Customer Case Studies
Geo-Contextual Analysis
Corvic powers an interactive agent turning multimodal street-level data into instant geo-temporal insights — enabling real-time analysis for air quality, noise, traffic, and urban decision-making.
The Challenge
Cities, real estate firms, and public agencies are increasingly relying on street-level sensor networks to monitor air quality, noise, traffic, and neighborhood livability. While this data is rich and continuously collected, it is often siloed and difficult to access in a way that is geo- and time-specific. Existing dashboards and static reports cannot easily answer questions like “What’s the air quality on this block compared to the city average?” or “How has parking availability changed in this ZIP code over the past 6 months?”
The Solution
Ensense has built an interactive conversational agent that makes their multimodal sensor data accessible through natural language. Corvic powers this capability by providing the data intelligence backbone:
• Ingests and processes multimodal sensor streams (audio, imagery, air quality, weather, light, traffic).
• Uses Mixture of Spaces (MoS™) to preserve context across location, time, and data type.
• Applies Explainable Chain of Adaptive Actions (ECoAA™) for accurate, context-aware responses.
• Supports scalable geo-temporal analysis across millions of data points.
Users can now ask address-level or neighborhood-level questions and receive instant, explainable answers — improving data-driven decisions in real estate, city planning, and public health.
Key Benefits
Instant Hyperlocal Insights
Answers delivered in seconds for any street, ZIP code, or neighborhood.
+30% Accuracy Improvement
More reliable than legacy chat or static dashboards.
Multi-Modal Intelligence
Combines air, noise, traffic, and visual data for deeper context.
Enterprise-Scale Performance
Handles millions of geo-tagged records across multiple cities.
Example Impact
• Real Estate: Compare neighborhood air quality and noise levels to inform property valuations.
• Public Sector: Track pollution trends and parking availability for urban planning.
• Enterprises: Use street-level data to guide retail site selection or assess community conditions.
Instead of manually pulling data from disparate systems, decision-makers now get on-demand, conversational insights that connect multiple data sources in real time.
How It Works
01
Ingest & Elevate
Process multimodal sensor data (audio, images, weather, air, traffic).
02
Link & Structure
Use CAPA™ to preserve geo-temporal context.
03
Query with Natural Language
Users ask questions by location and time.
04
Deliver Insights
Instant answers with explainable outputs and visualizations.
Corvic enables organizations like Ensense to make multimodal sensor data accessible, accurate, and actionable through interactive, intelligent agents.
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