessment of AI service providers for life sciences and healthcare. This recognition accompanies a growing need for solutions that integrate with established processes, produce reviewable results, and deliver measurable operational improvements.

For Raina, successful adoption starts with a business problem rather than a predetermined technology. He encourages healthcare and life sciences leaders to identify the decisions that matter, establish what evidence their teams need, and determine where that material resides and what delays its review. These questions create a practical basis for defining objectives and measuring results. Ultimately, the value of technology in life sciences depends on what people can accomplish with it. Faster processing matters when it frees specialists to concentrate on interpretation, review, and decisions that require their expertise. As Raina concludes, “A faster answer has limited value unless the team can trust it, understand how it was reached, and use it responsibly.”