Building the Future of Autonomous Urban Intelligence and Self Healing Cities
We invest deeply in advanced computer vision and state-of-the-art vision-language models (VLMs), leveraging a deep active learning (DAL) approach for scene recognition, change detection, and anomaly identification. This results in significantly higher detection rates of compliance-related issues and enables more intelligent diagnostic and predictive capabilities across urban environments.
We’re a dynamic collective operating at the intersection of human ingenuity and advanced technology.
FOCUS AREA / 01
Advanced Computer Vision
FOCUS AREA / 02
Generative AI Framework
FOCUS AREA / 03
Deep Active Learning
FOCUS AREA / 04
Adaptive Autonomous Twins
FOCUS AREA / 05
Event Driven Architecture
FOCUS AREA / 06
Agentic AI Workflows
Domestic and international provisional patents pending.
Our platform is built on a foundation of technologies that push the boundaries of urban automation, regulatory compliance, and public service optimization.
From real-time visual intelligence on edge devices to large-scale agentic AI driving autonomous decision-making, we are shaping the future of adaptive city systems.
PATENT 63/770,777
Machine learning framework to detect and monitor compliance matters.
PATENT 63/770,859
Detecting Compliance Violations Using Advanced Computer Vision and Generative Artificial Intelligence
PATENT 63/777,612
Rules and logic creation for compliance monitoring.
Intelligent Perception at the Source
We have developed a state-of-the-art, edge-based AI vision system capable of autonomously detecting and capturing information on objects and events of interest in real time. This architecture enables low-latency, high-efficiency, and privacy-aware city monitoring without reliance on continuous cloud processing.
↳ On-device deep learning model optimization for real-time inference with minimal power and bandwidth requirements.
↳ Custom-trained object/event detection models fine-tuned for urban environments (e.g., sidewalk obstruction, illegal dumping, infrastructure damage).
↳ Edge-to-cloud pipeline with intelligent data reduction, reducing redundant information and prioritizing events of interest for higher-tier analysis.
↳ Secure, modular firmware stack enabling rapid updates and customization while preserving system integrity
↳ Agentic AI decision modules on edge devices, drones, and robots that can autonomously determine when to collect more data, trigger alerts, or execute localized actions, enabling real-time situational autonomy without round-trip communication to central servers
Vision Language Models for Complex Urban Semantics
Our platform leverages state-of-the-art multimodal vision-language models (VLMs) to understand complex visual scenes in regulatory, safety, and city-service contexts. These models go beyond traditional computer vision to interpret context, reason across modalities, and identify subtle compliance issues in urban settings. These include Image Captioning, Visual Question Answering (VQA), Multimodal Reasoning, Visual Grounding, and Optical Character Recognition (OCR).
↳ Integration of foundational VLMs with domain-specific fine-tuning to recognize nuanced public ordinance violations or safety hazards.
↳ Custom-built datasets representing high-resolution, multimodal urban scenes with annotated policy infractions.
↳ Promptable scene understanding allows real-time querying of live city data using natural language (e.g., “Show me all sites with ADA compliance issues.” – or another example that the team thinks is more representative).
↳ Few-shot and zero-shot learning capabilities, enabling rapid generalization to new compliance categories without model retraining
Automating Urban Compliance and Repair Workflows
At the heart of our system is a truly agentic AI engine that orchestrates end-to-end workflows: from issue detection and classification to alert generation, dispatching, and even autonomous reporting and regulatory follow-up. This is where the system transitions from passive monitoring to autonomous action.
↳ Multi-agent architecture incorporating reasoning, planning, and execution agents — enabling adaptive responses to complex city scenarios.
↳ Closed-loop repair workflow (detection → validation → dispatch → verification → report → system update), handled entirely by autonomous agents.
↳ Feedback learning mechanism allows the system to learn from task outcomes (e.g., successful vs. failed remediation) and optimize future decisions.
↳ Dynamic simulation interface with an evolving urban twin model to test, evaluate, and refine agent behaviors in virtual or live environments.
↳ Regulation-aware policy engine that allows agents to reason about city codes and prioritize actions accordingly — a major innovation in machine policy understanding.