The City of London Public Safety Department was struggling with an outdated public safety travel app that lacked advanced features necessary for modern urban challenges. The app failed to provide real-time insights and predictive analytics crucial for public safety management in a rapidly evolving city.
Hours delivered back to the business
SOX compliance in Settlement process automation
Success rate of bot case completion
For functional release of OBT, RTS and OGS
The Challenges
The existing app had several limitations:
Outdated Technology: Inability to integrate with modern data sources and lack of advanced analytical capabilities.
Slow Response Time: Ineffective at providing real-time updates and alerts, which hampered quick responses to safety incidents.
Limited Predictive Insights: No capability to analyze historical data and predict potential safety risks, leading to reactive rather than proactive measures.
What did Nawlabs do
Nawlabs was brought in to transform the public safety travel app by integrating AI technologies. The solution involved the following phases:
1. IT Consulting & Advisory:
Needs Assessment: Evaluated the existing system and identified gaps in AI capabilities and real-time data processing.
Strategic Planning: Developed a comprehensive AI integration strategy to enhance the app’s functionality with features like predictive analytics, machine learning algorithms, and real-time data processing.
Roadmap Development: Created a detailed implementation plan, including resource allocation and project milestones.
2. Web and Mobile Development
AI Integration: Integrated machine learning algorithms to provide predictive safety alerts and real-time risk assessments. The AI system analyzed historical and current data to forecast potential safety incidents and generate actionable insights.
App Redesign: Overhauled the app’s user interface to accommodate new AI features and improve overall usability. Added functionalities for real-time notifications, predictive alerts, and interactive safety maps.
Deployment and Testing: Developed and rigorously tested the AI-enhanced app across web and mobile platforms to ensure seamless performance and reliability
The Results
- Predictive Alerts: The AI algorithms enabled proactive safety measures by predicting potential risks and providing early warnings, leading to faster response times.
- Real-Time Updates: Enhanced real-time data processing ensured that users received timely notifications about safety issues, improving overall safety awareness.
- Enhanced User Experience: The redesigned app offered an intuitive interface with personalized safety alerts and interactive features, leading to increased user engagement and satisfaction.
