Building the Future of Autonomous Urban Intelligence
We invest heavily in developing advanced computer vision, superior vision language models (VLMs), and utilize a deep active learning (ADL) approach to scene recognition, change and anomaly detection. This translates into a higher detection of compliance-related issues and more intelligent diagnostic and predictive tools.
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.
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
Distributed Analytics
Intelligent Perception at the Source
We’ve developed an edge-based vision-AI system capable of autonomously detecting and collecting information related to objects or events of interest in real-time. This enables low-latency, high-efficiency, privacy-aware city monitoring without reliance on constant 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.
↳ 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.