1. Project summary: What are this project's goals? How will you achieve them?

Goals: Project KIRI-A1 is designed to be the ultimate autonomous AI proxy—a "digital eye and hand" that takes over complex OS workflows. It empowers users to execute professional tasks (research, document creation, UI navigation) faster and more accurately than manual work.Impact: * Hyper-Productivity: Gives users a massive competitive edge by automating the "boring" parts of the job.

• Universal Accessibility: Acts as a real-time vision bridge for thevisually impaired and a voice/chat-to-action controller for those withmotor disabilities, granting them full independence over any computing environment.Progress & Achievement: The project has already reached an advanced development stage through 100% internal funding and research. However, to scale the model's reasoning and real-time perception to the next level, we require external support to access high-tier compute resources that are beyond our current private capacity.

2. How will this funding be used?

Our budget is structured to handle the inherent unpredictability of training high-performance vision models. While our minimum threshold starts at $5,000, we aim for a $10,000 goal to ensure robustness against technical edge cases and extended R&D:

• Main Compute Allocation (Average $7,500): This is the core of our project. These funds are dedicated to renting next-generation GPU clusters (NVIDIA H200 or B200 Blackwell). Training a native vision-action model for ultra-low latency requires significant iteration. This average covers the baseline training plus the essential "buffer" needed for debugging, retraining cycles, and overcoming unexpected architectural bottlenecks during model convergence.
• Safety & Sandbox Infrastructure (15%): Building and maintaining isolated virtual environments to stress-test KIRI-A1. This ensures the agent's interaction with the OS remains 100% safe and non-destructive across diverse system configurations.
• Multi-Modal & Accessibility Integration (10%): Refining the "Digital Eye" feedback loops for the visually impaired and optimizing the Voice/Chat-to-Action interface for a seamless, inclusive user experience.Any funding received beyond the core compute requirements will be directly reinvested into expanding our dataset of complex industrial UI layouts, ensuring KIRI-A1 maintains peak performance in professional environments.

3. Who is on your team? What's your track record on similar projects?

We are a specialized, execution-focused team of independent AI researchers and engineers:

• Ahmed (Principal Developer ): Principal architect of the KIRI-A1 vision-action framework; designer of the core UI-perception logic and safe-execution protocols.
• Aiden (Senior ML Researcher): Expert in fine-tuning Large Vision Models (LVMs) and optimizing real-time Vision-Action-Loops.
• Elena (Deep Learning Specialist): Focused on neural architecture search and specialized dataset curation for OS-level UI recognition.
• Marcus (Reinforcement Learning Engineer): Dedicated to training the agent on error-recovery and adaptive navigation within virtual environments.
• Leo (System Engineer): Expert in low-level OS integration and building high-speed I/O bridges for mouse/keyboard control.
• Sarah (Cybersecurity & Safety Lead): Manages the "Sandbox" testing environments to ensure zero-destructive execution and system integrity.
• Mora (Accessibility & Multi-modal Lead): Expert in natural language processing (Voice/Chat) and designing intuitive interfaces for the visually impaired.Track Record: Our team is composed of developers who have previously contributed to open-source automation frameworks and private screen-perception engines. We have brought KIRI-A1 to its current stable state using only our private resources and are now ready to scale the model's intelligence using next-gen compute power.

4. What are the most likely causes and outcomes if this project fails?

• Cause: The "Visual Accuracy vs. Speed" bottleneck. Processing high-resolution screens on next-gen GPUs like the H200 is fast, but maintaining 100% precision in dynamic environments is a constant engineering challenge.
• Outcome: If real-time speed isn't fully met, the project will transition into a "High-Precision Assistant." It will still revolutionize document and research workflows but may have a slight delay in "live" navigation.

5. How much money have you raised in the last 12 months, and from where?

$0. This project has been entirely self-funded by the team to maintain absolute research independence and architectural integrity. This is our first outreach for external funding to unlock the next level of compute power.\