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.

AI71 AI Technologies top builders

Explore the top contributors showcasing the highest number of AI71 AI Technologies app submissions within our community.

AI71 AI Technologies Hackathon projects

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

ForgeSense: AI Maintenance Copilot on AMD MI300X

ForgeSense: AI Maintenance Copilot on AMD MI300X

Factories already collect sensor streams, camera footage, work orders and thick PDF manuals, yet technicians still lose hours deciding what an alarm means and what to do. ForgeSense is a maintenance copilot for shift technicians that closes that gap. How it works: deterministic detectors watch the data first. An anomaly and remaining-useful-life model monitors vibration and temperature signals, and a vision model flags surface defects on camera frames. When one fires, an agent running an open LLM on AMD Instinct GPUs (ROCm + vLLM) gathers evidence with tools: the recent sensor window, similar past work orders, and a retrieval index of the equipment manuals. It then writes a plain-language alert: what is wrong, the evidence chart, ranked likely causes, step-by-step repair actions with manual page citations, and required parts and safety lockout notes. Responsible by design: the LLM never makes the detection and never stops a machine. Every alert carries a confidence level and cites its sources. Low-confidence or safety-critical cases are escalated to a human supervisor with a pre-filled work order, and the technician approves before anything is created. Impact: we measured time-to-diagnosis, detection recall and false alerts against a manual-search baseline on public industrial datasets (NASA C-MAPSS, MVTec AD), plus tokens/second and cost per alert on a single MI300X, where the vision-language model, embedding model and reranker run side by side in 192 GB of memory

Two-Arm Table Setter

Two-Arm Table Setter

Say "set the table" - or "put the red thing top left of the plate and the grey utensil on the right" - and two SO-101 arms in MuJoCo do it. HOW IT WORKS. An overhead colour-and-depth camera finds every object from its height and shape (0.5 mm average error over 20 random tables); the simulator's true positions are never used to act. Qwen3-VL-4B, running locally as Intel's INT4 OpenVINO build, reads the command together with the camera image, so it knows "the red thing" is the mug. A planner decides which arm does what: cutlery starting on the wrong side is passed hand to hand, and round items are set down where both arms can reach. After every item the robot looks again, and anything knocked out of place is put back first. LEARNING. Our scripted skills became the teacher, recording 1,219 successful demonstrations automatically. A LeRobot ACT policy trained on a laptop CPU learned the mug skill - 19/20 within 1.5 cm on tables it had never seen. It took seven versions; the write-up covers what failed and why. INTEL OPTIMISATION, on a Core Ultra 7 155H across all three chips. OpenVINO runs the policy 2.9x faster than PyTorch on the CPU (0.72 ms vs 2.07 ms) with no loss of task success. The same policy runs on the AI Boost NPU at 0.90 ms and places 10/10 mugs, freeing the CPU and iGPU. The Arc iGPU reads a command in 1.3 s, 2.8x faster than the CPU. An honest negative result too: OpenVINO 2026.3's NPU compiler cannot build Qwen3-VL's vision tower, so the language model falls back to the iGPU automatically. RESULTS. 30/30 random tables set correctly, 29/30 on harder ones, 9/10 on the Core Ultra. Every number is checked against simulator truth, reproducible from one command, and the raw logs ship in the repo. Not included, said plainly: the drawer and pouring, and most skills are scripted rather than learned.

ManipulaX

ManipulaX

This project is an end-to-end Physical AI system for bimanual robotic manipulation, built for the Intel Physical AI Online Challenge. It uses two simulated SO-101 robot arms in MuJoCo to understand natural-language instructions, interpret camera observations, reason about the current scene, and execute multi-step manipulation tasks for setting up a dinner table. The system combines vision-language-action reasoning with coordinated dual-arm control, enabling actions such as opening a drawer, retrieving utensils, picking up and placing a plate or mug, and performing complementary actions between both arms. The environment supports randomized scene conditions to evaluate robustness across different object placements and physical or visual variations. The inference pipeline is designed for Intel Core Ultra hardware, using OpenVINO for model optimization and efficient edge inference while maintaining task performance. The project also includes reproducible simulation, evaluation, benchmarking, and demonstration workflows.

Fresno LUX: A Living City

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.

Fresno LUX: A Living City

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.

Fresno LUX: A Living City

Fresno LUX: A Living City

Fresno LUX is a prototype for a living intelligence layer across an entire city. Fresno already contains enormous amounts of intelligence: residents know where problems exist, nonprofits know where help is needed, businesses know where opportunities exist, educators understand gaps in learning, researchers generate knowledge, and government maintains critical infrastructure and data. The problem is that much of this intelligence is fragmented across disconnected systems. Fresno LUX connects those pieces. The platform creates a shared city graph where people, organizations, resources, needs, projects, observations, experiments, and outcomes become connected nodes. Instead of simply collecting data, LUX continuously asks: What is happening? What do we know? What is missing? What can we test? What changed? A community problem can become an observable signal. That signal can become an experiment. The experiment produces evidence. Evidence changes the system's understanding. That updated understanding informs the next action. Observe → Connect → Understand → Act → Measure → Learn → Converge. The goal is to move Fresno from disconnected information toward collective intelligence—a system where knowledge can travel across boundaries and successful solutions can be discovered, tested, measured, and replicated. LUX is designed to make the invisible connections visible: the relationship between a neighborhood and its resources, between a student and educational opportunity, between a business and workforce capacity, between environmental conditions and community health, and between an idea and measurable real-world impact. This hackathon prototype demonstrates the beginning of a Living Fresno Graph: a city that can observe itself, learn from evidence, and coordinate toward better outcomes.