Periodic Reporting for period 2 - 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)
Reporting period: 2024-05-01 to 2025-04-30
At Iris.ai we envisioned that if one person could absorb all scientific knowledge, they could solve many of humanity’s greatest challenges. Recognizing human limitations, we founded Iris.ai to build an AI that makes sense of and connects the dots across the vast and growing body of scientific knowledge. We are not just another AI company—we are mission-driven, aiming to transform how science is accessed, understood, and applied.
Our clients—primarily in chemical, pharmaceutical, MedTech, biotech, and materials science—face a critical challenge: the exponential growth of scientific content, increasing pressure to innovate, and a lack of digital transformation in R&D. These organizations rely on scientific papers, patents, and proprietary research. Their highly educated teams often spend up to 40% of their time on manual tasks like searching, aggregating, and cleaning data. With over 6,000 papers and 8,000 patents published daily, growing at 5% annually, accessing the right knowledge at the right time is becoming unmanageable.
These companies face two types of burdens: time-consuming tasks they must do (e.g. literature reviews) and high-value tasks they cannot scale (e.g. extracting insights from competitor patents). Their core need is to unlock insights from documented research—quickly, accurately, and securely.
Through this project, we transitioned from fragmented TRL6 tools to a coherent TRL8 platform—the Researcher Workspace—now deployed in four pilot organizations. It automates document comprehension, contextual interrogation, and knowledge extraction, reducing data-gathering time by up to 40%. The platform is content-agnostic, privacy-first, and adaptable to internal and external sources. It supports on-premise deployment and uses explainable, non-hallucinatory AI to ensure scientific rigor.
The project’s impact has been validated through pilot deployments, technical milestones, and market feedback. Dissemination reached thousands across academia, industry, and media. We secured €7.64 million in investment, submitted 18 approved deliverables, and launched a revised Go-To-Market strategy. With over 2,000 professionals engaged through events and media, we are well-positioned for growth.
Looking ahead, our vision is to build a fully-fledged scientific assistant—an AI that not only answers questions but guides users through research, asks the right questions, and brings interdisciplinary insights. This is our roadmap, and the project confirms Iris.ai’s readiness to scale and contribute to Europe’s digital and innovation agenda.
Our clients—primarily in chemical, pharmaceutical, MedTech, biotech, and materials science—face a critical challenge: the exponential growth of scientific content, increasing pressure to innovate, and a lack of digital transformation in R&D. These organizations rely on scientific papers, patents, and proprietary research. Their highly educated teams often spend up to 40% of their time on manual tasks like searching, aggregating, and cleaning data. With over 6,000 papers and 8,000 patents published daily, growing at 5% annually, accessing the right knowledge at the right time is becoming unmanageable.
These companies face two types of burdens: time-consuming tasks they must do (e.g. literature reviews) and high-value tasks they cannot scale (e.g. extracting insights from competitor patents). Their core need is to unlock insights from documented research—quickly, accurately, and securely.
Through this project, we transitioned from fragmented TRL6 tools to a coherent TRL8 platform—the Researcher Workspace—now deployed in four pilot organizations. It automates document comprehension, contextual interrogation, and knowledge extraction, reducing data-gathering time by up to 40%. The platform is content-agnostic, privacy-first, and adaptable to internal and external sources. It supports on-premise deployment and uses explainable, non-hallucinatory AI to ensure scientific rigor.
The project’s impact has been validated through pilot deployments, technical milestones, and market feedback. Dissemination reached thousands across academia, industry, and media. We secured €7.64 million in investment, submitted 18 approved deliverables, and launched a revised Go-To-Market strategy. With over 2,000 professionals engaged through events and media, we are well-positioned for growth.
Looking ahead, our vision is to build a fully-fledged scientific assistant—an AI that not only answers questions but guides users through research, asks the right questions, and brings interdisciplinary insights. This is our roadmap, and the project confirms Iris.ai’s readiness to scale and contribute to Europe’s digital and innovation agenda.
Over two years, Iris.ai transformed scattered tools and TRL6 technologies into the TRL8 Researcher Workspace—a market-leading platform for scientific knowledge processing. It enables users to: (1) gain overviews of research documents; (2) narrow document sets using context-aware filtering; (3) extract insights through interrogation; and (4) automate updates and monitor vocabulary shifts.
The Workspace is content-agnostic and supports integration of internal and external sources. It ensures full privacy and does not use client data beyond their workflows. The platform emphasizes explainability and factual accuracy, avoiding hallucinations and using efficient models that are cost-effective and environmentally sustainable. It adapts rapidly to domain-specific vocabularies, making it scalable across R&D, regulatory affairs, and IP analysis.
Pilot deployments with four clients in pharma, MedTech, and materials science validated the platform’s adaptability and usability. These pilots revealed new use cases, informed the roadmap, and fostered strong engagement. The platform has already reduced R&D professionals’ time spent on data gathering and supports secure, on-premise use.
Commercially, the project laid a strong foundation. A revised Go-To-Market strategy, new brand direction, and improved marketing funnel are in place. The platform is in use via API-based enterprise deployments. The exploitation strategy now includes adjacent domains and emphasizes privacy-first deployment.
Dissemination exceeded expectations: 500–700 scientific professionals were reached through 13 conferences and 3 workshops; 800–1,200 industry participants joined webinars and trainings. Social media and blogs generated 15,000–25,000 impressions, and media coverage in 47+ publications reached 35,000–50,000 readers. These efforts built strong visibility and support future outreach.
With €7.64 million in investment, including EIC Fund support, Iris.ai is ready to scale, expand its team, and strengthen compliance. The Workspace enhances research, supports innovation, and aligns with EU priorities in AI and scientific excellence.
The Workspace is content-agnostic and supports integration of internal and external sources. It ensures full privacy and does not use client data beyond their workflows. The platform emphasizes explainability and factual accuracy, avoiding hallucinations and using efficient models that are cost-effective and environmentally sustainable. It adapts rapidly to domain-specific vocabularies, making it scalable across R&D, regulatory affairs, and IP analysis.
Pilot deployments with four clients in pharma, MedTech, and materials science validated the platform’s adaptability and usability. These pilots revealed new use cases, informed the roadmap, and fostered strong engagement. The platform has already reduced R&D professionals’ time spent on data gathering and supports secure, on-premise use.
Commercially, the project laid a strong foundation. A revised Go-To-Market strategy, new brand direction, and improved marketing funnel are in place. The platform is in use via API-based enterprise deployments. The exploitation strategy now includes adjacent domains and emphasizes privacy-first deployment.
Dissemination exceeded expectations: 500–700 scientific professionals were reached through 13 conferences and 3 workshops; 800–1,200 industry participants joined webinars and trainings. Social media and blogs generated 15,000–25,000 impressions, and media coverage in 47+ publications reached 35,000–50,000 readers. These efforts built strong visibility and support future outreach.
With €7.64 million in investment, including EIC Fund support, Iris.ai is ready to scale, expand its team, and strengthen compliance. The Workspace enhances research, supports innovation, and aligns with EU priorities in AI and scientific excellence.
A key achievement was introducing the Researcher Workspace to four pilot clients. These ongoing deployments have shown the platform’s flexibility and effectiveness across diverse environments. Outcomes include successful implementation, discovery of new use cases, and strong training and feedback mechanisms. High user engagement has shaped the product roadmap. The pilots also revealed strong potential for scaling across broader organizational segments. Beyond immediate needs, they serve as strategic tools for deepening relationships and fostering long-term partnerships.
We continue to advance the platform toward enterprise readiness and are building a commercial pipeline. This includes refining our market strategy, expanding IP protection, and securing long-term funding. These efforts are progressing well and position us for sustained post-project impact.
Socio-economically, the project has created jobs—especially in Bulgaria and through global EoR hires—and strengthened our sales and marketing infrastructure. Reducing R&D professionals’ time spent on data tasks by 30–40% boosts productivity and supports European competitiveness in AI-driven innovation. Our use of efficient models also contributes to environmental sustainability.
Wider societal implications include democratizing access to scientific knowledge, promoting responsible AI, and aligning with EU policy goals like the AI Act. By enabling faster, more inclusive access to insights, Iris.ai helps bridge the gap between knowledge and application, supporting innovation that benefits society.
We continue to advance the platform toward enterprise readiness and are building a commercial pipeline. This includes refining our market strategy, expanding IP protection, and securing long-term funding. These efforts are progressing well and position us for sustained post-project impact.
Socio-economically, the project has created jobs—especially in Bulgaria and through global EoR hires—and strengthened our sales and marketing infrastructure. Reducing R&D professionals’ time spent on data tasks by 30–40% boosts productivity and supports European competitiveness in AI-driven innovation. Our use of efficient models also contributes to environmental sustainability.
Wider societal implications include democratizing access to scientific knowledge, promoting responsible AI, and aligning with EU policy goals like the AI Act. By enabling faster, more inclusive access to insights, Iris.ai helps bridge the gap between knowledge and application, supporting innovation that benefits society.