Manual record sifting meant trial matching took hundreds of days. AI agents now identify eligible patient cohorts in 24 hours.
A clinical AI platform supporting pharmaceutical research handles complex data tasks like chart reviews and patient cohort analysis.
Matching patients with clinical trials traditionally takes hundreds of days, causing 80% of trials to miss their enrollment goals. This bottleneck...
“We’re about to make a huge leap in patient outcomes. Open source models help companies innovate quicker by letting them concentrate on their specific needs and add advanced reasoning capabilities to their AI stacks.”
AI platform for transforming unstructured medical records into structured clinical data.
Social technology company developing AI frameworks, VR hardware, and digital platforms.
Related implementations across industries and use cases
Selecting trial sites took months of manual data collection. AI now generates lists in 24 hours, cutting activation cycles by 10%.
PhDs spent half their time on paperwork. Agents now draft filings in minutes for scientists to verify.
Building disease models meant three months of complex mapping. Pre-training LLMs on 200B tokens cut the cycle to just four weeks.
Silos forced teams to manually chase data. Now, AI agents identify adverse events in seconds, eliminating weeks of cross-referencing.
Attorneys juggled fragmented tools to search 50,000 cases. An AI orchestrator now routes queries to sub-agents in a single chat.
Experts spent 15 minutes pulling data from scattered systems. Natural language prompts now generate detailed reports instantly.
Lab supply orders were handwritten in notebooks. Digital ordering now takes seconds, saving 30,000 hours for research annually.