AI that handles routine support inquiries and analyzes customer sentiment at scale. These systems resolve common questions via chat, route complex issues to agents, and surface insights from feedback. The result: 24/7 response, lower support costs, and agents focused on what matters.

AI models analyze customer messages, tickets, and calls to detect sentiment, emotion, and urgency across every service interaction. These insights help teams prioritize at‑risk customers, tailor responses in real time, and surface systemic issues driving dissatisfaction. The result is higher CSAT, faster resolution, and reduced churn through data-driven customer care.

AI Customer Interaction Orchestration centralizes and automates customer-service conversations across chat, messaging, and other digital channels. It uses conversational agents to resolve standard inquiries, guide complex cases, and adapt responses to each customer’s context and history. This improves customer satisfaction while reducing support costs and freeing human agents to focus on high‑value issues.

This AI solution uses AI to automatically score, prioritize, and route customer service tickets across channels like email, chat, and helpdesk platforms. By intelligently triaging issues based on urgency, impact, and customer context, it ensures the right agent handles the right case at the right time, reducing response times and improving customer satisfaction while minimizing manual queue management.

AI Customer Service Chatbots handle live customer inquiries through automated, conversational interfaces across web, mobile, and in-app chat. They deflect routine tickets, provide instant answers, and can escalate seamlessly to human agents, improving response times and CSAT while lowering support costs. Businesses gain scalable 24/7 support that reduces queue volumes and frees agents to focus on high‑value interactions.

AI Support Ticket Orchestration automatically classifies, routes, prioritizes, and updates customer service tickets across platforms like Zendesk. It ensures that each issue reaches the right agent with the right priority, reducing handling time, improving response and resolution SLAs, and boosting customer satisfaction while lowering operational overhead.

This AI solution covers AI tools that make customer service channels more accessible, responsive, and consistent across help desks, IT support, and omnichannel CX platforms. These systems automate routine inquiries, surface the right knowledge instantly, and adapt interactions to users’ needs, improving resolution speed and service quality while reducing support costs.

This AI solution uses advanced conversational AI to automate customer service interactions across chat, email, and help desks. It resolves common inquiries instantly, routes complex issues to human agents with full context, and delivers consistent, scalable support, improving customer satisfaction while reducing handling time and support costs.

This application area focuses on using advanced analytics to support clinical decisions across the cancer care pathway, from diagnosis through treatment selection and monitoring. It integrates heterogeneous data sources—such as genomic sequencing results, pathology, medical imaging, and electronic health records—to generate structured insights that help clinicians interpret complex findings and choose the most appropriate interventions for each patient. It matters because oncology increasingly depends on precision medicine, where treatment effectiveness hinges on nuanced biomarkers and molecular profiles that are too complex and voluminous for manual review at scale. By automating variant interpretation, risk stratification, prognosis estimation, and therapy or clinical-trial matching, these systems reduce diagnostic bottlenecks, improve consistency and quality of care, and enable more personalized, evidence-based treatment decisions for conditions like non–small cell lung cancer and other malignancies. AI is used to process and classify genomic variants, detect patterns in imaging and pathology, synthesize unstructured clinical notes, and generate ranked recommendations or structured reports for clinicians. The result is faster turnaround, more accurate and reproducible assessments, and better alignment of patients with the therapies most likely to benefit them.

This application area focuses on automating and accelerating the design and operationalization of clinical trials, from protocol authoring through configuration of eClinical systems. It uses advanced language models and configurable platforms to draft structured, compliant protocols, standardize terminology, and translate study designs into executable digital workflows, case report forms, and data capture configurations. It matters because trial design and setup are major bottlenecks in drug development—slow, expert‑intensive, and prone to rework due to regulatory, operational, and data‑management complexities. By systematizing protocol creation and rapidly configuring eClinical environments to match those protocols, sponsors and CROs can shorten study start‑up timelines, reduce change‑order costs, support more complex and decentralized trial models, and improve compliance and data quality across the trial lifecycle.