Case Study
BNGAI
BNGAI is an AI-powered biodiversity net gain (BNG) assessment platform designed to automate ecological evaluations, planning, and reporting processes. The platform combines artificial intelligence, geospatial data analysis, and intelligent workflow automation to deliver fast, accurate, and compliant biodiversity assessments for environmental projects. By integrating AI-driven habitat classification, automated compliance calculations, and real-time reporting tools, BNGAI simplifies complex ecological workflows. Built on scalable cloud infrastructure with both web and mobile accessibility, the platform empowers environmental consultants, planners, and developers to make data-driven decisions with confidence and precision.
Challenges
Advanced AI-Based Habitat Classification
Developing a machine learning system capable of accurately classifying habitats using geospatial and environmental datasets while ensuring reliability and compliance with biodiversity regulations.
Complex Regulatory Compliance Calculations
Implementing automated biodiversity net gain calculations aligned with ecological policies and government frameworks, while ensuring accuracy and transparency in reporting.
Large-Scale Geospatial Data Processing
Managing high-resolution satellite imagery, GIS datasets, and environmental data layers without affecting system performance or response time.
Multi-Platform Accessibility
Ensuring seamless performance across web and mobile applications, maintaining synchronized data and real-time updates for field assessments and reporting.
Secure Data Management & Reporting
Protecting sensitive environmental data with secure authentication, role-based access, and encrypted data storage while enabling automated report generation.
Solutions
We developed a high-scale architecture combining AI models, geospatial visualization, and rigorous automated calculation engines.
AI-Powered Habitat Intelligence Engine
Developed a machine learning-based classification system integrated with geospatial APIs to automatically detect, categorize, and assess biodiversity habitats with high precision.
Automated Compliance & Reporting Framework
Built a rules-based compliance engine that performs biodiversity net gain calculations automatically and generates structured reports aligned with regulatory standards.
Scalable Cloud Infrastructure (AWS)
Deployed the platform on AWS with optimized storage solutions and scalable compute resources to efficiently process geospatial datasets and AI workloads.
Cross-Platform Web & Mobile Development
Developed responsive web and mobile applications using modern frameworks to ensure real-time synchronization, intuitive dashboards, and field-friendly usability.
Secure & Role-Based Access Control
Implemented secure authentication systems with encrypted data handling and granular permission management for administrators, consultants, and project stakeholders.
How We Implemented the Solution
AI Model Training & Habitat Data Pipeline
We developed a structured data ingestion pipeline to process satellite imagery, GIS layers, and environmental datasets before feeding them into machine learning models. This ensured accurate habitat recognition and minimized classification errors.
Geospatial Processing & Layer Optimization
To manage high-resolution spatial datasets, we implemented optimized geospatial indexing, tiled rendering, and asynchronous processing. This maintained fast system performance even with large environmental data volumes.
Rule-Driven Compliance Calculation Engine
We engineered a configurable rules engine capable of translating biodiversity regulations into automated calculation logic. This ensured transparency, repeatability, and audit-ready reporting.
Scalable AI & Cloud Workload Management
AI workloads and geospatial computations were distributed across scalable cloud resources. This allowed dynamic resource allocation during intensive processing tasks.
Field-to-Cloud Data Synchronization
We created a unified API layer to synchronize field-collected data from mobile devices directly to the central system, ensuring real-time updates and consistent reporting.
Secure Environmental Data Governance
A layered security framework was implemented with encrypted storage, structured access permissions, and audit tracking to protect sensitive ecological project data.
Engineering Matrix
The technical foundation and measurable performance outcomes of the system.
AI-Driven Habitat Classification
Automatic and accurate environmental categorization.
Accelerated biodiversity assessments through AI automation
Automated BNG Calculations
Real-time compliance validation computing net gain.
Reduced manual errors in compliance calculations
Geospatial Mapping & Visualization
High-resolution satellite overlays mapped precisely.
Improved decision-making with geospatial insights
Ecological Workflow Automation
End-to-end processing minimizing standard consultant tasks.
Enabled scalable environmental project management
Real-Time Dashboard & Analytics
Project management overviews with key KPI tracking.
Delivered secure, future-ready ecological compliance infrastructure
Custom Report Generation
Pre-structured reports strictly following regulatory standards.
Admin Panel & Project Management
Holistic oversight interface for multiple consulting actions.
Secure Role-Based Access Control
Protecting critical findings and client project isolation.
Web & Mobile Application Access
Continuous connectivity from field evaluations to desk reporting.
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