.1AI Across IndustriesApril 28, 2026 Karina How Pharma and Life Sciences Are Using AI to Accelerate Knowledge WorkPharma generates vast amounts of research data – but most of it remains unused. This creates costly inefficiencies and slows innovation. The real challenge isn’t creating knowledge, but accessing and activating it. Discover how AI, powered by the right infrastructure and context layer, turns static documents into usable intelligence.
.2Use CasesJuly 25, 2024 Aashima Driving Transformation Across Industries with Iris.aiIn today's rapidly evolving, data-centric world, innovation is the cornerstone of progress across all industries. From healthcare and agriculture to manufacturing and research, organizations are constantly seeking ways to enhance efficiency, solve complex problems, and uncover new opportunities.
.3EventsJune 2, 2022 Ada WEBINAR: Iris.ai and Materiom – extracting, linking and systematizing dataOn May 25th 2022, we hosted a webinar together with Materiom where we shared details about our collaboration and the project. Here, you can read more about it or watch it below.
.4Use CasesMay 19, 2022 Ada Materiom partners with Iris.ai to create a database for regenerative materialsIris.ai has recently partnered with Materiom to help building the world’s largest database and research community of material science knowledge to aid the transition away from petrochemicals.
.5AI Across IndustriesNovember 4, 2021 Ada Why is a horizontal approach to AI the way to go? AI across domainsAI can be applied in two different yet equally important and complementary ways: vertically and horizontally. In layman’s terms, vertical AI is applied to a specific problem in a specific industry. It is trained exclusively on that industry-specific data. On the other hand, horizontal AI can be applied across different industries and is able to handle multiple tasks.