Solo Tech

Top Builders

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

Solo Tech

Solo Tech is a cutting-edge platform that provides on-device artificial intelligence (AI) solutions. It stands out by emphasizing data privacy, efficient performance, and user control without relying on cloud-based services. This technology facilitates the integration of AI capabilities directly onto user devices, allowing for faster, private, and customized AI experiences.

General
Author	Solo Tech
Website	https://www.getsolo.tech/
Link to the official source of API	Solo Tech Developer API
Type	On-device AI platform

Key Features

On-Device AI Processing: Runs AI models locally, eliminating the need for continuous internet connectivity and preserving data privacy.
Enhanced Data Privacy: Users retain full ownership of their data, as all processing is done locally on their devices.
Model Customization: Offers the ability to fine-tune and tailor AI models according to specific user needs and use cases.
Offline Functionality: Ensures that AI operations remain functional even in environments with limited or no internet access.
High-Efficiency Performance: Optimized for speed and effective operation across various devices, enhancing user experience and productivity.

Use Cases

Enterprise Solutions: Deploys predictive analytics, customer insights, and process optimization tools for business growth and operational efficiency.
GovTech Applications: Assists in data analysis, public service enhancements, and informed decision-making processes for governmental organizations.
Healthcare Innovations: Powers patient data analytics, diagnostic tools, and the development of personalized treatment plans.
Education Sector: Facilitates personalized learning experiences and supports administrative functions through AI-driven solutions.

Get Started Building with Solo Tech

To begin developing with Solo Tech, visit the official Solo Tech website for comprehensive guides, API documentation, and support resources. Integrating on-device AI into your projects ensures enhanced data privacy, reliability, and custom AI functionalities tailored to your needs.

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Solo Tech AI technology page Hackathon projects

Discover innovative solutions crafted with Solo Tech AI technology page, developed by our community members during our engaging hackathons.

TrafficOS: Gemini-Powered Mobility Intelligence

TrafficOS is an AI-powered urban traffic intelligence platform designed to transform raw mobility data into operational decisions for cities, logistics teams, and delivery fleets. Instead of simply displaying traffic on a map, TrafficOS analyzes live road conditions, estimates congestion levels, forecasts future traffic density, and explains route decisions through an AI intelligence layer. The system combines Mapbox visualization, TomTom live traffic data, SARIMAX-style forecasting, and a Gemini-powered assistant called ARIA. TrafficOS monitors road segments, estimates speed, density, flow, congestion tier, and route risk, then converts those technical signals into clear operational reports. ARIA allows non-technical users to understand what is happening in the network, why a route is recommended, and what conditions may affect delivery time, fuel efficiency, or congestion exposure. A key part of TrafficOS is its use of exogenous variables in forecasting. The model is designed to account not only for traffic history, but also for weather, precipitation, time of day, day of week, holidays, festivities, incidents, road events, and logistics pressure. This makes the system more realistic for real-world urban mobility, where congestion is rarely caused by one factor alone. The demo supports Fusagasugá, Colombia and San Francisco, USA to show how the same architecture can adapt to both emerging-city mobility challenges and dense metropolitan logistics scenarios. TrafficOS is especially relevant for delivery companies, fleet operators, and urban planners who need to optimize fuel, time, reliability, and dispatch decisions. The platform includes a live traffic dashboard, dynamic routing interface, segment analytics, Gemini/ARIA city pulse intelligence, and PDF report generation. The goal is to move beyond consumer navigation and build a command-center style system for operational traffic intelligence.

GeminEYE- AI Contract Risk Investigator

GeminEYE is an AI-powered contract risk analyzer for legal and compliance teams. Users upload PDFs or DOCX files, or paste text directly, and the system extracts clauses, normalizes formatting, and sends the content to Gemini 3.1 Pro Preview through a configurable API layer. The model returns a structured investigator-style memo with narrative reasoning, a bullet summary, evidence-backed findings, and a 0–10 risk score across liability, indemnity, privacy, termination, IP, and venue. The dashboard surfaces risk trends, severity breakdowns, and blocked-versus-allowed analysis outcomes, giving teams a clear operational view of contract review activity. The UI presents risks with clear severity badges and negotiation-ready recommendations. If the model fails, the app returns a safe fallback memo so the demo never breaks. Built with Next.js API routes, the backend keeps the provider layer swappable so teams can move from the current integration to a direct Gemini setup with minimal changes. GeminEYE delivers practical, enterprise-ready contract intelligence that speeds review cycles while keeping human oversight in the loop.

CLOAK-AI

CLOAK-AI is a high-performance privacy-preserving engine designed to bridge the gap between enterprise data utility and strict PII/PCI compliance. Built specifically for the AMD Hackathon, the project addresses the critical risk of sensitive data leakage in Generative AI workflows. By leveraging the massive 192GB VRAM of the AMD Instinct™ MI300X and optimizing inference via ROCm™ 6.1, CLOAK-AI achieves an industry-leading scrubbing latency of just 0.08 seconds. The system utilizes a multi-layer neural architecture: a Llama-3.3-70B model serves as the intelligent reasoning core to identify complex data clusters, while a specialized NLP masking engine handles high-speed scrubbing. The infrastructure is deployed on the AMD Developer Cloud, ensuring that data never leaves the secure environment. Key features include real-time identification of identity documents (Passport/CNIC), financial credentials (IBAN/Credit Cards), and cybersecurity tokens. CLOAK-AI empowers organizations to adopt LLMs safely without compromising user trust or regulatory standards (GDPR/HIPAA).

RoboHazard Arena: VLM Drone Control

RoboHazard Arena is a research-based drone simulation project exploring how small vision-language models perform when placed inside a live 3D environment. The goal is not to give the model step-by-step instructions, but to give it a mission: reach the green target pad safely while avoiding obstacles. The drone can be controlled manually, or control can be delegated to Qwen3-VL. The model receives a camera view, telemetry, target information, obstacle information, and available actions, then chooses high-level movements such as move forward, strafe, ascend, descend, hover, or land. The project shows both the promise and limitations of small VLMs for embodied control. Qwen3-VL-2B succeeds in some simple cases but fails or hallucinates in others, proving that more research, better simulation, and stronger evaluation are needed before small models can reliably control small vehicles in dynamic real-world environments.