Atlas.new

Product Introduction

1. Definition: Atlas.new is a cloud-based, AI-powered geospatial platform (technical category: SaaS for spatial analytics) enabling users to create interactive maps, perform spatial analysis, and develop custom spatial applications without coding or GIS expertise.
2. Core Value Proposition: It democratizes geospatial intelligence by eliminating the need for GIS specialists, allowing businesses to leverage location data for decision-making through automated AI workflows and no-code tools.

Main Features

1. AI-Powered Spatial Analysis:

   • How it works: Users input natural language queries (e.g., "Find high-traffic retail sites near competitors"), and Atlas.new’s AI engine processes geospatial data (e.g., foot traffic, demographics) to generate heatmaps, drive-time polygons, or site suitability scores.
   • Technologies: Integrates machine learning (ML) for predictive modeling, computer vision for satellite imagery interpretation, and NLP for query processing.

2. Builder Mode for Spatial Apps:

   • How it works: Drag-and-drop interface to embed maps into custom web applications (e.g., property dashboards, infrastructure trackers). Supports real-time data syncing via APIs (e.g., Salesforce, IoT sensors).
   • Technologies: React-based frontend, GeoJSON/vector tile rendering, and serverless backend for scalable deployments.

3. Template Library:

   • How it works: Pre-built templates (e.g., "Wind Farm Site Selection," "Real Estate Portfolio Map") with configured AI models and data connectors. Users customize layers (e.g., zoning boundaries, environmental risks) via intuitive UI.
   • Technologies: Modular architecture with plug-and-play data integrations (e.g., OpenStreetMap, weather APIs, public datasets).

Problems Solved

1. Pain Point: High cost and complexity of traditional GIS software requiring specialized skills, slowing data-driven decisions in sectors like real estate or energy.
2. Target Audience:
   • Non-technical roles: Marketing managers (location-based targeting), urban planners (infrastructure mapping), sustainability officers (climate risk assessment).
   • Technical roles: Full-stack developers embedding maps into apps, data analysts automating spatial reports.
3. Use Cases:
   • Retailers optimizing franchise territories using demographic AI analysis.
   • Energy companies identifying solar panel sites via terrain and sun-exposure algorithms.
   • Municipalities tracking street light repairs with real-time spatial dashboards.

Unique Advantages

1. Differentiation: Unlike ArcGIS or QGIS (requiring GIS certification), Atlas.new uses AI to automate complex tasks (e.g., geocoding, spatial clustering), reducing project timelines from weeks to hours. Competitors like Mapbox lack built-in AI analytics.
2. Key Innovation: Proprietary "Navi" AI engine that auto-suggests data layers and statistical methods based on use case (e.g., recommending flood-risk datasets for site selection), minimizing manual configuration.

Frequently Asked Questions (FAQ)

1. Can Atlas.new integrate with my existing business data?
   Yes, Atlas.new connects to CRMs (Salesforce), ERPs, and databases via APIs, and ingests CSV/Excel files for real-time spatial visualization without data migration.

2. What AI analysis capabilities does Atlas.new offer?
   The platform includes travel-time analysis, heatmap clustering, predictive modeling (e.g., sales forecasting), and environmental risk scoring—all executable via natural language commands.

3. Is coding required to build spatial apps in Atlas.new?
   No, Atlas.new’s Builder Mode uses a no-code drag-and-drop interface with pre-built widgets (e.g., filters, popups) to create custom web apps for internal or public use.

4. How does Atlas.new ensure data security for enterprise use?
   Atlas.new employs SOC 2-compliant encryption, role-based access controls (RBAC), and private cloud deployment options to secure sensitive location data.

5. Can I use Atlas.new for climate risk assessment?
   Yes, templates like "Climate Risk & Sustainability" include AI models for flood, wildfire, or sea-level rise analysis using global climate datasets (e.g., NOAA, Copernicus).