Periodic Reporting for period 1 - The Digital R&D team member (A Digital R&D team member, automating scientific knowledge handling, allowing European R&D to increase their speed of innovation)
Período documentado: 2023-05-01 hasta 2024-04-30
From a more commercial perspective, our clients and users' pain points are the intersection between an exponentially growing body of human scientific knowledge, increasing pressure to stay competitive through R&D and innovation efforts, and a realisation that while most other industries and departments are receiving digital makeovers, R&D is unusually stagnant in its ways. Our clients are R&D-heavy organisations in the chemical, pharmaceutical, MedTech, biotech, and material science industries. Their core operations depend highly on scientific content, from papers and patents to internally produced proprietary research. They have heavily staffed teams where most contributors will have education at least at the PhD level and can spend up to 40% of their time searching for aggregating and cleansing data. With a rapidly increasing body of content (6,000 papers and 8,000 patents a day, growing at 5% p.a.) their problem of accessing the proper knowledge at the right time to push innovation forward is becoming insurmountable. These companies operate in industries with heavy competition and depend on their scientific breakthroughs to stay ahead of their competitors. This hard-to-solve conundrum is causing a significant problem for these players. Navigating all these knowledge issues falls into two categories: One, they can or are required to do incredibly time-consuming tasks. For example, systematically reviewing new literature and extracting key findings. Two, tasks that are so time-consuming they cannot do them at scale even when they know it would yield significant value. Tasks include searching for and extracting detailed scientific information from competitor patents to gain a competitive advantage or covering all ground needed when developing new products. No matter the fundamental premise of their problem, the activities they want to, need to, or wish they could undertake falls into a basic category: Unlock insights from documented research.
Our current tools are just the beginning. We have a grand vision for the future of Iris.ai. We are working towards creating a fully-fledged scientific assistant, a tool that can answer questions about data sets, guide users through their processes, ask the right questions, bring novel insights or interdisciplinary knowledge, and unlock discoveries. This is not just a dream. It's our roadmap for the future.
Over the past year, we have successfully built this from scattered tools and a range of TRL6 technologies into a fully-fledged market-leading research platform. The Workspace is content agnostic, and clients can load, add, or connect various knowledge sources - external and internal. We provide full privacy and security and do not use our clients’ content, data, or inputs for anything but their process, which is a must-have that few competitors have. Research demands rigorous attention to facts and no room for hallucinations, and we go beyond LLMs to enforce the ground truth. We focus on optimal human-machine collaboration, but when something is repeatable enough to be automated, it can be automated. We use smart, not large, language models where they suffice. Better for pricing and the environment. Finally, the ML models are rapidly reinforced in the client’s research field to handle their unique vocabulary context. This means the solution is rapidly scalable to various challenges, from core R&D via medical regulatory affairs to IP analysis.
In addition to bringing these tools to enterprise readiness and executing the pilots, both are works in progress. We are working actively to create our pipeline for commercial development, a key project component, and secure longer-term funding for our growth phase. Both processes are well underway.