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AI71

AI71, a pioneering AI company launched by Abu Dhabi's Advanced Technology Research Council (ATRC) and VentureOne, stands as a pivotal movement in the realm of AI innovation. Leveraging the globally top-ranked Falcon AI models from the Technology Innovation Institute, AI71's focus spans across multi-domain advancements, initially targeting the medical, education, and legal sectors. The Technology Innovation Institute (TII) is a leading global research center based in Abu Dhabi, United Arab Emirates. TII focuses on pushing the frontiers of knowledge and delivering transformative technologies through its teams of scientists, researchers, and engineers. As part of the Abu Dhabi Government's Advanced Technology Research Council, TII serves as a catalyst for change and sets new standards in scientific research.

General
Organization[Technology Innovation Institute AI71
LocationAbu Dhabi, United Arab Emirates
Area servedWorldwide

About AI71

AI71, an innovative AI company founded by Abu Dhabi's Advanced Technology Research Council (ATRC) in collaboration with VentureOne, is a significant force in the field of AI development. Drawing on the world-renowned Falcon AI models from the Technology Innovation Institute, AI71 is advancing multiple domains, with an initial focus on the medical, education, and legal sectors.

Committed to decentralizing data ownership, AI71 is setting new benchmarks in privacy and security, empowering enterprises and governments to maintain full control over their data. Through strategic partnerships, AI71 seeks to revolutionize AI accessibility, driving a new chapter in the UAE's knowledge economy and positioning the nation as a prominent player on the global AI landscape.

About TII UAE

TII UAE is part of the Abu Dhabi Government's Advanced Technology Research Council, which oversees technology research in the emirate. As a disruptor in science, TII is setting new standards and serves as a catalyst for change.

Faced with a future of limitless possibilities and supported by strategically funded investments, TII encourages a culture of discovery. Their work reinforces Abu Dhabi and the UAE's status as an R&D hub and a global leader in breakthrough technologies.

Key Projects and Initiatives

AI71 is involved in various projects and initiatives aimed at driving innovation and creating a better future. Some of their key projects include:

  • Falcon 2: We're excited to announce that it's now Open-Source, Multilingual, and Multimodal—standing out as the only AI model with Vision-to-Language capabilities. The new Falcon 2 11B surpasses Meta's Llama 3 8B and matches the performance of Google's leading Gemma 7B model, as independently verified by the Hugging Face Leaderboard. Looking ahead, Further plans are to integrate a 'Mixture of Experts' to enhance Falcon 2's capabilities.

  • Falcon LLM: TII's flagship series of large language models, built from scratch using a custom data pipeline and distributed training library. Falcon LLM models are state-of-the-art for their size, outperforming most other models on NLP benchmarks. TII has open-sourced several Falcon LLM artifacts, including pretrained models, instruction/chat models, and the RefinedWeb dataset.

  • RefinedWeb Dataset: A massive web dataset with stringent filtering and large-scale deduplication, enabling models trained on web data alone to match or outperform models trained on curated corpora. RefinedWeb is licensed under Apache 2.0.

  • Open Call for Proposals: TII is calling for proposals from the global research community and SME entrepreneurs to submit use cases for Falcon LLM, promoting collaborations and driving innovation.

  • TII Falcon LLM License: A fork of Apache 2.0, this license allows researchers and developers to freely use TII's models for research and personal purposes. For commercial use, royalties are exempted for attributable revenues under $1 million per year; otherwise, a commercial agreement with TII is required.

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AI71 AI Technologies Hackathon projects

Discover innovative solutions crafted with AI71 AI Technologies, developed by our community members during our engaging hackathons.

VICTOR - AI ER Triage

VICTOR - AI ER Triage

V.I.C.T.O.R. (Voice-Intelligent Cardiovascular Triage & Outcome Re-evaluator) is a voice-first AI triage agent designed for Emergency Departments. It addresses a critical gap in emergency medicine: atypical cardiovascular disease presentations — such as abdominal pain, nausea, fatigue, and jaw pain — are systematically undertriaged compared to classic chest pain, particularly in women, elderly patients, and underserved populations. V.I.C.T.O.R. runs a 5-agent swarm on Llama 3.1 8B Instruct, LoRA fine-tuned on ~61,000 training examples derived from MIMIC-IV v3.1 and the MUSIC sudden cardiac death dataset. The agents include a Triage Leader, a Patient Voice advocate, a Concordance Analyst that cross-references patient speech content against voice biomarker signals, a Clinical Note Writer, and an Evidence Synthesiser. Together they re-evaluate triage acuity in real time and flag cases where standard ESI scoring may have missed a serious cardiovascular event. The system features two interfaces: a patient-facing kiosk where patients speak naturally to Victor or Jackie (two voice personas) in their preferred language, and a clinician-facing dashboard styled as an EMR module, showing concordance alerts, voice biomarker trends, re-triage recommendations, and AI-generated clinical notes. Deepgram Flux Multilingual handles speech-to-text with automatic language detection, while ElevenLabs Flash v2.5 provides natural spoken responses. All inference runs on an AMD MI300X GPU via vLLM, with the LoRA adapter fine-tuned using ROCm and PyTorch on the same AMD hardware — full AMD stack from training to production. Quantitative analysis of MIMIC-IV data confirmed the project's core premise: non-chest-pain CVD presentations receive higher mean acuity scores (undertriaged) than chest pain, with the critical caveat that MIMIC-IV only captures patients who eventually received a CVD diagnosis — truly missed cases are invisible, meaning real-world bias is likely worse than measured.

VICTOR - AI ER Triage

VICTOR - AI ER Triage

V.I.C.T.O.R. (Voice-Intelligent Cardiovascular Triage & Outcome Re-evaluator) is a voice-first AI triage agent designed for Emergency Departments. It addresses a critical gap in emergency medicine: atypical cardiovascular disease presentations — such as abdominal pain, nausea, fatigue, and jaw pain — are systematically undertriaged compared to classic chest pain, particularly in women, elderly patients, and underserved populations. V.I.C.T.O.R. runs a 5-agent swarm on Llama 3.1 8B Instruct, LoRA fine-tuned on ~61,000 training examples derived from MIMIC-IV v3.1 and the MUSIC sudden cardiac death dataset. The agents include a Triage Leader, a Patient Voice advocate, a Concordance Analyst that cross-references patient speech content against voice biomarker signals, a Clinical Note Writer, and an Evidence Synthesiser. Together they re-evaluate triage acuity in real time and flag cases where standard ESI scoring may have missed a serious cardiovascular event. The system features two interfaces: a patient-facing kiosk where patients speak naturally to Victor or Jackie (two voice personas) in their preferred language, and a clinician-facing dashboard styled as an EMR module, showing concordance alerts, voice biomarker trends, re-triage recommendations, and AI-generated clinical notes. Deepgram Flux Multilingual handles speech-to-text with automatic language detection, while ElevenLabs Flash v2.5 provides natural spoken responses. All inference runs on an AMD MI300X GPU via vLLM, with the LoRA adapter fine-tuned using ROCm and PyTorch on the same AMD hardware — full AMD stack from training to production. Quantitative analysis of MIMIC-IV data confirmed the project's core premise: non-chest-pain CVD presentations receive higher mean acuity scores (undertriaged) than chest pain, with the critical caveat that MIMIC-IV only captures patients who eventually received a CVD diagnosis — truly missed cases are invisible, meaning real-world bias is likely worse than measured.

VICTOR - AI ER Triage

VICTOR - AI ER Triage

V.I.C.T.O.R. (Voice-Intelligent Cardiovascular Triage & Outcome Re-evaluator) is a voice-first AI triage agent designed for Emergency Departments. It addresses a critical gap in emergency medicine: atypical cardiovascular disease presentations — such as abdominal pain, nausea, fatigue, and jaw pain — are systematically undertriaged compared to classic chest pain, particularly in women, elderly patients, and underserved populations. V.I.C.T.O.R. runs a 5-agent swarm on Llama 3.1 8B Instruct, LoRA fine-tuned on ~61,000 training examples derived from MIMIC-IV v3.1 and the MUSIC sudden cardiac death dataset. The agents include a Triage Leader, a Patient Voice advocate, a Concordance Analyst that cross-references patient speech content against voice biomarker signals, a Clinical Note Writer, and an Evidence Synthesiser. Together they re-evaluate triage acuity in real time and flag cases where standard ESI scoring may have missed a serious cardiovascular event. The system features two interfaces: a patient-facing kiosk where patients speak naturally to Victor or Jackie (two voice personas) in their preferred language, and a clinician-facing dashboard styled as an EMR module, showing concordance alerts, voice biomarker trends, re-triage recommendations, and AI-generated clinical notes. Deepgram Flux Multilingual handles speech-to-text with automatic language detection, while ElevenLabs Flash v2.5 provides natural spoken responses. All inference runs on an AMD MI300X GPU via vLLM, with the LoRA adapter fine-tuned using ROCm and PyTorch on the same AMD hardware — full AMD stack from training to production. Quantitative analysis of MIMIC-IV data confirmed the project's core premise: non-chest-pain CVD presentations receive higher mean acuity scores (undertriaged) than chest pain, with the critical caveat that MIMIC-IV only captures patients who eventually received a CVD diagnosis — truly missed cases are invisible, meaning real-world bias is likely worse than measured.

Second Opinion App For Medical Patients

Second Opinion App For Medical Patients

Our project is a python based application designed to assist patients in analyzing their prescriptions. By uploading their prescriptions and entering medicine names, the app uses transformers(huggingface) to assess the need for a second opinion. It also provides personalized suggestions for questions patients (before taking prescribed medicines) to ask their doctors to avoid potential side effects or medication-related issues. The app aims to empower patients to make more informed decisions about their health, promoting safer medication practices and fostering communication between patients and healthcare professionals.

NetConnect

NetConnect

Public Sector Network Connectivity Analyzer The Public Sector Network Connectivity Analyzer is a comprehensive solution designed to address the critical need for reliable network monitoring across public institutions. Our application serves as an essential tool for IT administrators managing connectivity infrastructure for schools, healthcare facilities, government offices, libraries, and other public service organizations. Core Capabilities Real-Time Network Visualization Interactive diagrams and topology maps provide clear visibility into how public institutions are connected, displaying network elements, connection points, and infrastructure components with intuitive visualization tools. Performance Monitoring System Our platform continuously tracks vital network metrics including uptime percentages, latency measurements, bandwidth utilization, and connection status across the entire public sector network, enabling proactive management. Advanced Simulation Engine IT professionals can run comprehensive simulations to test network resilience under various scenarios such as increased user loads, infrastructure failures, or cyber incidents, helping identify vulnerabilities before they impact critical services. Institution Management Portal Administrators can efficiently manage information about connected institutions, monitor their connection status in real-time, and access detailed performance metrics through a unified dashboard interface. Geographic Mapping Integration Our system incorporates geographic visualization capabilities to display the physical distribution of institutions and network infrastructure across regions, facilitating better resource allocation and planning. Technical Implementation This solution addresses the unique challenges faced by public sector organizations that require reliable connectivity for delivering essential services to communities, while providing the tools needed to ensure network resilience, performance, and security.

NetConnect

NetConnect

Public Sector Network Connectivity Analyzer The Public Sector Network Connectivity Analyzer is a comprehensive solution designed to address the critical need for reliable network monitoring across public institutions. Our application serves as an essential tool for IT administrators managing connectivity infrastructure for schools, healthcare facilities, government offices, libraries, and other public service organizations. Core Capabilities Real-Time Network Visualization Interactive diagrams and topology maps provide clear visibility into how public institutions are connected, displaying network elements, connection points, and infrastructure components with intuitive visualization tools. Performance Monitoring System Our platform continuously tracks vital network metrics including uptime percentages, latency measurements, bandwidth utilization, and connection status across the entire public sector network, enabling proactive management. Advanced Simulation Engine IT professionals can run comprehensive simulations to test network resilience under various scenarios such as increased user loads, infrastructure failures, or cyber incidents, helping identify vulnerabilities before they impact critical services. Institution Management Portal Administrators can efficiently manage information about connected institutions, monitor their connection status in real-time, and access detailed performance metrics through a unified dashboard interface. Geographic Mapping Integration Our system incorporates geographic visualization capabilities to display the physical distribution of institutions and network infrastructure across regions, facilitating better resource allocation and planning. Technical Implementation This solution addresses the unique challenges faced by public sector organizations that require reliable connectivity for delivering essential services to communities, while providing the tools needed to ensure network resilience, performance, and security.