A sense of the range — across life sciences, public health, law, and consumer. Clients are kept confidential, so each engagement is described generically.
RBQM analytics across 15 oncology studies (with companion diagnostics), support for a transition to an AI platform, an LLM-powered application for evidence generation across historical trial data, and GenAI training so cross-functional teams could adopt AI in day-to-day evidence workflows.
AI-driven pipelines that strengthened the analytical and clinical validation processes for an FDA 510(k) diagnostics program — improving efficiency and communication across teams.
Evaluated and validated a public-health AI system — assessing the accuracy and actionability of AI-generated population-health insights and recommending adjustments to maximize impact on operational decisions.
Rapid-turn analytics pipelines and AI NLQ dashboards for decision support across the lead → conversion → referral → milestone funnel — letting non-technical staff ask questions in plain language, with reproducibility, QA, and stakeholder-ready outputs throughout.
An AI engine for automated, human-in-the-loop report generation from clinical and bioinformatics data; a multimodal conversational agent for clients; and an NLQ dashboard for clinicians — agentic LLM workflows behind all three.
A conversational "going-out" guide that surfaces an interactive map of nearby events, personalized to each user's location and travel-time/distance constraints and to a taste profile learned from event RSVPs and conversation — agentic AI + geospatial analytics + hyper-personalization over a real venue/events database.
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